From 21230bf91e288eba209207c747c7a6264a112588 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Thu, 2 Aug 2018 17:18:52 -0700
Subject: [PATCH 01/78] Add visualise as alias

---
 NAMESPACE                       | 1 +
 R/visualize.R                   | 4 ++++
 man/visualize.Rd                | 6 ++++++
 tests/testthat/test-visualize.R | 4 ++++
 4 files changed, 15 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index 11f3fcff..a6bf6e27 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -16,6 +16,7 @@ export(rep_sample_n)
 export(specify)
 export(t_stat)
 export(t_test)
+export(visualise)
 export(visualize)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,group_by)
diff --git a/R/visualize.R b/R/visualize.R
index 68d57da6..76e200e0 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -675,3 +675,7 @@ visualize_both <- function(data, bins,
 get_percentile <- function(vector, observation) {
   stats::ecdf(vector)(observation)
 }
+
+#' @rdname visualize
+#' @export
+visualise <- visualize
\ No newline at end of file
diff --git a/man/visualize.Rd b/man/visualize.Rd
index e7c2a8c4..43cbc7ce 100755
--- a/man/visualize.Rd
+++ b/man/visualize.Rd
@@ -2,12 +2,18 @@
 % Please edit documentation in R/visualize.R
 \name{visualize}
 \alias{visualize}
+\alias{visualise}
 \title{Visualize statistical inference}
 \usage{
 visualize(data, bins = 15, method = "simulation",
   dens_color = "black", obs_stat = NULL, obs_stat_color = "red2",
   pvalue_fill = "pink", direction = NULL, endpoints = NULL,
   endpoints_color = "mediumaquamarine", ci_fill = "turquoise", ...)
+
+visualise(data, bins = 15, method = "simulation",
+  dens_color = "black", obs_stat = NULL, obs_stat_color = "red2",
+  pvalue_fill = "pink", direction = NULL, endpoints = NULL,
+  endpoints_color = "mediumaquamarine", ci_fill = "turquoise", ...)
 }
 \arguments{
 \item{data}{The output from \code{\link[=calculate]{calculate()}}.}
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 0a3e3a46..78d5272e 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -51,6 +51,10 @@ obs_F <- anova(
 
 test_that("visualize basic tests", {
   expect_silent(visualize(Sepal.Width_resamp))
+  
+  # visualise also works
+  expect_silent(visualise(Sepal.Width_resamp))
+  
   expect_error(
     Sepal.Width_resamp %>% visualize(bins = "yep")
   )

From f1ad7cbba91d8a3ff376886f6dcd1e1da30c1d40 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 11:31:50 +0300
Subject: [PATCH 02/78] Remove trailing spaces in code.

Used {styler} (1.0.2):
`styler::style_pkg(scope = "none", strict = FALSE)`.
---
 R/calculate.R                      |  90 +++----
 R/conf_int.R                       |  16 +-
 R/generate.R                       |  26 +-
 R/hypothesize.R                    |  25 +-
 R/infer.R                          |   2 +-
 R/p_value.R                        |  30 +--
 R/print_methods.R                  |   4 +-
 R/rep_sample_n.R                   |   1 -
 R/set_params.R                     |  58 ++--
 R/specify.R                        |  10 +-
 R/utils.R                          |  62 ++---
 R/visualize.R                      | 326 +++++++++++-----------
 R/wrappers.R                       |  36 +--
 tests/testthat/test-calculate.R    |  76 +++---
 tests/testthat/test-conf_int.R     |  17 +-
 tests/testthat/test-generate.R     |  96 +++----
 tests/testthat/test-hypothesize.R  |  32 +--
 tests/testthat/test-p_value.R      |  48 ++--
 tests/testthat/test-rep_sample_n.R |  13 +-
 tests/testthat/test-utils.R        |   4 +-
 tests/testthat/test-visualize.R    | 420 ++++++++++++++---------------
 tests/testthat/test-wrappers.R     |  62 ++---
 22 files changed, 724 insertions(+), 730 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 9eeccd81..56147753 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -51,12 +51,12 @@ calculate <- function(x,
   check_for_factor_stat(x, stat, explanatory_variable(x))
   check_args_and_attr(x, explanatory_variable(x), response_variable(x), stat)
   check_point_params(x, stat)
-  
+
   if (!has_response(x))
     stop_glue(
       "The response variable is not set. Make sure to `specify()` it first."
     )
-  
+
   if (is.null(attr(x, "generate")) || !attr(x, "generate")) {
     if (is.null(attr(x, "null"))) {
       x$replicate <- 1L
@@ -77,7 +77,7 @@ calculate <- function(x,
         "implemented) for `stat` = \"{stat}\". Are you missing ",
         "a `generate()` step?"
       )
-    
+
     else if (!(stat %in% c("Chisq", "prop"))){
       # From `hypothesize()` to `calculate()`
       # Catch-all if generate was not called
@@ -87,13 +87,13 @@ calculate <- function(x,
       return(x)
     }
   }
-  
+
   if (stat %in% c("diff in means", "diff in medians", "diff in props")  ||
       (!is.null(attr(x, "theory_type")) &&
        attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))) {
     check_order(x, explanatory_variable(x), order)
   }
-  
+
   if (!(
     stat %in% c("diff in means", "diff in medians", "diff in props") ||
     (
@@ -108,11 +108,11 @@ calculate <- function(x,
       )
     }
   }
-  
+
   # Use S3 method to match correct calculation
-  result <- calc_impl(structure(stat, class = gsub(" ", "_", stat)), 
+  result <- calc_impl(structure(stat, class = gsub(" ", "_", stat)),
                       x, order, ...)
-  
+
   if ("NULL" %in% class(result))
     stop_glue(
       "Your choice of `stat` is invalid for the ",
@@ -120,14 +120,14 @@ calculate <- function(x,
     )
 #  else
 #    class(result) <- append("infer", class(result))
-  
+
   result <- set_attributes(to = result, from = x)
   attr(result, "stat") <- stat
-  
+
   # For returning a 1x1 observed statistic value
   if (nrow(result) == 1)
     result <- select(result, stat)
-  
+
   return(result)
 }
 
@@ -138,16 +138,16 @@ calc_impl <-
 
 calc_impl.mean <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
-  
+
   x %>%
     dplyr::group_by(replicate) %>%
     dplyr::summarize(stat = mean(!!(sym(col)), ...))
-  
+
 }
 
 calc_impl.median <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
-  
+
   x %>%
     dplyr::group_by(replicate) %>%
     dplyr::summarize(stat = stats::median(!!(sym(col)), ...))
@@ -155,7 +155,7 @@ calc_impl.median <- function(stat, x, order, ...) {
 
 calc_impl.sd <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
-  
+
   x %>%
     dplyr::group_by(replicate) %>%
     dplyr::summarize(stat = stats::sd(!!(sym(col)), ...))
@@ -163,7 +163,7 @@ calc_impl.sd <- function(stat, x, order, ...) {
 
 calc_impl.prop <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
-  
+
   ## No longer needed with implementation of `check_point_params()`
   # if(!is.factor(x[[col]])){
   #   stop_glue(
@@ -171,13 +171,13 @@ calc_impl.prop <- function(stat, x, order, ...) {
   #     "variable is not a factor."
   #   )
   # }
-  
+
   if (is.null(attr(x, "success")))
     stop_glue(
       'To calculate a proportion, the `"success"` argument ',
       'must be provided in `specify()`.'
     )
-  
+
   success <- attr(x, "success")
   x %>%
     dplyr::group_by(replicate) %>%
@@ -208,8 +208,8 @@ calc_impl.slope <- function(stat, x, order, ...) {
 }
 
 calc_impl.correlation <- function(stat, x, order, ...) {
-  x %>% 
-    dplyr::summarize(stat = stats::cor(!!attr(x, "explanatory"), 
+  x %>%
+    dplyr::summarize(stat = stats::cor(!!attr(x, "explanatory"),
                                 !!attr(x, "response")))
 }
 
@@ -234,7 +234,7 @@ calc_impl.diff_in_medians <- function(stat, x, order, ...) {
 
 calc_impl.Chisq <- function(stat, x, order, ...) {
   ## The following could stand to be cleaned up
-  
+
   if (is.null(attr(x, "explanatory"))) {
     # Chi-Square Goodness of Fit
     if (!is.null(attr(x, "params"))) {
@@ -243,7 +243,7 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
        dplyr::summarize(stat = stats::chisq.test(table(!!(
          attr(x, "response")
        )), p = attr(x, "params"))$stat)
-      
+
     } else {
       # Straight from `specify()`
         stop_glue("In order to calculate a Chi-Square Goodness of Fit ",
@@ -252,7 +252,7 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
                   "using `calculate()`")
 
     }
-    
+
   } else {
     # This is not matching with chisq.test
     # obs_tab <- x %>%
@@ -266,22 +266,22 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     #   dplyr::summarize(stat = sum((table(!!(attr(x, "response")),
     #                                      !!(attr(x, "explanatory")))
     #                                - expected)^2 / expected, ...))
-    
+
     # Chi-Square Test of Independence
-    
+
     result <- x %>%
       dplyr::do(broom::tidy(suppressWarnings(stats::chisq.test(table(
         .[[as.character(attr(x, "response"))]],
         .[[as.character(attr(x, "explanatory"))]]
       ))))) %>%
       dplyr::ungroup()
-    
+
     if (!is.null(attr(x, "generate")))
        result <-
         result %>% dplyr::select(replicate, stat = statistic)
     else
       result <- result %>% dplyr::select(stat = statistic)
-    
+
     attr(result, "response") <- attr(x, "response")
     attr(result, "success") <- attr(x, "success")
     attr(result, "explanatory") <- attr(x, "explanatory")
@@ -290,16 +290,16 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     attr(result, "distr_param") <- attr(x, "distr_param")
     attr(result, "distr_param2") <- attr(x, "distr_param2")
     attr(result, "theory_type") <- attr(x, "theory_type")
-    
+
     result
-    
+
   }
 }
 
 calc_impl.diff_in_props <- function(stat, x, order, ...) {
   col <- attr(x, "response")
   success <- attr(x, "success")
-  
+
   x %>%
     dplyr::group_by(replicate,!!attr(x, "explanatory")) %>%
     dplyr::summarize(prop = mean(!!sym(col) == success, ...)) %>%
@@ -309,17 +309,17 @@ calc_impl.diff_in_props <- function(stat, x, order, ...) {
 
 calc_impl.t <- function(stat, x, order, ...) {
   # Two sample means
-  
+
   if (attr(x, "theory_type") == "Two sample t") {
     # Re-order levels
     x <- reorder_explanatory(x, order)
-    
+
     df_out <- x %>%
       dplyr::summarize(stat = stats::t.test(
         !!attr(x, "response") ~ !!attr(x, "explanatory"), ...
       )[["statistic"]])
   }
-  
+
   # Standardized slope and standardized correlation are commented out
   # since there currently is no way to specify which one and
   # the standardization formulas are different.
@@ -344,7 +344,7 @@ calc_impl.t <- function(stat, x, order, ...) {
   #                      ) %>% 
   #     dplyr::mutate(stat = corr * (sqrt(nrow(x) - 2)) / sqrt(1 - corr ^ 2))
   # }
-  
+
   # One sample mean
   else if (attr(x, "theory_type") == "One sample t") {
     # For bootstrap
@@ -359,7 +359,7 @@ calc_impl.t <- function(stat, x, order, ...) {
     else {
       x %>%
         dplyr::summarize(stat = stats::t.test(
-          !!attr(x, "response"), 
+          !!attr(x, "response"),
           mu = attr(x, "params"),
           ...)[["statistic"]])
     }
@@ -371,10 +371,10 @@ calc_impl.z <- function(stat, x, order, ...) {
   if (attr(x, "theory_type") == "Two sample props z") {
     col <- attr(x, "response")
     success <- attr(x, "success")
-    
+
     x$explan <- factor(explanatory_variable(x),
                        levels = c(order[1], order[2]))
-    
+
     aggregated <- x %>%
       dplyr::group_by(replicate, explan) %>%
       dplyr::summarize(
@@ -384,7 +384,7 @@ calc_impl.z <- function(stat, x, order, ...) {
         num_suc = sum(rlang::eval_tidy(col) ==
                         rlang::eval_tidy(success))
       )
-    
+
     df_out <- aggregated %>%
       dplyr::summarize(
         diff_prop = prop[explan == order[1]]
@@ -398,30 +398,30 @@ calc_impl.z <- function(stat, x, order, ...) {
         stat = diff_prop / denom
       ) %>%
       dplyr::select(-total_suc,-n1,-n2)
-    
+
     df_out
-    
+
   } else
     # One sample proportion
     if (attr(x, "theory_type") == "One sample prop z") {
       # When `hypothesize()` has been called
       success <- attr(x, "success")
-      
+
       p0 <- attr(x, "params")[1]
       num_rows <- nrow(x) / length(unique(x$replicate))
-      
+
       col <- attr(x, "response")
       #    if(is.null(success))
       #      success <- quo(get_par_levels(x)[1])
       # Error given instead
-      
+
       df_out <- x %>%
         dplyr::summarize(stat = (mean(
           rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
         ) - p0) / sqrt((p0 * (1 - p0)) / num_rows))
-      
+
       df_out
-      
+
       # Straight from `specify()` doesn't make sense
       # since standardizing requires a hypothesized value
     }
diff --git a/R/conf_int.R b/R/conf_int.R
index 6c1a47e0..81bfffd2 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -35,15 +35,15 @@ NULL
 
 #' @rdname get_ci
 #' @export
-conf_int <- function(x, level = 0.95, type = "percentile", 
+conf_int <- function(x, level = 0.95, type = "percentile",
                      point_estimate = NULL){
-  
+
   check_ci_args(x, level, type, point_estimate)
-  
+
   if(type == "percentile") {
-    ci_vec <- stats::quantile(x[["stat"]], 
+    ci_vec <- stats::quantile(x[["stat"]],
                       probs = c((1 - level) / 2, level + (1 - level) / 2))
-  
+
     ci <- tibble::tibble(ci_vec[1], ci_vec[2])
     names(ci) <- names(ci_vec)
   } else {
@@ -53,12 +53,12 @@ conf_int <- function(x, level = 0.95, type = "percentile",
       lower = point_estimate - multiplier * stats::sd(x[["stat"]]),
       upper = point_estimate + multiplier * stats::sd(x[["stat"]]))
   }
-  
+
   return(ci)
 }
 
 check_ci_args <- function(x, level, type, point_estimate){
-  
+
   if(!is.null(point_estimate)){
     if(!is.data.frame(point_estimate))
       check_type(point_estimate, is.numeric)
@@ -70,7 +70,7 @@ check_ci_args <- function(x, level, type, point_estimate){
   if(level <= 0 || level >= 1){
     stop_glue("The value of `level` must be between 0 and 1 non-inclusive.")
   }
-  
+
   if(!(type %in% c("percentile", "se"))){
     stop_glue('The options for `type` are "percentile" or "se".')
   }
diff --git a/R/generate.R b/R/generate.R
index bb3b3087..029fa99f 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -24,12 +24,12 @@
 generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
   auto_type <- attr(x, "type")
-  
+
   if(!is.null(auto_type)){
     if (is.null(type)) {
       stop_glue("Supply not `NULL` value of `type`.")
     }
-    
+
     if(auto_type != type)
       stop_glue(
         "You have specified `type = \"{type}\"`, but `type` is expected to be ",
@@ -38,9 +38,9 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
     else
       type <- auto_type
   }
-  
+
   attr(x, "generate") <- TRUE
-  
+
   if (type == "permute" &&
       any(is.null(attr(x, "response")), is.null(attr(x, "explanatory")))) {
     stop_glue("Please `specify()` an explanatory and a response variable ",
@@ -80,12 +80,12 @@ bootstrap <- function(x, reps = 1, ...) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
     if(attr(attr(x, "params"), "names") == "mu"){
-      
+
       col <- as.character(attr(x, "response"))
 #      if(attr(x, "theory_type") != "One sample t"){
         x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
 #      }
-    
+
       # Standardize after centering above
       #####
       # Determining whether or not to implement this t transformation
@@ -119,9 +119,9 @@ bootstrap <- function(x, reps = 1, ...) {
   # Set variables for use in calculate()
   result <- rep_sample_n(x, size = nrow(x), replace = TRUE, reps = reps)
   result <- set_attributes(to = result, from = x)
-  
+
   class(result) <- append("infer", class(result))
-  
+
   return(result)
 }
 
@@ -132,11 +132,11 @@ permute <- function(x, reps = 1, ...) {
     dplyr::bind_rows() %>%
     dplyr::mutate(replicate = rep(1:reps, each = nrow(x))) %>%
     dplyr::group_by(replicate)
-  
+
   df_out <- set_attributes(to = df_out, from = x)
-  
+
   class(df_out) <- append("infer", class(df_out))
-  
+
   return(df_out)
 }
 
@@ -171,10 +171,8 @@ simulate <- function(x, reps = 1, ...) {
   )
 
   rep_tbl <- set_attributes(to = rep_tbl, from = x)
-  
+
   class(rep_tbl) <- append("infer", class(rep_tbl))
 
   return(dplyr::group_by(rep_tbl, replicate))
 }
-
-
diff --git a/R/hypothesize.R b/R/hypothesize.R
index b49e4909..41baf587 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -19,39 +19,39 @@
 #' 
 #' @export
 hypothesize <- function(x, null, ...) {
-  
+
   hypothesize_checks(x, null)
-  
+
   attr(x, "null") <- null
-  
+
   dots <- list(...)
-  
+
   if( (null == "point") && (length(dots) == 0) ){
     stop_glue("Provide a parameter and a value to check such as `mu = 30` ",
               "for the point hypothesis.")
   }
-  
+
   if((null == "independence") && (length(dots) > 0)) {
     warning_glue("Parameter values are not specified when testing that two ",
                  "variables are independent.")
   }
-  
+
   if((length(dots) > 0) && (null == "point")) {
     params <- parse_params(dots, x)
     attr(x, "params") <- params
-    
+
     if(any(grepl("p.", attr(attr(x, "params"), "names")))){
      # simulate instead of bootstrap based on the value of `p` provided
       attr(x, "type") <- "simulate"
     } else {
       attr(x, "type") <- "bootstrap"
     }
-    
+
   }
-  
+
   if(!is.null(null) && null == "independence")
     attr(x, "type") <- "permute"
-  
+
   # Check one proportion test set up correctly
   if(null == "point"){
     if(is.factor(response_variable(x))){
@@ -60,7 +60,7 @@ hypothesize <- function(x, null, ...) {
                   'to be used as a parameter.')
     }
   }
-  
+
   # Check one numeric test set up correctly
   ## Not currently able to reach in testing as other checks
   ## already produce errors
@@ -70,7 +70,6 @@ hypothesize <- function(x, null, ...) {
   #     stop_glue('Testing one numerical variable requires one of ',
   #               '`mu`, `med`, or `sd` to be used as a parameter.')
   # }
-  
+
   return(tibble::as_tibble(x))
 }
-
diff --git a/R/infer.R b/R/infer.R
index 4620ef2a..af1163fa 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -14,7 +14,7 @@ NULL
 
 ## quiets concerns of R CMD check re: the .'s that appear in pipelines
 ## From Jenny Bryan's googlesheets package
-if(getRversion() >= "2.15.1")  
+if(getRversion() >= "2.15.1")
   utils::globalVariables(c("prop", "stat", "xbar", "xtilde", "x", "..density..",
                            "statistic", ".", "parameter", "p.value",
                            "xmin", "xmax", "density", "denom",
diff --git a/R/p_value.R b/R/p_value.R
index 7d0ee23d..785471a5 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -32,24 +32,24 @@ NULL
 #' @rdname get_pvalue
 #' @export
 p_value <- function(x, obs_stat, direction){
-  
+
   check_type(x, is.data.frame)
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
-  
+
   is_simulation_based <- !is.null(attr(x, "generate")) &&
     attr(x, "generate")
 
   if(is_simulation_based)
-    pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat, 
+    pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
                                         direction = direction)
-  
+
   ## Theoretical-based p-value
   # Could be more specific
   # else if(is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param")))
   #   stop_glue("Attributes have not been set appropriately. ",
   #             "Check your {{infer}} pipeline again.")
-  
+
   # if(!("stat" %in% names(x))){
   #    # Theoretical distribution
   #  which_distribution(x, 
@@ -57,39 +57,39 @@ p_value <- function(x, obs_stat, direction){
   #                     obs_stat = obs_stat,
   #                     direction = direction) 
   # }
-  
+
   return(pvalue)
 }
 
 simulation_based_p_value <- function(x, obs_stat, direction){
-  
+
   if(direction %in% c("less", "left")){
-   p_value <- x %>% 
+   p_value <- x %>%
       dplyr::summarize(p_value = mean(stat <= obs_stat))
   }
   else if(direction %in% c("greater", "right")){
-   p_value <- x %>% 
+   p_value <- x %>%
       dplyr::summarize(p_value = mean(stat >= obs_stat))
   }
   else{
     p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
   }
-  
+
   p_value
 }
 
 two_sided_p_value <- function(x, obs_stat){
-  
+
   if(stats::median(x$stat) >= obs_stat){
     basic_p_value <- get_percentile(x$stat, obs_stat) +
-      (1 - get_percentile(x$stat, stats::median(x$stat) + 
+      (1 - get_percentile(x$stat, stats::median(x$stat) +
                        stats::median(x$stat) - obs_stat))
   } else {
     basic_p_value <- 1 - get_percentile(x$stat, obs_stat) +
-      (get_percentile(x$stat, stats::median(x$stat) + 
+      (get_percentile(x$stat, stats::median(x$stat) +
                             stats::median(x$stat) - obs_stat))
   }
-  
+
   if(basic_p_value >= 1)
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
@@ -118,7 +118,7 @@ get_pvalue <- p_value
 # }
 
 #theory_t_pvalue <- 
-  
+
 # set_lower_tail <- function(direction){
 #   if(direction %in% c("greater", "right"))
 #     lower_tail <- FALSE
diff --git a/R/print_methods.R b/R/print_methods.R
index 752845fc..9433f5d2 100644
--- a/R/print_methods.R
+++ b/R/print_methods.R
@@ -21,9 +21,9 @@ print.infer <- function(x, ...) {
   if ("null" %in% attrs) {
     header[3] <- glue_null('Null Hypothesis: {attr(x, "null")}')
   }
-  
+
   cat(glue::glue_collapse(header[header != ""], sep = "\n"))
   cat("\n")
-  
+
   NextMethod()
 }
diff --git a/R/rep_sample_n.R b/R/rep_sample_n.R
index 6268f23e..20b80a1e 100644
--- a/R/rep_sample_n.R
+++ b/R/rep_sample_n.R
@@ -77,4 +77,3 @@ rep_sample_n <- function(tbl, size, replace = FALSE, reps = 1, prob = NULL) {
   names(rep_tbl)[-1] <- names(tbl)
   dplyr::group_by(rep_tbl, replicate)
 }
-
diff --git a/R/set_params.R b/R/set_params.R
index 94f23e52..13d9fc32 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -6,30 +6,30 @@
 set_params <- function(x){
 
   attr(x, "theory_type") <- NULL
-  
+
   if(!is.null(attr(x, "response"))){
     num_response_levels <- length(levels(response_variable(x)))
   }
-  
+
   # One variable
-  if (!is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) && 
-      !is.null(attr(x, "response_type")) && 
+  if (!is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
+      !is.null(attr(x, "response_type")) &&
       is.null(attr(x, "explanatory_type"))){
-    
+
     # One mean
     if(attr(x, "response_type") %in% c("integer", "numeric")){
       attr(x, "theory_type") <- "One sample t"
-      attr(x, "distr_param") <- x %>% 
+      attr(x, "distr_param") <- x %>%
         dplyr::summarize(df = stats::t.test(
             response_variable(x))[["parameter"]]
-          ) %>% 
+          ) %>%
         dplyr::pull()
       attr(x, "type") <- "bootstrap"
     }
-    
+
     # One prop
     else if(attr(x, "response_type") == "factor" && (num_response_levels == 2)){
-      
+
       # No parameters since standard normal
       attr(x, "theory_type") <- "One sample prop z"
       # Changed to `"simulate"` when `p` provided in `hypothesize()`
@@ -39,53 +39,53 @@ set_params <- function(x){
       attr(x, "distr_param") <- num_response_levels - 1
       attr(x, "type") <- "simulate"
     }
-    
+
   }
-  
+
   # Two variables
-  if (!is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) & 
-      !is.null(attr(x, "response_type")) && 
+  if (!is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
+      !is.null(attr(x, "response_type")) &&
       !is.null(attr(x, "explanatory_type"))){
-    
+
     attr(x, "type") <- "bootstrap"
-    
+
     # Response is numeric, explanatory is categorical
     if(attr(x, "response_type") %in% c("integer", "numeric") &
        attr(x, "explanatory_type") == "factor"){
-      
+
       # Two sample means (t distribution)
       if(length(levels(explanatory_variable(x))) == 2) {
         attr(x, "theory_type") <- "Two sample t"
         # Keep track of Satterthwaite degrees of freedom since lost when 
         # in aggregation w/ calculate()/generate()
-        attr(x, "distr_param") <- x %>% 
+        attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = stats::t.test(
               !!attr(x, "response") ~ !!attr(x, "explanatory"))[["parameter"]]
-            ) %>% 
+            ) %>%
           dplyr::pull()
       } else {
         # >2 sample means (F distribution)
         attr(x, "theory_type") <- "ANOVA"
         # Get numerator and denominator degrees of freedom
-        attr(x, "distr_param") <- x %>% 
+        attr(x, "distr_param") <- x %>%
           dplyr::summarize(df1 = stats::anova(stats::aov(
               !! attr(x, "response") ~ !! attr(x, "explanatory")))$Df[1]
-            ) %>% 
+            ) %>%
           dplyr::pull()
-        attr(x, "distr_param2") <- x %>% 
+        attr(x, "distr_param2") <- x %>%
           dplyr::summarize(df2 = stats::anova(stats::aov(
               !! attr(x, "response") ~ !! attr(x, "explanatory")))$Df[2]
-            ) %>% 
+            ) %>%
           dplyr::pull()
       }
     }
-    
+
     # Response is categorical, explanatory is categorical
     if(attr(x, "response_type") == "factor" &
        attr(x, "explanatory_type") == "factor"){
-      
+
       attr(x, "type") <- "bootstrap"
-      
+
       # Two sample proportions (z distribution) 
       # Parameter(s) not needed since standard normal
       if(length(levels(response_variable(x))) == 2 &
@@ -95,14 +95,14 @@ set_params <- function(x){
       # >2 sample proportions (chi-square test of indep)
       else{
         attr(x, "theory_type") <- "Chi-square test of indep"
-        attr(x, "distr_param") <- x %>% 
+        attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = suppressWarnings(stats::chisq.test(
             table(response_variable(x),
                   explanatory_variable(x)))$parameter)) %>%
           dplyr::pull()
       }
     }
-    
+
     # Response is numeric, explanatory is numeric
     if(attr(x, "response_type") %in% c("integer", "numeric") &
        attr(x, "explanatory_type") %in% c("integer", "numeric")){
@@ -112,9 +112,9 @@ set_params <- function(x){
     attr(x, "distr_param") <- nrow(x) - 2
     }
   }
-  
+
 #  if(is.null(attr(x, "theory_type")))
 #     warning_glue("Theoretical type not yet implemented")
-  
+
   x
 }
diff --git a/R/specify.R b/R/specify.R
index ad87cbb5..f3779839 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -55,11 +55,11 @@ specify <- function(x, formula, response = NULL,
     attr(x, "response")    <- f_lhs(formula)
     attr(x, "explanatory") <- f_rhs(formula)
   }
-  
+
   if (is.null(attr(x, "response"))) {
     stop_glue("Supply not `NULL` response variable.")
   }
-  
+
   if (!(as.character(attr(x, "response")) %in% names(x))) {
     stop_glue('The response variable `{attr(x, "response")}` ',
               'cannot be found in this dataframe.')
@@ -115,15 +115,15 @@ specify <- function(x, formula, response = NULL,
     warning_glue("Removed {sum(!is_complete)} rows containing missing values.")
   }
 
-  
+
   # To help determine theoretical distribution to plot
   attr(x, "response_type") <- class(response_variable(x))
-  
+
   if(is.null(attr(x, "explanatory")))
     attr(x, "explanatory_type") <- NULL
   else
     attr(x, "explanatory_type") <- class(explanatory_variable(x))
-  
+
   if(attr(x, "response_type") == "factor" && is.null(success) &&
      length(levels(response_variable(x))) == 2 &&
      (is.null(attr(x, "explanatory_type")) ||
diff --git a/R/utils.R b/R/utils.R
index 6c5c2a35..7a740919 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -22,7 +22,7 @@ set_attributes <- function(to, from = x){
   attr(to, "theory_type") <- attr(from, "theory_type")
   attr(to, "generate") <- attr(from, "generate")
   attr(to, "type") <- attr(from, "type")
-  
+
   return(to)
 }
 
@@ -85,7 +85,7 @@ null_transformer <- function(text, envir) {
   if (is.null(out)) {
     return("NULL")
   }
-  
+
   out
 }
 
@@ -114,9 +114,9 @@ check_order <- function(x, explanatory_variable, order){
   }
 }
 
-check_args_and_attr <- function(x, explanatory_variable, response_variable, 
+check_args_and_attr <- function(x, explanatory_variable, response_variable,
                                 stat){
-  
+
   # Could also do `stat <- match.arg(stat)`
   # but that's not as helpful to beginners with the cryptic error msg
   if (!stat %in% c("mean", "median", "sd", "prop",
@@ -125,11 +125,11 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
     stop_glue("You specified a string for `stat` that is not implemented. ",
               "Check your spelling and `?calculate` for current options.")
   }
-  
+
   if (!("replicate" %in% names(x)) && !is.null(attr(x, "generate")))
     warning_glue('A `generate()` step was not performed prior to ',
                  '`calculate()`. Review carefully.')
-  
+
   if (stat %in% c("F", "slope", "diff in means", "diff in medians")){
     if (has_explanatory(x) && !is.numeric(response_variable(x))){
       stop_glue(
@@ -138,7 +138,7 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
       )
     }
   }
-  
+
   if (stat %in% c("diff in props", "Chisq")){
     if (has_explanatory(x) && !is.factor(response_variable(x))){
       stop_glue(
@@ -152,7 +152,7 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
 check_for_numeric_stat <- function(x, stat){
   if (stat %in% c("mean", "median", "sd")){
     col <- base::setdiff(names(x), "replicate")
-    
+
     if (!is.numeric(x[[as.character(col)]])){
       stop_glue(
         "Calculating a {stat} here is not appropriate\n",
@@ -163,7 +163,7 @@ check_for_numeric_stat <- function(x, stat){
 }
 
 check_for_factor_stat <- function(x, stat, explanatory_variable){
-  
+
   if (stat %in% c("diff in means", "diff in medians", "diff in props", "F")){
     if (!is.factor(explanatory_variable)){
       stop_glue(
@@ -176,8 +176,8 @@ check_for_factor_stat <- function(x, stat, explanatory_variable){
 }
 
 check_point_params <- function(x, stat){
-  
-  param_names <- attr(attr(x, "params"), "names") 
+
+  param_names <- attr(attr(x, "params"), "names")
   hyp_text <- 'to be set in `hypothesize()`.'
   if(!is.null(attr(x, "null"))){
     if(stat %in% c("mean", "median", "sd", "prop")){
@@ -194,7 +194,7 @@ check_point_params <- function(x, stat){
       #   stop_glue('`stat == "sd"` requires `"sigma"` {hyp_text}')
       if ( (!(stat == "sd") && ("sigma" %in% param_names)) )
         stop_glue('`"sigma"` does not correspond to `stat = "{stat}"`.')
-      
+
       ## Tests unable to get to
       # if(stat == "prop" && !(any(grepl("p.", param_names))))
       #   stop_glue('`stat == "prop"` requires `"p"` {hyp_text}')
@@ -207,21 +207,21 @@ parse_params <- function(dots, x) {
   mu_ind <- grep("mu", names(dots))
   med_ind <- grep("med", names(dots))
   sig_ind <- grep("sigma", names(dots))
-  
+
   # error: cannot specify more than one of props, means, medians, or sds
-  if ( length(p_ind) + length(mu_ind) + length(med_ind) 
+  if ( length(p_ind) + length(mu_ind) + length(med_ind)
        + length(sig_ind) != 1 ){
     stop_glue(
       'Parameter values can be only one of `p`, `mu`, `med`, or `sigma`.'
     )
   }
-  
+
   # add in 1 - p if it's missing
   # Outside if() is needed to ensure an error does not occur in referencing the
   # 0 index of dots
   if (length(p_ind)) {
     if (length(dots[[p_ind]]) == 1) {
-      
+
       if (attr(x, "null") == "point" && is.null(attr(x, "success"))) {
         stop_glue("A point null regarding a proportion requires ",
                   "that `success` be indicated in `specify()`.")
@@ -230,7 +230,7 @@ parse_params <- function(dots, x) {
         stop_glue(
           "The value suggested for `p` is not between 0 and 1, inclusive."
         )
-      missing_lev <- base::setdiff(unique(pull(x, !!attr(x, "response"))), 
+      missing_lev <- base::setdiff(unique(pull(x, !!attr(x, "response"))),
                              attr(x, "success"))
       dots$p <- append(dots$p, 1 - dots$p)
       names(dots$p) <- c(attr(x, "success"), missing_lev)
@@ -241,12 +241,12 @@ parse_params <- function(dots, x) {
       }
     }
   }
-  
+
   # if (sum(dots[[p_ind]]) != 1){
   #   dots[[p_ind]] <- dots[[p_ind]]/sum(dots[[p_ind]])
   #   warning_glue("Proportions do not sum to 1, normalizing automatically.")
   # }
-  
+
   return(unlist(dots))
 }
 
@@ -255,25 +255,25 @@ hypothesize_checks <- function(x, null){
   if (!sum(class(x) %in% c("data.frame", "tbl", "tbl_df", "grouped_df"))) {
     stop_glue("x must be a data.frame or tibble")
   }
-  
+
   # error: null not found
   if (!(null %in% c("independence", "point"))) {
     stop_glue(
       "Choice of null is not supported. Check `?hypothesize` for options."
     )
   }
-  
+
   #  if (length(null) != 1) {
   #    stop_glue('Choose between either `"independence"` or `"point"` ',
   #              'for `null` argument.')
   #  }
-  
+
   if(!has_response(x)){
     stop_glue(
       "The response variable is not set. Make sure to `specify()` it first."
     )
   }
-  
+
   if(null == "independence" && !has_explanatory(x)){
     stop_glue('Please `specify()` an explanatory and a response variable ',
               'when testing\n',
@@ -284,7 +284,7 @@ hypothesize_checks <- function(x, null){
 check_direction <- function(direction = c("less", "greater", "two_sided",
                                           "left", "right", "both")){
   check_type(direction, is.character)
-  
+
   if(!(direction %in% c("less", "greater", "two_sided",
                         "left", "right", "both"))){
     stop_glue('The provided value for `direction` is not appropriate. ',
@@ -297,10 +297,10 @@ check_obs_stat <- function(obs_stat){
   if(!is.null(obs_stat)){
     if("data.frame" %in% class(obs_stat)){
       check_type(obs_stat, is.data.frame)
-      if( (nrow(obs_stat) != 1) || (ncol(obs_stat) != 1) ) 
-        warning_glue("The first row and first column value of the given ", 
+      if( (nrow(obs_stat) != 1) || (ncol(obs_stat) != 1) )
+        warning_glue("The first row and first column value of the given ",
                      "`obs_stat` will be used.")
-      
+
       # [[1]] is used in case `stat` is not specified as name of 1x1
       obs_stat <- obs_stat[[1]][[1]]
       check_type(obs_stat, is.numeric)
@@ -309,7 +309,7 @@ check_obs_stat <- function(obs_stat){
       check_type(obs_stat, is.numeric)
     }
   }
-  
+
   obs_stat
 }
 
@@ -341,12 +341,12 @@ check_type <- function(x, predicate, type = NULL) {
     predicate_name <- deparse(rlang::enexpr(predicate))
     type <- parse_type(predicate_name)
   }
-  
+
   if (!isTRUE(predicate(x))) {
     # Not using "must be of type" because of 'tibble' and 'string' cases
     stop_glue("`{x_name}` must be '{type}', not '{get_type(x)}'.")
   }
-  
+
   x
 }
 
@@ -355,7 +355,7 @@ get_type <- function(x) {
   if (is.data.frame(x)) {
     return("data.frame")
   }
-  
+
   typeof(x)
 }
 
diff --git a/R/visualize.R b/R/visualize.R
index 68d57da6..7129b050 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -69,16 +69,16 @@
 #' @importFrom ggplot2 xlab ylab geom_vline geom_rect geom_bar
 #' @importFrom stats dt qt df qf dnorm qnorm dchisq qchisq
 #' @export
-visualize <- function(data, bins = 15, method = "simulation", 
+visualize <- function(data, bins = 15, method = "simulation",
                       dens_color = "black",
-                      obs_stat = NULL, 
+                      obs_stat = NULL,
                       obs_stat_color = "red2",
                       pvalue_fill = "pink",
-                      direction = NULL, 
-                      endpoints = NULL, 
+                      direction = NULL,
+                      endpoints = NULL,
                       endpoints_color = "mediumaquamarine",
                       ci_fill = "turquoise", ...) {
-  
+
   check_type(data, is.data.frame)
   check_type(bins, is.numeric)
   check_type(method, is.character)
@@ -87,7 +87,7 @@ visualize <- function(data, bins = 15, method = "simulation",
   check_type(pvalue_fill, is.character)
   if(!is.null(direction))
     check_type(direction, is.character)
-  if(is.data.frame(endpoints) && 
+  if(is.data.frame(endpoints) &&
      ( (nrow(endpoints) != 1) || (ncol(endpoints) != 2) ) ){
     stop_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
@@ -103,7 +103,7 @@ visualize <- function(data, bins = 15, method = "simulation",
   if(is.data.frame(endpoints))
     endpoints <- unlist(endpoints)
   obs_stat <- check_obs_stat(obs_stat)
-  if(!is.null(direction) && 
+  if(!is.null(direction) &&
      (is.null(obs_stat) + is.null(endpoints)) != 1)
     stop_glue(
       "Shading requires either `endpoints` values for a confidence interval ",
@@ -111,50 +111,50 @@ visualize <- function(data, bins = 15, method = "simulation",
     )
 
   if(method == "simulation"){
-    
-    infer_plot <- visualize_simulation(data = data, bins = bins, 
+
+    infer_plot <- visualize_simulation(data = data, bins = bins,
                                        dens_color = dens_color,
-                                       obs_stat = obs_stat, 
+                                       obs_stat = obs_stat,
                                        obs_stat_color = obs_stat_color,
-                                       direction = direction, 
+                                       direction = direction,
                                        pvalue_fill = pvalue_fill,
-                                       endpoints = endpoints, 
+                                       endpoints = endpoints,
                                        ci_fill = ci_fill,
                                        ...)
-    
+
   } else if(method == "theoretical"){
-    
+
     infer_plot <- visualize_theoretical(data = data,
                                         dens_color = dens_color,
-                                        obs_stat = obs_stat, 
+                                        obs_stat = obs_stat,
                                         obs_stat_color = obs_stat_color,
-                                        direction = direction, 
+                                        direction = direction,
                                         pvalue_fill = pvalue_fill,
-                                        endpoints = endpoints, 
+                                        endpoints = endpoints,
                                         ci_fill = ci_fill,
                                         ...)
-    
-    
+
+
   } else if(method == "both"){
-    
+
     if(!("stat" %in% names(data)))
       stop_glue('`generate()` and `calculate()` are both required ',
                 'to be done prior to `visualize(method = "both")`')
-    
+
     if(("replicate" %in% names(data)) &&
          length(unique(data$replicate)) < 100)
       warning_glue(
         "With only {length(unique(data$stat))} replicates, it may be ",
         "difficult to see the relationship between simulation and theory."
       )
-    
-    infer_plot <- visualize_both(data = data, bins = bins, 
+
+    infer_plot <- visualize_both(data = data, bins = bins,
                                  dens_color = dens_color,
-                                 obs_stat = obs_stat, 
+                                 obs_stat = obs_stat,
                                  obs_stat_color = obs_stat_color,
                                  direction = direction,
                                  pvalue_fill = pvalue_fill,
-                                 endpoints = endpoints, 
+                                 endpoints = endpoints,
                                  ci_fill = ci_fill,
                                  ...)
   } else {
@@ -162,31 +162,31 @@ visualize <- function(data, bins = 15, method = "simulation",
               '`"theoretical"`, `"both"`, or `"simulation"`. ',
               '`"simulation"` is the default.')
   }
-  
+
   if(!is.null(obs_stat)){#&& !is.null(direction)
     infer_plot <- infer_plot +
       geom_vline(xintercept = obs_stat, size = 2, color = obs_stat_color, ...)
   }
-  
+
   if(!is.null(endpoints)){
     if(!is.null(obs_stat))
       warning_glue("Values for both `endpoints` and `obs_stat` were given ",
                    "when only one should be set. Ignoring `obs_stat` values.")
     infer_plot <- infer_plot +
-      geom_vline(xintercept = endpoints, size = 2, 
-                 color = endpoints_color, 
+      geom_vline(xintercept = endpoints, size = 2,
+                 color = endpoints_color,
                  ...)
   }
-  
+
   infer_plot
 }
 
 
-theory_t_plot <- function(deg_freedom, statistic_text = "t", 
+theory_t_plot <- function(deg_freedom, statistic_text = "t",
                           dens_color = dens_color, ...){
-  ggplot(data.frame(x = c(qt(0.001, deg_freedom), 
-                          qt(0.999, deg_freedom)))) + 
-    stat_function(mapping = aes(x), fun = dt, args = list(df = deg_freedom), 
+  ggplot(data.frame(x = c(qt(0.001, deg_freedom),
+                          qt(0.999, deg_freedom)))) +
+    stat_function(mapping = aes(x), fun = dt, args = list(df = deg_freedom),
                   color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
@@ -196,12 +196,12 @@ theory_t_plot <- function(deg_freedom, statistic_text = "t",
 both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
                         dens_color,
                         obs_stat,
-                        direction, 
+                        direction,
                         bins,
-                        pvalue_fill, 
-                        endpoints, 
+                        pvalue_fill,
+                        endpoints,
                         ci_fill, ...){
-  
+
   infer_t_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
                                       direction = direction,
@@ -211,7 +211,7 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
                                       ci_fill = ci_fill)
 
   infer_t_plot +
-    stat_function(fun = dt, args = list(df = deg_freedom), 
+    stat_function(fun = dt, args = list(df = deg_freedom),
                   color = dens_color) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
@@ -220,12 +220,12 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
     ylab("")
 }
 
-theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom, 
-                              statistic_text = "F", 
+theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom,
+                              statistic_text = "F",
                               dens_color = dens_color, ...){
-  ggplot(data.frame(x = c(qf(0.001, deg_freedom_top, deg_freedom_bottom), 
-                          qf(0.999, deg_freedom_top, deg_freedom_bottom)))) + 
-    stat_function(mapping = aes(x), fun = df, 
+  ggplot(data.frame(x = c(qf(0.001, deg_freedom_top, deg_freedom_bottom),
+                          qf(0.999, deg_freedom_top, deg_freedom_bottom)))) +
+    stat_function(mapping = aes(x), fun = df,
                   args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
                   color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
@@ -233,44 +233,44 @@ theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom,
     ylab("")
 }
 
-both_anova_plot <- function(data, deg_freedom_top, 
+both_anova_plot <- function(data, deg_freedom_top,
                             deg_freedom_bottom, statistic_text = "F",
                             dens_color,
                             obs_stat,
-                            direction, 
+                            direction,
                             bins,
                             endpoints,
                             pvalue_fill,
                             ci_fill,
                             ...){
-  
+
   if(!is.null(direction) && !(direction %in% c("greater", "right")))
     warning_glue(
       "F usually corresponds to right-tailed tests. Proceed with caution."
     )
-  
-  infer_anova_plot <- shade_density_check(data = data, 
+
+  infer_anova_plot <- shade_density_check(data = data,
                                           obs_stat = obs_stat,
                                           direction = direction,
                                           bins = bins,
                                           endpoints = endpoints,
                                           pvalue_fill = pvalue_fill,
                                           ci_fill = ci_fill)
-  
+
   infer_anova_plot <- infer_anova_plot +
-    stat_function(fun = df, 
+    stat_function(fun = df,
                   args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
                   color = dens_color) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
     )) +
     xlab("Fstat") +
-    ylab("")  
+    ylab("")
 }
 
 theory_z_plot <- function(statistic_text = "z", dens_color = dens_color,  ...){
-  
-  ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) + 
+
+  ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) +
     stat_function(mapping = aes(x), fun = dnorm, color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
@@ -286,15 +286,15 @@ both_z_plot <- function(data, statistic_text = "z",
                         endpoints,
                         ci_fill,
                         ...){
-  
-  infer_z_plot <- shade_density_check(data = data, 
+
+  infer_z_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
                                       direction = direction,
                                       bins = bins,
                                       endpoints = endpoints,
                                       pvalue_fill = pvalue_fill,
                                       ci_fill = ci_fill)
-  
+
   infer_z_plot +
     stat_function(fun = dnorm, color = dens_color) +
     ggtitle(glue_null(
@@ -304,13 +304,13 @@ both_z_plot <- function(data, statistic_text = "z",
     ylab("")
 }
 
-theory_chisq_plot <- function(deg_freedom, 
-                              statistic_text = "Chi-Square", 
+theory_chisq_plot <- function(deg_freedom,
+                              statistic_text = "Chi-Square",
                               dens_color = dens_color, ...){
-  ggplot(data.frame(x = c(qchisq(0.001, deg_freedom), 
-                          qchisq(0.999, deg_freedom)))) + 
-    stat_function(mapping = aes(x), fun = dchisq, 
-                  args = list(df = deg_freedom), 
+  ggplot(data.frame(x = c(qchisq(0.001, deg_freedom),
+                          qchisq(0.999, deg_freedom)))) +
+    stat_function(mapping = aes(x), fun = dchisq,
+                  args = list(df = deg_freedom),
                   color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
@@ -320,17 +320,17 @@ theory_chisq_plot <- function(deg_freedom,
 both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                             dens_color,
                             obs_stat,
-                            direction, 
-                            bins, 
-                            endpoints, 
+                            direction,
+                            bins,
+                            endpoints,
                             pvalue_fill = pvalue_fill,
                             ci_fill = ci_fill,
                             ...){
-  
+
   if(!is.null(direction) && !(direction %in% c("greater", "right")))
     warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                  "Proceed with caution.")
-  
+
   infer_chisq_plot <- shade_density_check(data = data,
                                           obs_stat = obs_stat,
                                           direction = direction,
@@ -338,9 +338,9 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                                           endpoints = endpoints,
                                           pvalue_fill = pvalue_fill,
                                           ci_fill = ci_fill)
-  
+
   infer_chisq_plot +
-    stat_function(fun = dchisq, args = list(df = deg_freedom), 
+    stat_function(fun = dchisq, args = list(df = deg_freedom),
                   color = dens_color) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
@@ -351,14 +351,14 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
 
 
 shade_density_check <- function(data,
-                                obs_stat, 
-                                direction, 
-                                bins, 
-                                density = TRUE, 
+                                obs_stat,
+                                direction,
+                                bins,
+                                density = TRUE,
                                 pvalue_fill,
                                 endpoints,
                                 ci_fill, ...) {
-  
+
   if(is.null(direction) || is.null(obs_stat)){
     if(density){
       gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
@@ -370,7 +370,7 @@ shade_density_check <- function(data,
       #  geom_histogram(bins = bins, color = "white", ...)
     #}
   }
-  
+
   if(xor(!is.null(obs_stat), !is.null(endpoints))){
     if(!is.null(direction)){
       if(density){
@@ -381,72 +381,72 @@ shade_density_check <- function(data,
         gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
           geom_histogram(bins = bins, color = "white", ...)
       }
-      
+
       if(direction %in% c("less", "left")){
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01, 
-                    aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf), 
+          geom_rect(fill = pvalue_fill, alpha = 0.01,
+                    aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
                     ...)
       }
       if(direction %in% c("greater", "right")){
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01, 
-                    aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf), 
+          geom_rect(fill = pvalue_fill, alpha = 0.01,
+                    aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
                     ...)
       }
-      
-      if(direction %in% c("two_sided", "both") && 
+
+      if(direction %in% c("two_sided", "both") &&
          obs_stat >= stats::median(data$stat)){
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0, 
+                    mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0,
                                   ymax = Inf), ...) +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     mapping = aes(
-                      xmin = -Inf, 
+                      xmin = -Inf,
                       xmax = stats::quantile(
-                        data$stat, 
+                        data$stat,
                         probs = 1 - get_percentile(data$stat, obs_stat)
                       ),
                       ymin = 0, ymax = Inf, ...)
-          ) 
+          )
       }
-      
-      if(direction %in% c("two_sided", "both") && 
+
+      if(direction %in% c("two_sided", "both") &&
          obs_stat < stats::median(data$stat)){
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0, 
+                    mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0,
                                   ymax = Inf), ...) +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes( 
+                    mapping = aes(
                       xmin = stats::quantile(
-                        data$stat, 
+                        data$stat,
                         probs = 1 - get_percentile(data$stat, obs_stat)
                       ), xmax = Inf, ymin = 0, ymax = Inf, ...)
-          ) 
+          )
       }
     }
 
-  
+
     if(direction == "between"){
       gg_plot <- gg_plot +
-        geom_rect(fill = ci_fill, alpha = 0.01, 
-                  aes(xmin = endpoints[1], 
-                      xmax = endpoints[2], ymin = 0, ymax = Inf), 
+        geom_rect(fill = ci_fill, alpha = 0.01,
+                  aes(xmin = endpoints[1],
+                      xmax = endpoints[2], ymin = 0, ymax = Inf),
                   ...)
     }
-  
+
   }
     gg_plot
 }
 
-visualize_simulation <- function(data, bins, 
-                                 method = "simulation", 
+visualize_simulation <- function(data, bins,
+                                 method = "simulation",
                                  dens_color,
-                                 obs_stat, 
+                                 obs_stat,
                                  obs_stat_color,
-                                 direction, 
+                                 direction,
                                  pvalue_fill,
                                  endpoints,
                                  ci_fill, ...) {
@@ -465,7 +465,7 @@ visualize_simulation <- function(data, bins,
                                       bins = bins,
                                       density = FALSE,
                                       pvalue_fill = pvalue_fill,
-                                      endpoints = endpoints, 
+                                      endpoints = endpoints,
                                       ci_fill = ci_fill
     )
   }
@@ -474,99 +474,99 @@ visualize_simulation <- function(data, bins,
 
 visualize_theoretical <- function(data,
                                   dens_color,
-                                  obs_stat, 
+                                  obs_stat,
                                   obs_stat_color,
-                                  direction, 
-                                  pvalue_fill, 
+                                  direction,
+                                  pvalue_fill,
                                   endpoints,
-                                  ci_fill, 
+                                  ci_fill,
                                   ...) {
-  
+
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. {{infer}} currently does not check these for you."
   )
-  
-  if(!is.null(attr(data, "stat")) && 
+
+  if(!is.null(attr(data, "stat")) &&
      !(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
     warning_glue(
       "Your `calculate`d statistic and the theoretical distribution are on ",
       "different scales. Displaying only the theoretical distribution."
     )
-  
-  if(attr(data, "theory_type") %in% 
-     c("Two sample t", "Slope with t", "One sample t")){    
+
+  if(attr(data, "theory_type") %in%
+     c("Two sample t", "Slope with t", "One sample t")){
     infer_plot <- theory_t_plot(deg_freedom = attr(data, "distr_param"),
                                 statistic_text = "t",
                                 dens_color = dens_color)
   }
-  
+
   else if(attr(data, "theory_type") == "ANOVA"){
-    
+
     if(!is.null(direction) && !(direction %in% c("greater", "right")))
       warning_glue(
         "F usually corresponds to right-tailed tests. Proceed with caution."
       )
-    
+
     infer_plot <- theory_anova_plot(
-      deg_freedom_top = attr(data, "distr_param"), 
-      deg_freedom_bottom = attr(data, "distr_param2"), 
+      deg_freedom_top = attr(data, "distr_param"),
+      deg_freedom_bottom = attr(data, "distr_param2"),
       statistic_text = "F",
       dens_color = dens_color)
   }
-  
-  else if(attr(data, "theory_type") %in% 
+
+  else if(attr(data, "theory_type") %in%
           c("One sample prop z", "Two sample props z")){
-    infer_plot <- theory_z_plot(statistic_text = "z", 
+    infer_plot <- theory_z_plot(statistic_text = "z",
                                 dens_color = dens_color)
   }
-  
-  else if(attr(data, "theory_type") %in% 
-          c("Chi-square test of indep", "Chi-square Goodness of Fit")){   
-    
+
+  else if(attr(data, "theory_type") %in%
+          c("Chi-square test of indep", "Chi-square Goodness of Fit")){
+
     if(!is.null(direction) && !(direction %in% c("greater", "right")))
       warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                    "Proceed with caution.")
-    
+
     infer_plot <- theory_chisq_plot(deg_freedom = attr(data, "distr_param"),
                                     statistic_text = "Chi-Square",
                                     dens_color = dens_color)
   }
-  
+
 #  else
 #    stop_glue(
 #      '"{attr(data, "theory_type")}" is not implemented (possibly yet).'
 #    )
-  
+
   # Move into its own function
-  
+
   if(!is.null(obs_stat)){
     if(!is.null(direction)){
       if(direction %in% c("less", "left")){
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
                     ...)
       }
       if(direction %in% c("greater", "right")){
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = obs_stat,
                         xmax = Inf, ymin = 0, ymax = Inf),
                     ...)
       }
-      
+
       # Assuming two-tailed shading will only happen with theoretical 
       # distributions centered at 0
       if(direction %in% c("two_sided", "both") && obs_stat >= 0){
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
                     ...) +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = -Inf, xmax = -obs_stat, ymin = 0, ymax = Inf),
                     ...)
@@ -574,44 +574,44 @@ visualize_theoretical <- function(data,
 
       if(direction %in% c("two_sided", "both") && obs_stat < 0){
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
                     ...) +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill, 
+          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = -obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
                     ...)
       }
     }
   }
-  
+
   # To implement: plotting of theoretical confidence interval values
-  
+
   infer_plot
 }
 
-visualize_both <- function(data, bins, 
+visualize_both <- function(data, bins,
                            dens_color,
-                           obs_stat, 
+                           obs_stat,
                            obs_stat_color,
-                           direction, 
-                           pvalue_fill, 
-                           endpoints, 
+                           direction,
+                           pvalue_fill,
+                           endpoints,
                            ci_fill, ...) {
-  
+
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. `infer` currently does not check these for you."
   )
-  
+
   if(!(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
     stop_glue("Your `calculate`d statistic and the theoretical distribution ",
               "are on different scales. Use a standardized `stat` instead.")
-  
+
   if(attr(data, "theory_type") %in% c("Two sample t", "Slope with t")){
-    
-    infer_plot <- both_t_plot(data = data, 
+
+    infer_plot <- both_t_plot(data = data,
                               deg_freedom = attr(data, "distr_param"),
                               statistic_text = "t",
                               dens_color = dens_color,
@@ -622,13 +622,13 @@ visualize_both <- function(data, bins,
                               endpoints = endpoints,
                               ci_fill = ci_fill)
   }
-  
+
   else if(attr(data, "theory_type") == "ANOVA"){
     infer_plot <- both_anova_plot(
-      data = data, 
-      deg_freedom_top = attr(data, "distr_param"), 
-      deg_freedom_bottom = attr(data, "distr_param2"), 
-      statistic_text = "F", 
+      data = data,
+      deg_freedom_top = attr(data, "distr_param"),
+      deg_freedom_bottom = attr(data, "distr_param2"),
+      statistic_text = "F",
       dens_color = dens_color,
       bins = bins,
       direction = direction,
@@ -637,11 +637,11 @@ visualize_both <- function(data, bins,
       endpoints = endpoints,
       ci_fill = ci_fill)
   }
-  
-  else if(attr(data, "theory_type") %in% 
+
+  else if(attr(data, "theory_type") %in%
           c("One sample prop z", "Two sample props z")){
-    infer_plot <- both_z_plot(data = data, 
-                              statistic_text = "z", 
+    infer_plot <- both_z_plot(data = data,
+                              statistic_text = "z",
                               dens_color = dens_color,
                               bins = bins,
                               direction = direction,
@@ -650,13 +650,13 @@ visualize_both <- function(data, bins,
                               endpoints = endpoints,
                               ci_fill = ci_fill)
   }
-  
+
   else if(
-    attr(data, "theory_type") %in% 
+    attr(data, "theory_type") %in%
     c("Chi-square test of indep", "Chi-square Goodness of Fit")){
-    infer_plot <- both_chisq_plot(data = data, 
+    infer_plot <- both_chisq_plot(data = data,
                                   deg_freedom = attr(data, "distr_param"),
-                                  statistic_text = "Chi-Square", 
+                                  statistic_text = "Chi-Square",
                                   dens_color = dens_color,
                                   bins = bins,
                                   direction = direction,
@@ -665,10 +665,10 @@ visualize_both <- function(data, bins,
                                   endpoints = endpoints,
                                   ci_fill = ci_fill)
   }
-  
+
 #  else
 #    stop_glue('"{attr(data, "theory_type")}" is not implemented yet.')
-  
+
   infer_plot
 }
 
diff --git a/R/wrappers.R b/R/wrappers.R
index 52908511..72e07f55 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -33,13 +33,13 @@
 #' @export
 t_test <- function(data, formula, #response = NULL, explanatory = NULL,
                    order = NULL,
-                   alternative = "two_sided", mu = 0, 
+                   alternative = "two_sided", mu = 0,
                    conf_int = TRUE,
                    conf_level = 0.95,
                    ...){
-  
+
   check_conf_level(conf_level)
-  
+
   # Match with old "dot" syntax
   if(alternative == "two_sided")
     alternative <- "two.sided"
@@ -47,17 +47,17 @@ t_test <- function(data, formula, #response = NULL, explanatory = NULL,
   ### Only currently working with formula interface
 #  if (hasArg(formula)) {
   if(!is.null(f_rhs(formula))){
-    
+
     data[[as.character(f_rhs(formula))]] <-
       factor(data[[as.character(f_rhs(formula))]],
              levels = c(order[1], order[2]))
-    
+
     # Two sample case
     prelim <- data %>%
       stats::t.test(formula = formula, data = .,
                     alternative = alternative,
-                    mu = mu, 
-                    conf.level = conf_level, 
+                    mu = mu,
+                    conf.level = conf_level,
                     ...) %>%
       broom::glance()
   } else {
@@ -67,24 +67,24 @@ t_test <- function(data, formula, #response = NULL, explanatory = NULL,
     data <- as.data.frame(data)
     prelim <- stats::t.test(x = data[[as.character(f_lhs(formula))]],
                   alternative = alternative,
-                  mu = mu, 
-                  conf.level = conf_level, 
-                  ...) %>% 
+                  mu = mu,
+                  conf.level = conf_level,
+                  ...) %>%
       broom::glance()
   }
-  
+
   if(conf_int){
-    results <- prelim %>% 
+    results <- prelim %>%
       dplyr::select(statistic, t_df = parameter, p_value = p.value,
-                    alternative, 
+                    alternative,
                     lower_ci = conf.low,
                     upper_ci = conf.high)
   } else {
-    results <- prelim %>% 
+    results <- prelim %>%
       dplyr::select(statistic, t_df = parameter, p_value = p.value,
                     alternative)
   }
-    
+
     return(results)
 #  } else {
     # data %>%
@@ -114,8 +114,8 @@ t_test <- function(data, formula, #response = NULL, explanatory = NULL,
 #' 
 #' @export
 t_stat <- function(data, formula, ...){
-  data %>% 
-    t_test(formula = formula, ...) %>% 
+  data %>%
+    t_test(formula = formula, ...) %>%
     dplyr::select(statistic)
 }
 
@@ -168,7 +168,7 @@ chisq_test <- function(data, formula, #response = NULL, explanatory = NULL,
 #' 
 #' @export
 chisq_stat <- function(data, formula, ...){
-  
+
   if(is.null(f_rhs(formula))){
     stop_glue(
       "`chisq_stat()` currently only has functionality for ",
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 8dda3b7b..34933f46 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -1,12 +1,12 @@
 context("calculate")
 
-iris_df <- tibble::as_tibble(iris) 
+iris_df <- tibble::as_tibble(iris)
 
-iris_tbl <- iris_df %>% 
+iris_tbl <- iris_df %>%
   dplyr::mutate(Sepal.Length.Group =
                   dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
                 Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small")) 
+                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
 
 # calculate arguments
 test_that("x is a tibble", {
@@ -29,7 +29,7 @@ test_that("stat argument is appropriate", {
 })
 
 test_that("response attribute has been set", {
-  expect_error(tibble::as.tibble(iris) %>% 
+  expect_error(tibble::as.tibble(iris) %>%
                  calculate(stat = "median")
                )
 })
@@ -40,7 +40,7 @@ test_that("variable chosen is of appropriate class (one var problems)", {
     specify(Species ~ NULL) %>%
     hypothesize(null = "point",
                 p = c("setosa" = .5,
-                      "versicolor" = .25, 
+                      "versicolor" = .25,
                       "virginica" = .25)) %>%
     generate(reps = 10, type = "simulate")
   expect_error(calculate(gen_iris1, stat = "mean"))
@@ -114,7 +114,7 @@ test_that("response variable is a factor (two var problems)", {
   expect_error(calculate(gen_iris4, stat = "diff in props"))
 
   expect_error(calculate(gen_iris4, stat = "t"))
-  
+
   # Check successful diff in props
   gen_iris4a <- iris %>%
     dplyr::mutate(Sepal.Length.Group =
@@ -142,9 +142,9 @@ test_that("response variable is numeric (two var problems)", {
 test_that("two sample mean-type problems are working", {
   gen_iris5a <- iris %>%
     dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>% 
-    specify(Sepal.Width ~ Sepal.Length.Group) %>% 
-    hypothesize(null = "independence") %>% 
+                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    specify(Sepal.Width ~ Sepal.Length.Group) %>%
+    hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
   expect_error(calculate(gen_iris5a, stat = "diff in means"))
   expect_silent(calculate(gen_iris5a, stat = "diff in means",
@@ -210,7 +210,7 @@ test_that("chi-square matches chisq.test value", {
     dplyr::do(broom::tidy(stats::chisq.test(table(.$Species)))) %>%
     dplyr::select(replicate, stat = statistic)
   expect_equal(infer_way, trad_way)
-  
+
   gen_iris9a <- iris %>%
     specify(Species ~ NULL) %>%
     hypothesize(null = "point",
@@ -226,7 +226,7 @@ test_that("chi-square matches chisq.test value", {
                                             p = c(0.8, 0.1, 0.1)))) %>%
     dplyr::select(replicate, stat = statistic)
   expect_equal(infer_way, trad_way)
-  
+
 })
 
 test_that("`order` is working", {
@@ -256,7 +256,7 @@ test_that("`order` is working", {
                          order = c(">5", "<=4", ">4")))
   # order not given
   expect_error(calculate(gen_iris11, stat = "diff in means"))
-  
+
 })
 
 test_that('success is working for stat = "prop"', {
@@ -270,7 +270,7 @@ test_that('success is working for stat = "prop"', {
                   calculate(stat = "prop"))
   expect_silent(gen_iris12 %>%
                   calculate(stat = "z"))
-  
+
 })
 
 test_that("NULL response gives error", {
@@ -325,61 +325,61 @@ test_that("order being given when not needed gives warning", {
 # })
 
 test_that("specify() %>% calculate() works", {
-  expect_silent(iris_tbl %>% 
+  expect_silent(iris_tbl %>%
                   specify(Petal.Width ~ NULL) %>%
                   calculate(stat = "mean")
   )
-  expect_error(iris_tbl %>% 
+  expect_error(iris_tbl %>%
                  specify(Petal.Width ~ NULL) %>%
                  hypothesize(null = "point", mu = 4) %>%
                  calculate(stat = "mean")
   )
-  
-  expect_error(iris_tbl %>% 
-                  specify(Species ~ NULL) %>% 
+
+  expect_error(iris_tbl %>%
+                  specify(Species ~ NULL) %>%
                   calculate(stat = "Chisq"))
 })
 
 test_that("One sample t hypothesis test is working", {
   expect_silent(
-    iris_tbl %>% 
-      specify(Petal.Width ~ NULL) %>% 
-      hypothesize(null = "point", mu = 1) %>% 
-      generate(reps = 10) %>% 
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 1) %>%
+      generate(reps = 10) %>%
       calculate(stat = "t")
   )
-  
+
 })
 
 test_that("specify done before calculate", {
 
-  iris_mean <- iris_tbl %>% 
+  iris_mean <- iris_tbl %>%
     dplyr::select(stat = Sepal.Width)
   expect_error(calculate(iris_mean, stat = "mean"))
-  
-  iris_prop <- iris_tbl %>% 
+
+  iris_prop <- iris_tbl %>%
     dplyr::select(Sepal.Length.Group)
   attr(iris_prop, "response") <- "Sepal.Length.Group"
   expect_error(calculate(iris_prop, stat = "prop"))
 })
 
 test_that("chisq GoF has params specified for observed stat", {
-  no_params <- iris_df %>% 
+  no_params <- iris_df %>%
     specify(response = Species)
-  expect_error(calculate(no_params, stat = "Chisq"))  
-  
-  params <- iris_df %>% 
-    specify(response = Species) %>% 
+  expect_error(calculate(no_params, stat = "Chisq"))
+
+  params <- iris_df %>%
+    specify(response = Species) %>%
     hypothesize(null = "point",
                 p = c("setosa" = .5,
-                      "versicolor" = .25, 
+                      "versicolor" = .25,
                       "virginica" = .25))
   expect_silent(calculate(params, stat = "Chisq"))
 })
 
 test_that("generate not done before calculate", {
-  iris_hyp <- iris_tbl %>% 
-    specify(Sepal.Width ~ Sepal.Length.Group) %>% 
+  iris_hyp <- iris_tbl %>%
+    specify(Sepal.Width ~ Sepal.Length.Group) %>%
     hypothesize(null = "independence")
   attr(iris_hyp, "generate") <- TRUE
   expect_warning(calculate(iris_hyp, stat = "t", order = c(">5", "<=5")))
@@ -387,10 +387,10 @@ test_that("generate not done before calculate", {
 
 test_that("One sample t bootstrap is working", {
   expect_silent(
-    iris_tbl %>% 
-      specify(Petal.Width ~ NULL) %>% 
-      generate(reps = 10) %>% 
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      generate(reps = 10) %>%
       calculate(stat = "t")
   )
-  
+
 })
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
index 0ae2ab3f..6dcc63ae 100644
--- a/tests/testthat/test-conf_int.R
+++ b/tests/testthat/test-conf_int.R
@@ -1,21 +1,21 @@
 context("conf_int")
 
-iris_tbl <- iris %>% 
+iris_tbl <- iris %>%
   dplyr::mutate(Sepal.Length.Group =
                   dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
                 Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small")) 
+                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
 
 iris_calc <- iris_tbl %>%
   specify(Sepal.Length.Group ~ Sepal.Width.Group,
           success = "<=5") %>%
   hypothesize(null = "independence") %>%
-  generate(reps = 1000) %>% 
+  generate(reps = 1000) %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
 obs_diff <-  iris_tbl %>%
   specify(Sepal.Length.Group ~ Sepal.Width.Group,
-          success = "<=5") %>% 
+          success = "<=5") %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
 set.seed(2018)
@@ -23,19 +23,19 @@ test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("basics work", {
   expect_silent(
-    test_df %>% 
+    test_df %>%
       conf_int()
     )
   expect_error(
-    test_df %>% 
+    test_df %>%
       conf_int(type = "other")
   )
   expect_error(
-    test_df %>% 
+    test_df %>%
       conf_int(level = 1.2)
   )
   expect_error(
-    test_df %>% 
+    test_df %>%
       conf_int(point_estimate = "help")
   )
   expect_silent(
@@ -51,4 +51,3 @@ test_that("basics work", {
     iris_calc %>% get_ci(type = "se")
   )
 })
-
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index cbf4617f..02ed4b43 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -75,7 +75,7 @@ test_that("cohesion with type argument", {
 
 test_that("sensible output", {
 
-  expect_equal(nrow(mtcars) * 500, 
+  expect_equal(nrow(mtcars) * 500,
                nrow(generate(hyp_prop, reps = 500, type = "simulate")))
   expect_silent(generate(hyp_mean, reps = 1, type = "bootstrap"))
   expect_error(generate(hyp_mean, reps = 1, type = "other"))
@@ -88,74 +88,74 @@ test_that("auto `type` works (generate)", {
                   am = factor(am),
                   gear = factor(gear),
                   carb = factor(carb))
-  
+
   one_mean <- mtcars %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
-    hypothesize(null = "point", mu = 25) %>% 
+    hypothesize(null = "point", mu = 25) %>%
     generate(reps = 100)
-  
+
   one_nonshift_mean <- mtcars %>%
     specify(response = mpg) %>%
     generate(reps = 100)
-  
+
   one_median <- mtcars %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
-    hypothesize(null = "point", med = 26) %>% 
+    hypothesize(null = "point", med = 26) %>%
     generate(reps = 100)
-  
+
   one_prop <- mtcars %>%
     specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-    hypothesize(null = "point", p = .25) %>% 
+    hypothesize(null = "point", p = .25) %>%
     generate(reps = 100)
-  
+
   two_props <- mtcars %>%
     specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
     hypothesize(null = "independence") %>%
     generate(reps = 100)
-  
+
   gof_chisq <- mtcars %>%
     specify(cyl ~ NULL) %>% # alt: response = cyl
     hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-    generate(reps = 100) 
-  
+    generate(reps = 100)
+
   indep_chisq <- mtcars %>%
     specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
     hypothesize(null = "independence") %>%
-    generate(reps = 100) 
-  
+    generate(reps = 100)
+
   two_means <- mtcars %>%
     specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
     hypothesize(null = "independence") %>%
     generate(reps = 100)
-  
+
   anova_f <- mtcars %>%
     specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100)
-  
+
   slopes <- mtcars %>%
     specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100)
-  
+
   one_nonshift_prop <- mtcars %>%
     specify(response = am, success = "1") %>%
     generate(reps = 100)
-  
+
   two_means_boot <- mtcars %>%
     specify(mpg ~ am) %>%
     generate(reps = 100)
-  
+
   two_props_boot <- mtcars %>%
     specify(am ~ vs, success = "1") %>%
     generate(reps = 100)
-  
+
   slope_boot <- mtcars %>%
-    specify(mpg ~ hp) %>% 
+    specify(mpg ~ hp) %>%
     generate(reps = 100)
-  
+
     expect_equal(attr(one_mean, "type"), "bootstrap")
-    expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")    
+    expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")
     expect_equal(attr(one_median, "type"), "bootstrap")
     expect_equal(attr(one_prop, "type"), "simulate")
     expect_equal(attr(two_props, "type"), "permute")
@@ -168,89 +168,89 @@ test_that("auto `type` works (generate)", {
     expect_equal(attr(two_means_boot, "type"), "bootstrap")
     expect_equal(attr(two_props_boot, "type"), "bootstrap")
     expect_equal(attr(slope_boot, "type"), "bootstrap")
-    
+
     expect_error(mtcars %>%
       specify(response = mpg) %>% # formula alt: mpg ~ NULL
-      hypothesize(null = "point", mu = 25) %>% 
+      hypothesize(null = "point", mu = 25) %>%
       generate(reps = 100, type = "permute"))
-    
+
     expect_error(mtcars %>%
       specify(response = mpg) %>%
       generate(reps = 100, type = "simulate"))
-    
+
     expect_error(mtcars %>%
       specify(response = mpg) %>% # formula alt: mpg ~ NULL
-      hypothesize(null = "point", med = 26) %>% 
+      hypothesize(null = "point", med = 26) %>%
       generate(reps = 100, type = "permute"))
-    
+
     expect_error(mtcars %>%
       specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-      hypothesize(null = "point", p = .25) %>% 
+      hypothesize(null = "point", p = .25) %>%
       generate(reps = 100, type = "bootstrap"))
-    
+
     expect_error(mtcars %>%
       specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap"))
-    
+
     expect_error(mtcars %>%
       specify(cyl ~ NULL) %>% # alt: response = cyl
       hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
       generate(reps = 100, type = "bootstrap"))
-    
+
     expect_error(mtcars %>%
       specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "simulate"))
-    
+
     expect_error(mtcars %>%
       specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap"))
-    
+
     expect_error(mtcars %>%
       specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "simulate"))
-    
+
     expect_error(mtcars %>%
       specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap"))
-    
+
     expect_error(mtcars %>%
       specify(response = am, success = "1") %>%
       generate(reps = 100, type = "simulate"))
-    
+
     expect_error(mtcars %>%
       specify(mpg ~ am) %>%
       generate(reps = 100, type = "permute"))
-    
+
     expect_error(mtcars %>%
       specify(am ~ vs, success = "1") %>%
       generate(reps = 100, type = "simulate"))
-    
+
     expect_error(mtcars %>%
-      specify(mpg ~ hp) %>% 
+      specify(mpg ~ hp) %>%
       generate(reps = 100, type = "simulate"))
-    
+
 })
 
 test_that("mismatches lead to error", {
   expect_error(mtcars %>% generate(reps = 10, type = "permute"))
-  expect_error(mtcars %>% specify(am ~ NULL, success = "1") %>% 
-                 hypothesize(null = "independence", p = c("1" = 0.5)) %>% 
+  expect_error(mtcars %>% specify(am ~ NULL, success = "1") %>%
+                 hypothesize(null = "independence", p = c("1" = 0.5)) %>%
                  generate(reps = 100, type = "simulate"))
   expect_error(mtcars %>%
                  specify(cyl ~ NULL) %>% # alt: response = cyl
-                 hypothesize(null = "point", p = c("4" = .5, "6" = .25, 
-                                                   "8" = .25)) %>% 
+                 hypothesize(null = "point", p = c("4" = .5, "6" = .25,
+                                                   "8" = .25)) %>%
                  generate(reps = 100, type = "bootstrap"))
   expect_error(mtcars %>%
-                 specify(mpg ~ hp) %>% 
+                 specify(mpg ~ hp) %>%
                  generate(reps = 100, type = "other"))
 })
 
 test_that("generate() handles `NULL` value of `type`", {
   expect_error(generate(hyp_prop, type = NULL), "NULL.*type")
-})
\ No newline at end of file
+})
diff --git a/tests/testthat/test-hypothesize.R b/tests/testthat/test-hypothesize.R
index 876ea935..828a815d 100644
--- a/tests/testthat/test-hypothesize.R
+++ b/tests/testthat/test-hypothesize.R
@@ -67,47 +67,47 @@ test_that("auto `type` works (hypothesize)", {
 })
 
 test_that("hypothesize arguments function",{
-  
+
   mtcars_f <- dplyr::mutate(mtcars, cyl = factor(cyl))
   mtcars_s <- mtcars_f %>% specify(response = mpg)
   matrix1 <- matrix(data = NA, nrow = 3, ncol = 3)
-  
+
   expect_error(hypothesize(matrix1))
   expect_error(hypothesize(mtcars_s, null = NA))
   expect_error(hypothesize(mtcars_s))
-  
+
   expect_error(mtcars_s %>% hypothesize(null = "point", mean = 3))
-  
+
   expect_error(mtcars_s %>% hypothesize(null = "independence"))
   expect_error(mtcars_s %>% hypothesize(null = "point"))
   # Produces error on win-build
 #  expect_warning(mtcars_s %>% 
 #                   hypothesize(null = c("point", "independence"), mu = 3))
-  
+
   expect_error(mtcars %>% dplyr::select(vs) %>%
                  hypothesize(null = "point", mu = 1))
-  
-  expect_error(mtcars %>% specify(response = vs) %>% 
+
+  expect_error(mtcars %>% specify(response = vs) %>%
                  hypothesize(null = "point", mu = 1))
-  
-  expect_error(mtcars %>% specify(response = vs, success = "1") %>% 
+
+  expect_error(mtcars %>% specify(response = vs, success = "1") %>%
                  hypothesize(null = "point", p = 1.1))
-  expect_error(mtcars %>% specify(response = vs, success = "1") %>% 
+  expect_error(mtcars %>% specify(response = vs, success = "1") %>%
                  hypothesize(null = "point", p = -23))
-  
-  expect_error(mtcars_s %>% 
-                 hypothesize(null = "point", 
+
+  expect_error(mtcars_s %>%
+                 hypothesize(null = "point",
                              p = c("4" = .2, "6" = .25, "8" = .25)))
-  
+
   expect_error(mtcars_s %>% hypothesize(null = "point", p = 0.2))
   expect_warning(mtcars %>% specify(mpg ~ vs) %>%
                    hypothesize(null = "independence", p = 0.5))
-  
+
   expect_error(mtcars_s %>% hypothesize())
 })
 
 test_that("params correct", {
-  expect_error(hypothesize(one_prop_specify, 
+  expect_error(hypothesize(one_prop_specify,
                            null = "point", mu = 2))
   expect_error(hypothesize(one_mean_specify,
                            null = "point", mean = 0.5))
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index 7735ad8e..75e6ff4b 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -1,16 +1,16 @@
 context("p_value")
 
-iris_tbl <- iris %>% 
+iris_tbl <- iris %>%
   dplyr::mutate(Sepal.Length.Group =
                   dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
                 Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small")) 
+                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
 
 iris_calc <- iris_tbl %>%
   specify(Sepal.Length.Group ~ Sepal.Width.Group,
           success = "<=5") %>%
   hypothesize(null = "independence") %>%
-  generate(reps = 1000) %>% 
+  generate(reps = 1000) %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
 set.seed(2018)
@@ -18,48 +18,48 @@ test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("direction is appropriate", {
   expect_error(
-    test_df %>% 
+    test_df %>%
       p_value(obs_stat = 0.5, direction = "righ")
     )
 })
 
 test_that("p_value makes sense", {
   expect_lt(
-    iris_calc %>% 
-      p_value(obs_stat = 0.1, direction = "right") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      p_value(obs_stat = 0.1, direction = "right") %>%
+      dplyr::pull(),
     expected = 0.1
     )
   expect_gt(
-    iris_calc %>% 
-      p_value(obs_stat = -0.1, direction = "greater") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      p_value(obs_stat = -0.1, direction = "greater") %>%
+      dplyr::pull(),
     expected = 0.9
   )
   expect_equal(
-    iris_calc %>% 
-      p_value(obs_stat = median(iris_calc$stat), 
-              direction = "both") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      p_value(obs_stat = median(iris_calc$stat),
+              direction = "both") %>%
+      dplyr::pull(),
     expected = 1
   )
   expect_lt(
-    iris_calc %>% 
-      p_value(obs_stat = -0.2, direction = "left") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      p_value(obs_stat = -0.2, direction = "left") %>%
+      dplyr::pull(),
     expected = 0.02
   )
   expect_gt(
-    iris_calc %>% 
-      p_value(obs_stat = -0.2, direction = "right") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      p_value(obs_stat = -0.2, direction = "right") %>%
+      dplyr::pull(),
     expected = 0.98
   )
   expect_equal(
-    iris_calc %>% 
-      get_pvalue(obs_stat = median(iris_calc$stat) + 1, 
-              direction = "two_sided") %>% 
-      dplyr::pull(), 
+    iris_calc %>%
+      get_pvalue(obs_stat = median(iris_calc$stat) + 1,
+              direction = "two_sided") %>%
+      dplyr::pull(),
     expected = 0
   )
 })
diff --git a/tests/testthat/test-rep_sample_n.R b/tests/testthat/test-rep_sample_n.R
index 69af2136..8e9f9544 100644
--- a/tests/testthat/test-rep_sample_n.R
+++ b/tests/testthat/test-rep_sample_n.R
@@ -9,19 +9,18 @@ population <- tibble::data_frame(
 test_that("rep_sample_n works", {
   expect_silent(population %>%
                   rep_sample_n(size = 2, reps = 10))
-  expect_error(population %>% 
-                  rep_sample_n(size = 2, reps = 10, 
+  expect_error(population %>%
+                  rep_sample_n(size = 2, reps = 10,
                                prob = rep(x = 1/5, times = 100)))
-  expect_error(population %>% 
+  expect_error(population %>%
                  rep_sample_n(size = 2, reps = 10,
                               prob = c(1/2, 1/2)))
-  expect_error(population %>% 
+  expect_error(population %>%
                  rep_sample_n(size = 2, reps = 10,
                               prob = c(0.25, 1/5, 1/5, 1/5, 0.15)))
   test_rep <- population %>%
     rep_sample_n(size = 2, reps = 10)
   expect_equal(c("replicate", names(population)), names(test_rep))
-  
-  
-})
 
+
+})
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index 5742ef7d..f86f18b0 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -16,14 +16,14 @@ test_that("message_glue handles `NULL`", {
 
 test_that("glue_null works", {
   adj <- "quick"
-  
+
   expect_equal(
     glue_null(
       "The {adj} brown {null_val} jumps ", "over the lazy {NULL}."
     ),
     "The quick brown NULL jumps over the lazy NULL."
   )
-  
+
   expect_equal(
     glue_null("The {adj}", "brown", .sep = "-"),
     "The quick-brown"
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 0a3e3a46..55fc5ad0 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -6,20 +6,20 @@ library(dplyr)
 Sepal.Width_resamp <- iris %>%
   specify(Sepal.Width ~ NULL) %>%
   hypothesize(null = "point", med = 3) %>%
-  generate(reps = 10, type = "bootstrap") %>% 
-  calculate(stat = "median") 
+  generate(reps = 10, type = "bootstrap") %>%
+  calculate(stat = "median")
 
-iris_tbl <- tibble::as_tibble(iris) %>% 
+iris_tbl <- tibble::as_tibble(iris) %>%
   dplyr::mutate(Sepal.Length.Group =
                   dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
                 Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small")) 
-  
-obs_slope <- lm(Sepal.Length ~ Sepal.Width, 
-                   data = iris_tbl) %>% 
-  broom::tidy() %>% 
-  dplyr::filter(term == "Sepal.Width") %>% 
-  dplyr::select(estimate) %>% 
+                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
+
+obs_slope <- lm(Sepal.Length ~ Sepal.Width,
+                   data = iris_tbl) %>%
+  broom::tidy() %>%
+  dplyr::filter(term == "Sepal.Width") %>%
+  dplyr::select(estimate) %>%
   dplyr::pull()
 
 obs_diff <- iris_tbl %>%
@@ -28,14 +28,14 @@ obs_diff <- iris_tbl %>%
   summarize(diff(prop)) %>%
   pull()
 
-obs_z <- sqrt(stats::prop.test(x = table(iris_tbl$Sepal.Length.Group, 
+obs_z <- sqrt(stats::prop.test(x = table(iris_tbl$Sepal.Length.Group,
                                     iris_tbl$Sepal.Width.Group),
                           n = nrow(iris_tbl),
                           alternative = "two.sided",
                           correct = FALSE)$statistic)
 
 obs_diff_mean <- iris_tbl %>%
-  group_by(Sepal.Length.Group) %>% 
+  group_by(Sepal.Length.Group) %>%
   summarize(mean_sepal_width = mean(Sepal.Width)) %>%
   summarize(diff(mean_sepal_width)) %>%
   pull()
@@ -47,7 +47,7 @@ obs_t <- iris_tbl %>%
 obs_F <- anova(
     aov(formula = Sepal.Width ~ Species, data = iris_tbl)
   )$`F value`[1]
-  
+
 
 test_that("visualize basic tests", {
   expect_silent(visualize(Sepal.Width_resamp))
@@ -57,235 +57,235 @@ test_that("visualize basic tests", {
   expect_silent(iris_tbl %>%
                   specify(Sepal.Length ~ Sepal.Width) %>%
                   hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "slope") %>% 
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "slope") %>%
                   visualize(obs_stat = obs_slope, direction = "right"))
-  
+
   #obs_stat not specified
-  expect_error(iris_tbl %>% 
+  expect_error(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "diff in props", 
-                            order = c(">5", "<=5")) %>% 
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "diff in props",
+                            order = c(">5", "<=5")) %>%
                   visualize(direction = "both")
                 )
-  
-  expect_silent(iris_tbl %>% 
+
+  expect_silent(iris_tbl %>%
                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>% 
-                 hypothesize(null = "independence") %>% 
-                 generate(reps = 100, type = "permute") %>% 
-                 calculate(stat = "diff in props", 
-                           order = c(">5", "<=5")) %>% 
+                         success = "large") %>%
+                 hypothesize(null = "independence") %>%
+                 generate(reps = 100, type = "permute") %>%
+                 calculate(stat = "diff in props",
+                           order = c(">5", "<=5")) %>%
                  visualize(direction = "both", obs_stat = obs_diff)
   )
-  
-  expect_warning(iris_tbl %>% 
+
+  expect_warning(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
-                  calculate(stat = "z", order = c(">5", "<=5")) %>% 
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  calculate(stat = "z", order = c(">5", "<=5")) %>%
                   visualize(method = "theoretical")
   )
-  
+
   # diff in props and z on different scales
-  expect_error(expect_warning(iris_tbl %>% 
+  expect_error(expect_warning(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "diff in props", 
-                            order = c(">5", "<=5")) %>% 
-                  visualize(method = "both", direction = "both", 
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "diff in props",
+                            order = c(">5", "<=5")) %>%
+                  visualize(method = "both", direction = "both",
                             obs_stat = obs_diff)
   ))
-  
-  expect_silent(iris_tbl %>% 
+
+  expect_silent(iris_tbl %>%
                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>% 
-                 hypothesize(null = "independence") %>% 
-                 generate(reps = 100, type = "permute") %>% 
-                 calculate(stat = "diff in props", 
-                           order = c(">5", "<=5")) %>% 
+                         success = "large") %>%
+                 hypothesize(null = "independence") %>%
+                 generate(reps = 100, type = "permute") %>%
+                 calculate(stat = "diff in props",
+                           order = c(">5", "<=5")) %>%
                  visualize()
   )
-  
-  expect_warning(iris_tbl %>% 
+
+  expect_warning(iris_tbl %>%
                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>% 
-                 hypothesize(null = "independence") %>% 
-                 generate(reps = 100, type = "permute") %>% 
-                 calculate(stat = "z", 
-                           order = c(">5", "<=5")) %>% 
+                         success = "large") %>%
+                 hypothesize(null = "independence") %>%
+                 generate(reps = 100, type = "permute") %>%
+                 calculate(stat = "z",
+                           order = c(">5", "<=5")) %>%
                  visualize(method = "both", direction = "both",
                            obs_stat = obs_z)
   )
-  
-  expect_warning(iris_tbl %>% 
+
+  expect_warning(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "z", 
-                            order = c("<=5", ">5")) %>% 
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "z",
+                            order = c("<=5", ">5")) %>%
                   visualize(method = "both", direction = "both",
                             obs_stat = -obs_z)
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Sepal.Width.Group) %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "t", order = c("small", "large") ) %>% 
-                  visualize(method = "both", direction = "left", 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Sepal.Width.Group) %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "t", order = c("small", "large") ) %>%
+                  visualize(method = "both", direction = "left",
                             obs_stat = -obs_t)
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Sepal.Width.Group) %>% 
-                  hypothesize(null = "independence") %>% 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Sepal.Width.Group) %>%
+                  hypothesize(null = "independence") %>%
  #                 generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "t", order = c("small", "large") ) %>% 
-                  visualize(method = "theoretical", direction = "left", 
+                  calculate(stat = "t", order = c("small", "large") ) %>%
+                  visualize(method = "theoretical", direction = "left",
                             obs_stat = -obs_t)
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Sepal.Length.Group) %>% 
-                  hypothesize(null = "independence") %>% 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Sepal.Length.Group) %>%
+                  hypothesize(null = "independence") %>%
                   visualize(method = "theoretical")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Species) %>% 
-                  hypothesize(null = "independence") %>% 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Species) %>%
+                  hypothesize(null = "independence") %>%
                   visualize(method = "theoretical")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Species) %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "F") %>% 
-                  visualize(method = "both", obs_stat = obs_F, 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Species) %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "F") %>%
+                  visualize(method = "both", obs_stat = obs_F,
                             direction = "right")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Length ~ Species) %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "F") %>% 
-                  visualize(method = "both", obs_stat = obs_F, 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Length ~ Species) %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "F") %>%
+                  visualize(method = "both", obs_stat = obs_F,
                             direction = "left")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Width.Group ~ Species, 
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "Chisq") %>% 
-                  visualize(method = "both", obs_stat = obs_F, 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Width.Group ~ Species,
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "Chisq") %>%
+                  visualize(method = "both", obs_stat = obs_F,
                             direction = "right")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Width.Group ~ Species, 
-                          success = "large") %>% 
-                  hypothesize(null = "independence") %>% 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Width.Group ~ Species,
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
                   #calculate(stat = "Chisq") %>% 
-                  visualize(method = "theoretical", obs_stat = obs_F, 
+                  visualize(method = "theoretical", obs_stat = obs_F,
                             direction = "right")
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Species ~ NULL) %>% 
-                  hypothesize(null = "point", 
+
+  expect_warning(iris_tbl %>%
+                  specify(Species ~ NULL) %>%
+                  hypothesize(null = "point",
                               p = c("setosa" = 0.4,
                                     "versicolor" = 0.4,
-                                    "virginica" = 0.2)) %>% 
-                  generate(reps = 100, type = "simulate") %>% 
-                  calculate(stat = "Chisq") %>% 
+                                    "virginica" = 0.2)) %>%
+                  generate(reps = 100, type = "simulate") %>%
+                  calculate(stat = "Chisq") %>%
                   visualize(method = "both")
   )
-  
+
   #traditional instead of theoretical
-  expect_error(iris_tbl %>% 
-                  specify(Species ~ NULL) %>% 
-                  hypothesize(null = "point", 
+  expect_error(iris_tbl %>%
+                  specify(Species ~ NULL) %>%
+                  hypothesize(null = "point",
                               p = c("setosa" = 0.4,
                                     "versicolor" = 0.4,
-                                    "virginica" = 0.2)) %>% 
+                                    "virginica" = 0.2)) %>%
 #                  generate(reps = 100, type = "simulate") %>% 
 #                  calculate(stat = "Chisq") %>% 
                   visualize(method = "traditional")
   )
-  
-  expect_warning(iris_tbl %>% 
-                 specify(Species ~ NULL) %>% 
-                 hypothesize(null = "point", 
+
+  expect_warning(iris_tbl %>%
+                 specify(Species ~ NULL) %>%
+                 hypothesize(null = "point",
                              p = c("setosa" = 0.4,
                                    "versicolor" = 0.4,
-                                   "virginica" = 0.2)) %>% 
+                                   "virginica" = 0.2)) %>%
                  #generate(reps = 100, type = "simulate") %>% 
                  #calculate(stat = "Chisq") %>% 
                  visualize(method = "theoretical")
   )
-  
-  expect_silent(iris_tbl %>% 
-                  specify(Petal.Width ~ Sepal.Width.Group) %>% 
-                  hypothesize(null = "independence") %>% 
-                  generate(reps = 10, type = "permute") %>% 
-                  calculate(stat = "diff in means", 
-                            order = c("large", "small")) %>% 
+
+  expect_silent(iris_tbl %>%
+                  specify(Petal.Width ~ Sepal.Width.Group) %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 10, type = "permute") %>%
+                  calculate(stat = "diff in means",
+                            order = c("large", "small")) %>%
                   visualize(direction = "both",
                             obs_stat = obs_diff_mean)
   )
-  
+
   # Produces warning first for not checking conditions but would also error
-  expect_error(expect_warning(iris_tbl %>% 
-                   specify(Petal.Width ~ Sepal.Width.Group) %>% 
-                   hypothesize(null = "independence") %>% 
-                   generate(reps = 100, type = "permute") %>% 
-                   calculate(stat = "diff in means", 
-                             order = c("large", "small")) %>% 
+  expect_error(expect_warning(iris_tbl %>%
+                   specify(Petal.Width ~ Sepal.Width.Group) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "diff in means",
+                             order = c("large", "small")) %>%
                    visualize(method = "both", direction = "both",
                              obs_stat = obs_diff_mean)
   ))
-  
-  expect_warning(iris_tbl %>% 
-                 specify(Petal.Width ~ Sepal.Width.Group) %>% 
-                 hypothesize(null = "independence") %>% 
-                 generate(reps = 100, type = "permute") %>% 
-                 calculate(stat = "diff in means", 
-                           order = c("large", "small")) %>% 
+
+  expect_warning(iris_tbl %>%
+                 specify(Petal.Width ~ Sepal.Width.Group) %>%
+                 hypothesize(null = "independence") %>%
+                 generate(reps = 100, type = "permute") %>%
+                 calculate(stat = "diff in means",
+                           order = c("large", "small")) %>%
                  visualize(method = "theoretical", direction = "both",
                            obs_stat = obs_diff_mean)
   )
-  
-  expect_warning(iris_tbl %>% 
-                  specify(Sepal.Width.Group ~ NULL, success = "small") %>% 
-                  hypothesize(null = "point", p = 0.8) %>% 
+
+  expect_warning(iris_tbl %>%
+                  specify(Sepal.Width.Group ~ NULL, success = "small") %>%
+                  hypothesize(null = "point", p = 0.8) %>%
 #                 generate(reps = 100, type = "simulate") %>% 
 #                  calculate(stat = "z") %>% 
-                  visualize(method = "theoretical", 
+                  visualize(method = "theoretical",
                             obs_stat = 2, # Should probably update
                             direction = "both")
   )
-  
+
   expect_silent(iris_tbl %>%
                   specify(Petal.Width ~ NULL) %>%
-                  hypothesize(null = "point", mu = 1.3) %>% 
-                  generate(reps = 100, type = "bootstrap") %>% 
-                  calculate(stat = "mean") %>% 
+                  hypothesize(null = "point", mu = 1.3) %>%
+                  generate(reps = 100, type = "bootstrap") %>%
+                  calculate(stat = "mean") %>%
                   visualize(direction = "left",
                             obs_stat = mean(iris$Petal.Width))
   )
-  
-  
+
+
 })
 
 test_that("get_percentile works", {
@@ -293,114 +293,114 @@ test_that("get_percentile works", {
 })
 
 test_that("obs_stat as a data.frame works", {
-  mean_petal_width <- iris_tbl %>% 
+  mean_petal_width <- iris_tbl %>%
                   specify(Petal.Width ~ NULL) %>%
                   calculate(stat = "mean")
-  expect_silent(iris_tbl %>% 
+  expect_silent(iris_tbl %>%
                  specify(Petal.Width ~ NULL) %>%
                  hypothesize(null = "point", mu = 4) %>%
                   generate(reps = 100, type = "bootstrap") %>%
-                 calculate(stat = "mean") %>% 
+                 calculate(stat = "mean") %>%
                  visualize(obs_stat = mean_petal_width)
   )
   mean_df_test <- data.frame(x = c(4.1, 1), y = c(1, 2))
-  expect_warning(iris_tbl %>% 
+  expect_warning(iris_tbl %>%
                    specify(Petal.Width ~ NULL) %>%
                    hypothesize(null = "point", mu = 4) %>%
                    generate(reps = 100, type = "bootstrap") %>%
-                   calculate(stat = "mean") %>% 
+                   calculate(stat = "mean") %>%
                    visualize(obs_stat = mean_df_test)
                  )
-  
+
 })
 
 
 test_that('method = "both" behaves nicely', {
   # stop_glue('`generate()` and `calculate()` are both required ',
   #           'to be done prior to `visualize(method = "both")`')
-  expect_error(iris_tbl %>% 
+  expect_error(iris_tbl %>%
                  specify(Petal.Width ~ NULL) %>%
                  hypothesize(null = "point", mu = 4) %>%
                  generate(reps = 100, type = "bootstrap") %>%
                  #    calculate(stat = "mean") %>% 
                  visualize(method = "both"))
-  
+
   #  
-  expect_warning(iris_tbl %>% 
+  expect_warning(iris_tbl %>%
                    specify(Petal.Width ~ Sepal.Length.Group) %>%
                    hypothesize(null = "point", mu = 4) %>%
                    generate(reps = 10, type = "bootstrap") %>%
-                   calculate(stat = "t", order = c(">5", "<=5")) %>% 
+                   calculate(stat = "t", order = c(">5", "<=5")) %>%
                    visualize(method = "both")
-  )  
+  )
 })
 
 test_that("Traditional right-tailed tests have warning if not right-tailed", {
-  expect_warning(iris_tbl %>% 
-                   specify(Sepal.Width.Group ~ Species, 
-                           success = "large") %>% 
-                   hypothesize(null = "independence") %>% 
-                   generate(reps = 100, type = "permute") %>% 
-                   calculate(stat = "Chisq") %>% 
+  expect_warning(iris_tbl %>%
+                   specify(Sepal.Width.Group ~ Species,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "Chisq") %>%
                    visualize(method = "both", obs_stat = 2, direction = "left")
                  )
-  expect_warning(iris_tbl %>% 
-                   specify(Sepal.Length ~ Species) %>% 
-                   hypothesize(null = "independence") %>% 
-                   generate(reps = 100, type = "permute") %>% 
-                   calculate(stat = "F") %>% 
-                   visualize(method = "both", obs_stat = 2, 
+  expect_warning(iris_tbl %>%
+                   specify(Sepal.Length ~ Species) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "F") %>%
+                   visualize(method = "both", obs_stat = 2,
                              direction = "two_sided")
   )
-  expect_warning(iris_tbl %>% 
-                   specify(Sepal.Width.Group ~ Species, 
-                           success = "large") %>% 
-                   hypothesize(null = "independence") %>% 
+  expect_warning(iris_tbl %>%
+                   specify(Sepal.Width.Group ~ Species,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
  #                  generate(reps = 100, type = "permute") %>% 
-                   calculate(stat = "Chisq") %>% 
-                   visualize(method = "theoretical", obs_stat = 2, 
+                   calculate(stat = "Chisq") %>%
+                   visualize(method = "theoretical", obs_stat = 2,
                              direction = "left")
   )
-  expect_warning(iris_tbl %>% 
-                   specify(Sepal.Length ~ Species) %>% 
-                   hypothesize(null = "independence") %>% 
+  expect_warning(iris_tbl %>%
+                   specify(Sepal.Length ~ Species) %>%
+                   hypothesize(null = "independence") %>%
   #                 generate(reps = 100, type = "permute") %>% 
-                   calculate(stat = "F") %>% 
-                   visualize(method = "theoretical", obs_stat = 2, 
+                   calculate(stat = "F") %>%
+                   visualize(method = "theoretical", obs_stat = 2,
                              direction = "two_sided")
   )
-  
+
 })
 
 test_that("confidence interval plots are working",{
-  
-  iris_boot <- iris_tbl %>% 
+
+  iris_boot <- iris_tbl %>%
     specify(Sepal.Width.Group ~ Sepal.Length.Group,
-            success = "large") %>% 
-    generate(reps = 100) %>% 
-    calculate(stat = "diff in props", 
+            success = "large") %>%
+    generate(reps = 100) %>%
+    calculate(stat = "diff in props",
               order = c(">5", "<=5"))
-  
+
   df_error <- tibble::tibble(col1 = rnorm(5), col2 = rnorm(5))
   vec_error <- 1:10
-  
+
   perc_ci <- iris_boot %>% get_ci()
-  
+
   expect_error(
-    iris_boot %>% visualize(endpoints = df_error) 
+    iris_boot %>% visualize(endpoints = df_error)
   )
-  
+
   expect_warning(
-    iris_boot %>% visualize(endpoints = vec_error) 
+    iris_boot %>% visualize(endpoints = vec_error)
   )
-  
+
   expect_silent(
     iris_boot %>% visualize(endpoints = perc_ci,
                             direction = "between")
   )
-  
+
   expect_warning(
     iris_boot %>% visualize(obs_stat = 3, endpoints = perc_ci)
   )
-  
+
 })
diff --git a/tests/testthat/test-wrappers.R b/tests/testthat/test-wrappers.R
index d210dc34..b43a331d 100644
--- a/tests/testthat/test-wrappers.R
+++ b/tests/testthat/test-wrappers.R
@@ -1,17 +1,17 @@
 context("wrappers")
 
 iris2 <- iris %>%
-  dplyr::filter(Species != "setosa") %>% 
+  dplyr::filter(Species != "setosa") %>%
   droplevels(.$Species)
 
 iris3 <- iris %>%
-  dplyr::mutate(Sepal.Length.Group = 
+  dplyr::mutate(Sepal.Length.Group =
                   dplyr::if_else(Sepal.Length > 5, ">5", "<=5"))
 
 test_that("t_test works", {
   # order is missing
   expect_error(iris2 %>% t_test(Sepal.Width ~ Species))
-  
+
   expect_error(iris2 %>% t_test(response = "Sepal.Width",
                                 explanatory = "Species"))
 ## Not implemented
@@ -23,10 +23,10 @@ test_that("chisq_test works", {
   expect_silent(iris3 %>% chisq_test(Sepal.Length.Group ~ Species))
   new_way <- iris3 %>% chisq_test(Sepal.Length.Group ~ Species)
   old_way <- chisq.test(x = table(iris3$Species,
-                                  iris3$Sepal.Length.Group)) %>% 
+                                  iris3$Sepal.Length.Group)) %>%
     broom::glance() %>%
     dplyr::select(statistic, chisq_df = parameter, p_value = p.value)
-  
+
   expect_equal(new_way, old_way)
   ## Not implemented
   #  expect_silent(iris3 %>% chisq_test(response = Sepal.Length.Group,
@@ -40,18 +40,18 @@ test_that("_stat functions work", {
     )
   another_way <- iris3 %>%
     chisq_test(Sepal.Length.Group ~ Species) %>%
-    dplyr::select(statistic) %>% 
+    dplyr::select(statistic) %>%
     dplyr::rename(stat = statistic)
-  obs_stat_way <- iris3 %>% 
+  obs_stat_way <- iris3 %>%
     chisq_stat(Sepal.Length.Group ~ Species)
   one_more <- chisq.test(
-    table(iris3$Species, 
+    table(iris3$Species,
                 iris3$Sepal.Length.Group)
     )$statistic
 
   expect_equivalent(another_way, obs_stat_way)
   expect_equivalent(one_more, dplyr::pull(obs_stat_way))
-  
+
   # Goodness of Fit
   expect_error(iris3 %>% chisq_test(Species ~ NULL))
   expect_error(iris3 %>% chisq_stat(Species ~ NULL))
@@ -61,81 +61,81 @@ test_that("_stat functions work", {
 #  obs_stat_way <- iris3 %>% 
 #    chisq_stat(Species ~ NULL)
 #  expect_equivalent(another_way, obs_stat_way)
-  
+
   # Two sample t
   expect_silent(
-    iris2 %>% t_stat(Sepal.Width ~ Species, 
+    iris2 %>% t_stat(Sepal.Width ~ Species,
                                  order = c("virginica", "versicolor"))
     )
   another_way <- iris2 %>%
     t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor")) %>%
     dplyr::select(statistic)
-  obs_stat_way <- iris2 %>% 
+  obs_stat_way <- iris2 %>%
     t_stat(Sepal.Width ~ Species, order = c("virginica", "versicolor"))
   expect_equivalent(another_way, obs_stat_way)
-  
+
   # One sample t
   expect_silent(iris2 %>% t_stat(Sepal.Width ~ NULL))
   another_way <- iris2 %>%
     t_test(Sepal.Width ~ NULL) %>%
     dplyr::select(statistic)
-  obs_stat_way <- iris2 %>% 
+  obs_stat_way <- iris2 %>%
     t_stat(Sepal.Width ~ NULL)
   expect_equivalent(another_way, obs_stat_way)
 })
 
 test_that("conf_int argument works", {
   expect_equal(
-    names(iris2 %>% 
+    names(iris2 %>%
       t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor"),
              conf_int = FALSE)),
       c("statistic", "t_df", "p_value", "alternative")
   )
   expect_equal(
-    names(iris2 %>% 
+    names(iris2 %>%
             t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor"),
                    conf_int = TRUE)),
     c("statistic", "t_df", "p_value", "alternative", "lower_ci", "upper_ci")
   )
-  
-  ci_test <- iris2 %>% 
+
+  ci_test <- iris2 %>%
     t_test(Sepal.Width ~ Species, order = c("versicolor", "virginica"),
            conf_int = TRUE, conf_level = 0.9)
-  old_way <- t.test(formula = Sepal.Width ~ Species, 
+  old_way <- t.test(formula = Sepal.Width ~ Species,
                     data = iris2,
                     conf.level = 0.9)[["conf.int"]]
   expect_equal(ci_test$lower_ci[1], old_way[1])
   expect_equal(ci_test$upper_ci[1], old_way[2])
-  
+
   expect_error(
-    iris2 %>% 
+    iris2 %>%
       t_test(Petal.Width ~ Species, order = c("versicolor", "virginica"),
              conf_int = TRUE, conf_level = 1.1)
   )
-  
+
   # Check that var.equal produces different results
   # Thanks for finding this @EllaKaye!
   set.seed(2018)
   iris_small <- iris2 %>% sample_n(10)
-  no_var_equal <- iris_small %>% 
+  no_var_equal <- iris_small %>%
     t_stat(Petal.Width ~ Species, order = c("versicolor", "virginica"))
-  var_equal <- iris_small %>% 
+  var_equal <- iris_small %>%
     t_stat(Petal.Width ~ Species, order = c("versicolor", "virginica"),
            var.equal = TRUE)
   expect_false(
     no_var_equal == var_equal
   )
-  
-  shortcut_no_var_equal <- iris_small %>% 
-    specify(Petal.Width ~ Species) %>% 
+
+  shortcut_no_var_equal <- iris_small %>%
+    specify(Petal.Width ~ Species) %>%
     calculate(stat = "t", order = c("versicolor", "virginica"))
-  
-  shortcut_var_equal <- iris_small %>% 
-    specify(Petal.Width ~ Species) %>% 
+
+  shortcut_var_equal <- iris_small %>%
+    specify(Petal.Width ~ Species) %>%
     calculate(stat = "t", order = c("versicolor", "virginica"),
               var.equal = TRUE)
   expect_false(
     shortcut_no_var_equal == shortcut_var_equal
   )
-  
+
 })

From 6b18823fa07c0c35b4641f67bdff9591c756b6fb Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 12:05:16 +0300
Subject: [PATCH 03/78] Update spaces.

Details:
- Remove trailing spaces in comments (both raw and roxygen).
- Update spacing in function calls and definitions.
- Remove spaces after `!!` and `!!!` (also a tidyverse style guide).

Used {styler} (1.0.2.9000 from commit 1dd6b04, as it has some bugs fixed):
`styler::style_pkg(scope = "spaces", strict = FALSE, include_roxygen_examples = FALSE)`.
After that manually remove spaces around `/` in simple fractions in test files.
---
 R/calculate.R                     |  28 +++---
 R/conf_int.R                      |  26 +++---
 R/generate.R                      |  28 +++---
 R/hypothesize.R                   |  24 ++---
 R/infer.R                         |   6 +-
 R/p_value.R                       |  46 +++++-----
 R/print_methods.R                 |   2 +-
 R/rep_sample_n.R                  |  10 +-
 R/set_params.R                    |  44 ++++-----
 R/specify.R                       |  26 +++---
 R/utils.R                         | 102 ++++++++++----------
 R/visualize.R                     | 148 +++++++++++++++---------------
 R/wrappers.R                      |  44 ++++-----
 tests/testthat/test-calculate.R   |  18 ++--
 tests/testthat/test-hypothesize.R |   4 +-
 tests/testthat/test-visualize.R   |  34 +++----
 tests/testthat/test-wrappers.R    |   2 +-
 17 files changed, 296 insertions(+), 296 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 56147753..668b9680 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -1,5 +1,5 @@
 #' Calculate summary statistics
-#' 
+#'
 #' @param x The output from [generate()] for computation-based inference or the
 #'   output from [hypothesize()] piped in to here for theory-based inference.
 #' @param stat A string giving the type of the statistic to calculate. Current
@@ -12,7 +12,7 @@
 #'   difference in means, medians, or proportions and t and z statistics.
 #' @param ... To pass options like `na.rm = TRUE` into functions like
 #'   [mean()][base::mean()], [sd()][stats::sd()], etc.
-#'   
+#'
 #' @return A tibble containing a `stat` column of calculated statistics.
 #'
 #' @examples
@@ -23,7 +23,7 @@
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "diff in props", order = c("1", "0"))
-#' 
+#'
 #' @importFrom dplyr group_by summarize n
 #' @importFrom rlang !! sym quo enquo eval_tidy
 #' @export
@@ -78,7 +78,7 @@ calculate <- function(x,
         "a `generate()` step?"
       )
 
-    else if (!(stat %in% c("Chisq", "prop"))){
+    else if (!(stat %in% c("Chisq", "prop"))) {
       # From `hypothesize()` to `calculate()`
       # Catch-all if generate was not called
 #      warning_glue("You unexpectantly went from `hypothesize()` to ",
@@ -182,7 +182,7 @@ calc_impl.prop <- function(stat, x, order, ...) {
   x %>%
     dplyr::group_by(replicate) %>%
     dplyr::summarize(stat = mean(!!sym(col) == success,
-           #rlang::eval_tidy(col) == rlang::eval_tidy(success),
+           # rlang::eval_tidy(col) == rlang::eval_tidy(success),
                                  ...))
 }
 
@@ -224,7 +224,7 @@ calc_impl.diff_in_means <- function(stat, x, order, ...) {
 
 calc_impl.diff_in_medians <- function(stat, x, order, ...) {
   x %>%
-    dplyr::group_by(replicate,!!(attr(x, "explanatory"))) %>%
+    dplyr::group_by(replicate, !!(attr(x, "explanatory"))) %>%
     dplyr::summarize(xtilde =
                        stats::median(!!attr(x, "response"), ...)) %>%
     dplyr::group_by(replicate) %>%
@@ -301,7 +301,7 @@ calc_impl.diff_in_props <- function(stat, x, order, ...) {
   success <- attr(x, "success")
 
   x %>%
-    dplyr::group_by(replicate,!!attr(x, "explanatory")) %>%
+    dplyr::group_by(replicate, !!attr(x, "explanatory")) %>%
     dplyr::summarize(prop = mean(!!sym(col) == success, ...)) %>%
     dplyr::summarize(stat = prop[!!attr(x, "explanatory") == order[1]]
                      - prop[!!attr(x, "explanatory") == order[2]])
@@ -327,21 +327,21 @@ calc_impl.t <- function(stat, x, order, ...) {
   # else if ( (attr(x, "theory_type") == "Slope/correlation with t") &&
   #           stat == "slope"){
   #   explan_string <- as.character(attr(x, "explanatory"))
-  #   
+  #
   #   x %>%
   #     dplyr::summarize(stat = summary(stats::lm(
   #       !!attr(x, "response") ~ !!attr(x, "explanatory")
   #     ))[["coefficients"]][explan_string, "t value"])
   # }
-  # 
+  #
   # # Standardized correlation
   # else if ( (attr(x, "theory_type") == "Slope/correlation with t") &&
   #           stat == "correlation"){
-  # 
-  #   x %>% 
-  #     dplyr::summarize(corr = cor(!!attr(x, "explanatory"), 
+  #
+  #   x %>%
+  #     dplyr::summarize(corr = cor(!!attr(x, "explanatory"),
   #                                 !!attr(x, "response"))
-  #                      ) %>% 
+  #                      ) %>%
   #     dplyr::mutate(stat = corr * (sqrt(nrow(x) - 2)) / sqrt(1 - corr ^ 2))
   # }
 
@@ -397,7 +397,7 @@ calc_impl.z <- function(stat, x, order, ...) {
                      + p_hat * (1 - p_hat) / n2),
         stat = diff_prop / denom
       ) %>%
-      dplyr::select(-total_suc,-n1,-n2)
+      dplyr::select(-total_suc, -n1, -n2)
 
     df_out
 
diff --git a/R/conf_int.R b/R/conf_int.R
index 81bfffd2..887a58ad 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -1,8 +1,8 @@
 #' Compute confidence interval
-#' 
+#'
 #' Only simulation-based methods are (currently only) supported.
 #' `get_confidence_interval()` and `get_ci()` are both aliases of `conf_int()`.
-#' 
+#'
 #' @param x Data frame of calculated statistics or containing attributes of
 #'   theoretical distribution values. Currently, dependent on statistics being
 #'   stored in `stat` column as created in [calculate()] function.
@@ -16,7 +16,7 @@
 #'
 #' @return A 1 x 2 tibble with values corresponding to lower and upper values in
 #'   the confidence interval.
-#' 
+#'
 #' @examples
 #' mtcars_df <- mtcars %>%
 #'   dplyr::mutate(am = factor(am))
@@ -29,18 +29,18 @@
 #'   calculate(stat = "diff in means", order = c("1", "0"))
 #' bootstrap_distn %>% conf_int(level = 0.9)
 #' bootstrap_distn %>% conf_int(type = "se", point_estimate = d_hat)
-#' 
+#'
 #' @name get_ci
 NULL
 
 #' @rdname get_ci
 #' @export
 conf_int <- function(x, level = 0.95, type = "percentile",
-                     point_estimate = NULL){
+                     point_estimate = NULL) {
 
   check_ci_args(x, level, type, point_estimate)
 
-  if(type == "percentile") {
+  if (type == "percentile") {
     ci_vec <- stats::quantile(x[["stat"]],
                       probs = c((1 - level) / 2, level + (1 - level) / 2))
 
@@ -57,29 +57,29 @@ conf_int <- function(x, level = 0.95, type = "percentile",
   return(ci)
 }
 
-check_ci_args <- function(x, level, type, point_estimate){
+check_ci_args <- function(x, level, type, point_estimate) {
 
-  if(!is.null(point_estimate)){
-    if(!is.data.frame(point_estimate))
+  if (!is.null(point_estimate)) {
+    if (!is.data.frame(point_estimate))
       check_type(point_estimate, is.numeric)
     else
       check_type(point_estimate, is.data.frame)
   }
   check_type(x, is.data.frame)
   check_type(level, is.numeric)
-  if(level <= 0 || level >= 1){
+  if (level <= 0 || level >= 1) {
     stop_glue("The value of `level` must be between 0 and 1 non-inclusive.")
   }
 
-  if(!(type %in% c("percentile", "se"))){
+  if (!(type %in% c("percentile", "se"))) {
     stop_glue('The options for `type` are "percentile" or "se".')
   }
 
-  if(type == "se" && is.null(point_estimate))
+  if (type == "se" && is.null(point_estimate))
     stop_glue('A numeric value needs to be given for `point_estimate` ',
               'for `type = "se"')
 
-  if(type == "se" && is.vector(point_estimate))
+  if (type == "se" && is.vector(point_estimate))
     check_type(point_estimate, is.numeric)
 }
 
diff --git a/R/generate.R b/R/generate.R
index 029fa99f..2c86a47d 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -1,16 +1,16 @@
 #' Generate resamples, permutations, or simulations
-#' 
+#'
 #' Generation is done based on [specify()] and (if needed) [hypothesize()]
 #' inputs.
-#' 
+#'
 #' @param x A data frame that can be coerced into a [tibble][tibble::tibble].
 #' @param reps The number of resamples to generate.
 #' @param type Currently either `bootstrap`, `permute`, or `simulate`.
 #' @param ... Currently ignored.
-#' 
+#'
 #' @return A tibble containing `rep` generated datasets, indicated by the
 #'   `replicate` column.
-#' 
+#'
 #' @examples
 #' # Permutation test for two binary variables
 #' mtcars %>%
@@ -18,19 +18,19 @@
 #'   specify(am ~ vs, success = "1") %>%
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100, type = "permute")
-#' 
+#'
 #' @importFrom dplyr group_by
 #' @export
 generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
   auto_type <- attr(x, "type")
 
-  if(!is.null(auto_type)){
+  if (!is.null(auto_type)) {
     if (is.null(type)) {
       stop_glue("Supply not `NULL` value of `type`.")
     }
 
-    if(auto_type != type)
+    if (auto_type != type)
       stop_glue(
         "You have specified `type = \"{type}\"`, but `type` is expected to be ",
         "`\"{auto_type}\"`. Please try again with appropriate `type` value."
@@ -76,10 +76,10 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
 bootstrap <- function(x, reps = 1, ...) {
   # Check if hypothesis test chosen
-  if(!is.null(attr(x, "null"))){
+  if (!is.null(attr(x, "null"))) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
-    if(attr(attr(x, "params"), "names") == "mu"){
+    if (attr(attr(x, "params"), "names") == "mu") {
 
       col <- as.character(attr(x, "response"))
 #      if(attr(x, "theory_type") != "One sample t"){
@@ -91,14 +91,14 @@ bootstrap <- function(x, reps = 1, ...) {
       # Determining whether or not to implement this t transformation
       #####
 #      else {
-#        std_error <- stats::sd(x[[col]], na.rm = TRUE) / 
+#        std_error <- stats::sd(x[[col]], na.rm = TRUE) /
 #                        sqrt(length(x[[col]]))
 #        x[[col]] <- ( x[[col]] - mean(x[[col]], na.rm = TRUE) ) / std_error
 #      }
     }
 
     # Similarly for median
-    else if(attr(attr(x, "params"), "names") == "med"){
+    else if (attr(attr(x, "params"), "names") == "med") {
       col <- as.character(attr(x, "response"))
       x[[col]] <- x[[col]] -
         stats::median(x[[col]], na.rm = TRUE) + attr(x, "params")
@@ -144,7 +144,7 @@ permute_once <- function(x, ...) {
   dots <- list(...)
 
   if (attr(x, "null") == "independence") {
-    y <- pull(x, !! attr(x, "response"))
+    y <- pull(x, !!attr(x, "response"))
 
     y_prime <- sample(y, size = length(y), replace = FALSE)
     x[as.character(attr(x, "response"))] <- y_prime
@@ -157,7 +157,7 @@ permute_once <- function(x, ...) {
 #' @importFrom tibble tibble
 #' @importFrom rlang :=
 simulate <- function(x, reps = 1, ...) {
-  fct_levels <- as.character(unique(dplyr::pull(x, !! attr(x, "response"))))
+  fct_levels <- as.character(unique(dplyr::pull(x, !!attr(x, "response"))))
 
   col_simmed <- unlist(replicate(reps, sample(fct_levels,
                                               size = nrow(x),
@@ -166,7 +166,7 @@ simulate <- function(x, reps = 1, ...) {
                                  simplify = FALSE))
 
   rep_tbl <- tibble::tibble(
-    !! attr(x, "response") := as.factor(col_simmed),
+    !!attr(x, "response") := as.factor(col_simmed),
     replicate = as.factor(rep(1:reps, rep(nrow(x), reps)))
   )
 
diff --git a/R/hypothesize.R b/R/hypothesize.R
index 41baf587..62f8046f 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -1,13 +1,13 @@
 #' Declare a null hypothesis
-#' 
+#'
 #' @param x A data frame that can be coerced into a [tibble][tibble::tibble].
 #' @param null The null hypothesis. Options include `"independence"` and
 #'   `"point"`.
 #' @param ... Arguments passed to downstream functions.
-#' 
+#'
 #' @return A tibble containing the response (and explanatory, if specified)
 #'   variable data with parameter information stored as well.
-#' 
+#'
 #' @examples
 #' # Permutation test similar to ANOVA
 #' mtcars %>%
@@ -16,7 +16,7 @@
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "F")
-#' 
+#'
 #' @export
 hypothesize <- function(x, null, ...) {
 
@@ -26,21 +26,21 @@ hypothesize <- function(x, null, ...) {
 
   dots <- list(...)
 
-  if( (null == "point") && (length(dots) == 0) ){
+  if ((null == "point") && (length(dots) == 0)) {
     stop_glue("Provide a parameter and a value to check such as `mu = 30` ",
               "for the point hypothesis.")
   }
 
-  if((null == "independence") && (length(dots) > 0)) {
+  if ((null == "independence") && (length(dots) > 0)) {
     warning_glue("Parameter values are not specified when testing that two ",
                  "variables are independent.")
   }
 
-  if((length(dots) > 0) && (null == "point")) {
+  if ((length(dots) > 0) && (null == "point")) {
     params <- parse_params(dots, x)
     attr(x, "params") <- params
 
-    if(any(grepl("p.", attr(attr(x, "params"), "names")))){
+    if (any(grepl("p.", attr(attr(x, "params"), "names")))) {
      # simulate instead of bootstrap based on the value of `p` provided
       attr(x, "type") <- "simulate"
     } else {
@@ -49,13 +49,13 @@ hypothesize <- function(x, null, ...) {
 
   }
 
-  if(!is.null(null) && null == "independence")
+  if (!is.null(null) && null == "independence")
     attr(x, "type") <- "permute"
 
   # Check one proportion test set up correctly
-  if(null == "point"){
-    if(is.factor(response_variable(x))){
-      if(!any(grepl("p", attr(attr(x, "params"), "names"))))
+  if (null == "point") {
+    if (is.factor(response_variable(x))) {
+      if (!any(grepl("p", attr(attr(x, "params"), "names"))))
         stop_glue('Testing one categorical variable requires `p` ',
                   'to be used as a parameter.')
     }
diff --git a/R/infer.R b/R/infer.R
index af1163fa..679908d8 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -1,5 +1,5 @@
 #' infer: a grammar for statistical inference
-#' 
+#'
 #' The objective of this package is to perform statistical inference using a
 #' grammar that illustrates the underlying concepts and a format that coheres
 #' with the tidyverse.
@@ -7,14 +7,14 @@
 #' @examples
 #' # Example usage:
 #' library(infer)
-#' 
+#'
 #' @docType package
 #' @name infer
 NULL
 
 ## quiets concerns of R CMD check re: the .'s that appear in pipelines
 ## From Jenny Bryan's googlesheets package
-if(getRversion() >= "2.15.1")
+if (getRversion() >= "2.15.1")
   utils::globalVariables(c("prop", "stat", "xbar", "xtilde", "x", "..density..",
                            "statistic", ".", "parameter", "p.value",
                            "xmin", "xmax", "density", "denom",
diff --git a/R/p_value.R b/R/p_value.R
index 785471a5..8cceab14 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -1,8 +1,8 @@
 #' Compute p-value
-#' 
+#'
 #' Only simulation-based methods are (currently only) supported. `get_pvalue()`
 #' is an alias of `p_value`.
-#' 
+#'
 #' @param x Data frame of calculated statistics or containing attributes of
 #'   theoretical distribution values.
 #' @param obs_stat A numeric value or a 1x1 data frame (as extreme or more
@@ -11,7 +11,7 @@
 #'   `"two_sided"`. Can also specify `"left"`, `"right"`, or `"both"`.
 #'
 #' @return A 1x1 data frame with value between 0 and 1.
-#' 
+#'
 #' @examples
 #' mtcars_df <- mtcars %>%
 #'   dplyr::mutate(am = factor(am))
@@ -19,19 +19,19 @@
 #'   specify(mpg ~ am) %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
 #' null_distn <- mtcars_df %>%
-#'   specify(mpg ~ am) %>% 
+#'   specify(mpg ~ am) %>%
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100) %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
-#' null_distn %>% 
+#' null_distn %>%
 #'   p_value(obs_stat = d_hat, direction = "right")
-#' 
+#'
 #' @name get_pvalue
 NULL
 
 #' @rdname get_pvalue
 #' @export
-p_value <- function(x, obs_stat, direction){
+p_value <- function(x, obs_stat, direction) {
 
   check_type(x, is.data.frame)
   obs_stat <- check_obs_stat(obs_stat)
@@ -40,7 +40,7 @@ p_value <- function(x, obs_stat, direction){
   is_simulation_based <- !is.null(attr(x, "generate")) &&
     attr(x, "generate")
 
-  if(is_simulation_based)
+  if (is_simulation_based)
     pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
                                         direction = direction)
 
@@ -52,35 +52,35 @@ p_value <- function(x, obs_stat, direction){
 
   # if(!("stat" %in% names(x))){
   #    # Theoretical distribution
-  #  which_distribution(x, 
+  #  which_distribution(x,
   #                     theory_type <- attr(x, "theory_type"),
   #                     obs_stat = obs_stat,
-  #                     direction = direction) 
+  #                     direction = direction)
   # }
 
   return(pvalue)
 }
 
-simulation_based_p_value <- function(x, obs_stat, direction){
+simulation_based_p_value <- function(x, obs_stat, direction) {
 
-  if(direction %in% c("less", "left")){
+  if (direction %in% c("less", "left")) {
    p_value <- x %>%
       dplyr::summarize(p_value = mean(stat <= obs_stat))
   }
-  else if(direction %in% c("greater", "right")){
+  else if (direction %in% c("greater", "right")) {
    p_value <- x %>%
       dplyr::summarize(p_value = mean(stat >= obs_stat))
   }
-  else{
+  else {
     p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
   }
 
   p_value
 }
 
-two_sided_p_value <- function(x, obs_stat){
+two_sided_p_value <- function(x, obs_stat) {
 
-  if(stats::median(x$stat) >= obs_stat){
+  if (stats::median(x$stat) >= obs_stat) {
     basic_p_value <- get_percentile(x$stat, obs_stat) +
       (1 - get_percentile(x$stat, stats::median(x$stat) +
                        stats::median(x$stat) - obs_stat))
@@ -90,7 +90,7 @@ two_sided_p_value <- function(x, obs_stat){
                             stats::median(x$stat) - obs_stat))
   }
 
-  if(basic_p_value >= 1)
+  if (basic_p_value >= 1)
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
     # scenario instead of using >=
@@ -104,26 +104,26 @@ two_sided_p_value <- function(x, obs_stat){
 get_pvalue <- p_value
 
 # which_distribution <- function(x, theory_type, obs_stat, direction){
-#   
+#
 #   param <- attr(x, "distr_param")
 #   if(!is.null(attr(x, "distr_param2")))
 #     param2 <- attr(x, "distr_param2")
-#   
+#
 #   if(theory_type == "Two sample t")
 #     return(pt(q = obs_stat,
 #                              df = param,
 #                              lower.tail = set_lower_tail(direction))
 #          )
-# 
+#
 # }
 
-#theory_t_pvalue <- 
+# theory_t_pvalue <-
 
 # set_lower_tail <- function(direction){
 #   if(direction %in% c("greater", "right"))
 #     lower_tail <- FALSE
 #   else
 #     lower_tail <- TRUE
-#   
+#
 #   lower_tail
-# }  
+# }
diff --git a/R/print_methods.R b/R/print_methods.R
index 9433f5d2..a1cf028c 100644
--- a/R/print_methods.R
+++ b/R/print_methods.R
@@ -3,7 +3,7 @@
 #' @param x An object of class `infer`, i.e. output from [specify()] or
 #'   [hypothesize()].
 #' @param ... Arguments passed to methods.
-#' 
+#'
 #' @export
 print.infer <- function(x, ...) {
   attrs <- names(attributes(x))
diff --git a/R/rep_sample_n.R b/R/rep_sample_n.R
index 20b80a1e..b9079387 100644
--- a/R/rep_sample_n.R
+++ b/R/rep_sample_n.R
@@ -9,10 +9,10 @@
 #' @param reps Number of samples of size n = `size` to take.
 #' @param prob A vector of probability weights for obtaining the elements of the
 #'   vector being sampled.
-#' 
+#'
 #' @return A tibble of size `rep` times `size` rows corresponding to `rep`
 #'   samples of size n = `size` from `tbl`.
-#' 
+#'
 #' @examples
 #' suppressPackageStartupMessages(library(dplyr))
 #' suppressPackageStartupMessages(library(ggplot2))
@@ -31,13 +31,13 @@
 #'   group_by(replicate) %>%
 #'   summarize(prop_hurricane = mean(status == "hurricane"))
 #' p_hats
-#' 
+#'
 #' # Plot sampling distribution
 #' ggplot(p_hats, aes(x = prop_hurricane)) +
 #'   geom_density() +
 #'   labs(x = "p_hat", y = "Number of samples",
 #'   title = "Sampling distribution of p_hat from 1000 samples of size 50")
-#' 
+#'
 #' @importFrom dplyr pull
 #' @importFrom dplyr inner_join
 #' @importFrom dplyr group_by
@@ -49,7 +49,7 @@ rep_sample_n <- function(tbl, size, replace = FALSE, reps = 1, prob = NULL) {
   check_type(size, is.numeric)
   check_type(replace, is.logical)
   check_type(reps, is.numeric)
-  if(!is.null(prob))
+  if (!is.null(prob))
     check_type(prob, is.numeric)
 
   # assign non-uniform probabilities
diff --git a/R/set_params.R b/R/set_params.R
index 13d9fc32..77717f74 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -1,23 +1,23 @@
 #' To determine which theoretical distribution to fit (if any)
-#' 
+#'
 #' @param x A data frame that can be coerced into a [tibble][tibble::tibble].
-#' 
+#'
 #' @noRd
-set_params <- function(x){
+set_params <- function(x) {
 
   attr(x, "theory_type") <- NULL
 
-  if(!is.null(attr(x, "response"))){
+  if (!is.null(attr(x, "response"))) {
     num_response_levels <- length(levels(response_variable(x)))
   }
 
   # One variable
   if (!is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
       !is.null(attr(x, "response_type")) &&
-      is.null(attr(x, "explanatory_type"))){
+      is.null(attr(x, "explanatory_type"))) {
 
     # One mean
-    if(attr(x, "response_type") %in% c("integer", "numeric")){
+    if (attr(x, "response_type") %in% c("integer", "numeric")) {
       attr(x, "theory_type") <- "One sample t"
       attr(x, "distr_param") <- x %>%
         dplyr::summarize(df = stats::t.test(
@@ -28,7 +28,7 @@ set_params <- function(x){
     }
 
     # One prop
-    else if(attr(x, "response_type") == "factor" && (num_response_levels == 2)){
+    else if (attr(x, "response_type") == "factor" && (num_response_levels == 2)) {
 
       # No parameters since standard normal
       attr(x, "theory_type") <- "One sample prop z"
@@ -45,18 +45,18 @@ set_params <- function(x){
   # Two variables
   if (!is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
       !is.null(attr(x, "response_type")) &&
-      !is.null(attr(x, "explanatory_type"))){
+      !is.null(attr(x, "explanatory_type"))) {
 
     attr(x, "type") <- "bootstrap"
 
     # Response is numeric, explanatory is categorical
-    if(attr(x, "response_type") %in% c("integer", "numeric") &
-       attr(x, "explanatory_type") == "factor"){
+    if (attr(x, "response_type") %in% c("integer", "numeric") &
+       attr(x, "explanatory_type") == "factor") {
 
       # Two sample means (t distribution)
-      if(length(levels(explanatory_variable(x))) == 2) {
+      if (length(levels(explanatory_variable(x))) == 2) {
         attr(x, "theory_type") <- "Two sample t"
-        # Keep track of Satterthwaite degrees of freedom since lost when 
+        # Keep track of Satterthwaite degrees of freedom since lost when
         # in aggregation w/ calculate()/generate()
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = stats::t.test(
@@ -69,31 +69,31 @@ set_params <- function(x){
         # Get numerator and denominator degrees of freedom
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df1 = stats::anova(stats::aov(
-              !! attr(x, "response") ~ !! attr(x, "explanatory")))$Df[1]
+              !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[1]
             ) %>%
           dplyr::pull()
         attr(x, "distr_param2") <- x %>%
           dplyr::summarize(df2 = stats::anova(stats::aov(
-              !! attr(x, "response") ~ !! attr(x, "explanatory")))$Df[2]
+              !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[2]
             ) %>%
           dplyr::pull()
       }
     }
 
     # Response is categorical, explanatory is categorical
-    if(attr(x, "response_type") == "factor" &
-       attr(x, "explanatory_type") == "factor"){
+    if (attr(x, "response_type") == "factor" &
+       attr(x, "explanatory_type") == "factor") {
 
       attr(x, "type") <- "bootstrap"
 
-      # Two sample proportions (z distribution) 
+      # Two sample proportions (z distribution)
       # Parameter(s) not needed since standard normal
-      if(length(levels(response_variable(x))) == 2 &
-         length(levels(explanatory_variable(x))) == 2){
+      if (length(levels(response_variable(x))) == 2 &
+         length(levels(explanatory_variable(x))) == 2) {
         attr(x, "theory_type") <- "Two sample props z"
       }
       # >2 sample proportions (chi-square test of indep)
-      else{
+      else {
         attr(x, "theory_type") <- "Chi-square test of indep"
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = suppressWarnings(stats::chisq.test(
@@ -104,8 +104,8 @@ set_params <- function(x){
     }
 
     # Response is numeric, explanatory is numeric
-    if(attr(x, "response_type") %in% c("integer", "numeric") &
-       attr(x, "explanatory_type") %in% c("integer", "numeric")){
+    if (attr(x, "response_type") %in% c("integer", "numeric") &
+       attr(x, "explanatory_type") %in% c("integer", "numeric")) {
       response_string <- as.character(attr(x, "response"))
       explanatory_string <- as.character(attr(x, "explanatory"))
     attr(x, "theory_type") <- "Slope/correlation with t"
diff --git a/R/specify.R b/R/specify.R
index f3779839..c3a3ab6b 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -1,7 +1,7 @@
 #' Specify the response and explanatory variables
-#' 
+#'
 #' `specify()` also converts character variables chosen to be `factor`s.
-#' 
+#'
 #' @param x A data frame that can be coerced into a [tibble][tibble::tibble].
 #' @param formula A formula with the response variable on the left and the
 #'   explanatory on the right.
@@ -12,10 +12,10 @@
 #' @param success The level of `response` that will be considered a success, as
 #'   a string. Needed for inference on one proportion, a difference in
 #'   proportions, and corresponding z stats.
-#' 
+#'
 #' @return A tibble containing the response (and explanatory, if specified)
 #'   variable data.
-#' 
+#'
 #' @examples
 #' # Permutation test similar to ANOVA
 #' mtcars %>%
@@ -24,7 +24,7 @@
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "F")
-#' 
+#'
 #' @importFrom rlang f_lhs
 #' @importFrom rlang f_rhs
 #' @importFrom dplyr mutate_if select one_of
@@ -39,7 +39,7 @@ specify <- function(x, formula, response = NULL,
     mutate_if(is.character, as.factor) %>%
     mutate_if(is.logical, as.factor)
 
-  if ((!methods::hasArg(formula) && !methods::hasArg(response))){
+  if ((!methods::hasArg(formula) && !methods::hasArg(response))) {
     stop_glue("Please give the `response` variable.")
   }
   if (methods::hasArg(formula)) {
@@ -68,18 +68,18 @@ specify <- function(x, formula, response = NULL,
   response_col <- rlang::eval_tidy(attr(x, "response"), x)
 
   # if there's an explanatory var
-  if(has_explanatory(x)) {
-    if(!as.character(attr(x, "explanatory")) %in% names(x)) {
+  if (has_explanatory(x)) {
+    if (!as.character(attr(x, "explanatory")) %in% names(x)) {
       stop_glue('The explanatory variable `{attr(x, "explanatory")}` ',
                 'cannot be found in this dataframe.')
     }
-    if(identical(as.character(attr(x, "response")),
+    if (identical(as.character(attr(x, "response")),
                   as.character(attr(x, "explanatory")))) {
       stop_glue("The response and explanatory variables must be different ",
                 "from one another.")
     }
     explanatory_col <- rlang::eval_tidy(attr(x, "explanatory"), x)
-    if(is.character(explanatory_col)) {
+    if (is.character(explanatory_col)) {
       explanatory_col <- as.factor(explanatory_col)
     }
   }
@@ -119,16 +119,16 @@ specify <- function(x, formula, response = NULL,
   # To help determine theoretical distribution to plot
   attr(x, "response_type") <- class(response_variable(x))
 
-  if(is.null(attr(x, "explanatory")))
+  if (is.null(attr(x, "explanatory")))
     attr(x, "explanatory_type") <- NULL
   else
     attr(x, "explanatory_type") <- class(explanatory_variable(x))
 
-  if(attr(x, "response_type") == "factor" && is.null(success) &&
+  if (attr(x, "response_type") == "factor" && is.null(success) &&
      length(levels(response_variable(x))) == 2 &&
      (is.null(attr(x, "explanatory_type")) ||
      (!is.null(attr(x, "explanatory_type")) &&
-     length(levels(explanatory_variable(x))) == 2)) )
+     length(levels(explanatory_variable(x))) == 2)))
     stop_glue(
       'A level of the response variable `{attr(x, "response")}` ',
       'needs to be specified for the `success` argument in `specify()`.'
diff --git a/R/utils.R b/R/utils.R
index 7a740919..feb18799 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,6 +1,6 @@
 format_params <- function(x) {
   par_levels <- get_par_levels(x)
-  fct_levels <- as.character(unique(dplyr::pull(x, !! attr(x, "response"))))
+  fct_levels <- as.character(unique(dplyr::pull(x, !!attr(x, "response"))))
   return(attr(x, "params")[match(fct_levels, par_levels)])
 }
 
@@ -9,7 +9,7 @@ get_par_levels <- function(x) {
   return(gsub("^.\\.", "", par_names))
 }
 
-set_attributes <- function(to, from = x){
+set_attributes <- function(to, from = x) {
   attr(to, "response") <- attr(from, "response")
   attr(to, "success") <- attr(from, "success")
   attr(to, "explanatory") <- attr(from, "explanatory")
@@ -34,18 +34,18 @@ response_variable <- function(x) {
   x[[as.character(attr(x, "response"))]]
 }
 
-reorder_explanatory <- function(x, order){
+reorder_explanatory <- function(x, order) {
   x[[as.character(attr(x, "explanatory"))]] <-
     factor(x[[as.character(attr(x, "explanatory"))]],
            levels = c(order[1], order[2]))
   x
 }
 
-has_explanatory <- function(x){
+has_explanatory <- function(x) {
   !is.null(attr(x, "explanatory"))
 }
 
-has_response <- function(x){
+has_response <- function(x) {
   !is.null(attr(x, "response"))
 }
 
@@ -89,39 +89,39 @@ null_transformer <- function(text, envir) {
   out
 }
 
-check_order <- function(x, explanatory_variable, order){
+check_order <- function(x, explanatory_variable, order) {
   unique_explanatory_variable <- unique(explanatory_variable)
-  if (length(unique_explanatory_variable) != 2){
+  if (length(unique_explanatory_variable) != 2) {
     stop_glue("Statistic is based on a difference; the explanatory variable ",
               "should have two levels.")
   }
-  if(is.null(order)){
+  if (is.null(order)) {
     stop_glue("Statistic is based on a difference; specify the `order` in ",
               "which to subtract the levels of the explanatory variable. ",
               '`order = c("first", "second")` means `("first" - "second")`. ',
               "Check `?calculate` for details.")
   } else {
-    if(xor(is.na(order[1]), is.na(order[2])))
+    if (xor(is.na(order[1]), is.na(order[2])))
       stop_glue(
         "Only one level specified in `order`. Both levels need to be specified."
       )
-    if(length(order) > 2)
+    if (length(order) > 2)
       stop_glue("`order` is expecting only two entries.")
-    if(order[1] %in% unique_explanatory_variable == FALSE)
+    if (order[1] %in% unique_explanatory_variable == FALSE)
       stop_glue("{order[1]} is not a level of the explanatory variable.")
-    if(order[2] %in% unique_explanatory_variable == FALSE)
+    if (order[2] %in% unique_explanatory_variable == FALSE)
       stop_glue("{order[2]} is not a level of the explanatory variable.")
   }
 }
 
 check_args_and_attr <- function(x, explanatory_variable, response_variable,
-                                stat){
+                                stat) {
 
   # Could also do `stat <- match.arg(stat)`
   # but that's not as helpful to beginners with the cryptic error msg
   if (!stat %in% c("mean", "median", "sd", "prop",
                    "diff in means", "diff in medians", "diff in props",
-                   "Chisq", "F", "slope", "correlation", "t", "z")){
+                   "Chisq", "F", "slope", "correlation", "t", "z")) {
     stop_glue("You specified a string for `stat` that is not implemented. ",
               "Check your spelling and `?calculate` for current options.")
   }
@@ -130,8 +130,8 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
     warning_glue('A `generate()` step was not performed prior to ',
                  '`calculate()`. Review carefully.')
 
-  if (stat %in% c("F", "slope", "diff in means", "diff in medians")){
-    if (has_explanatory(x) && !is.numeric(response_variable(x))){
+  if (stat %in% c("F", "slope", "diff in means", "diff in medians")) {
+    if (has_explanatory(x) && !is.numeric(response_variable(x))) {
       stop_glue(
         'The response variable of `{attr(x, "response")}` is not appropriate\n',
         "since '{stat}' is expecting the response variable to be numeric."
@@ -139,8 +139,8 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
     }
   }
 
-  if (stat %in% c("diff in props", "Chisq")){
-    if (has_explanatory(x) && !is.factor(response_variable(x))){
+  if (stat %in% c("diff in props", "Chisq")) {
+    if (has_explanatory(x) && !is.factor(response_variable(x))) {
       stop_glue(
         'The response variable of `{attr(x, "response")}` is not appropriate\n',
         "since '{stat}' is expecting the response variable to be a factor."
@@ -149,11 +149,11 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
   }
 }
 
-check_for_numeric_stat <- function(x, stat){
-  if (stat %in% c("mean", "median", "sd")){
+check_for_numeric_stat <- function(x, stat) {
+  if (stat %in% c("mean", "median", "sd")) {
     col <- base::setdiff(names(x), "replicate")
 
-    if (!is.numeric(x[[as.character(col)]])){
+    if (!is.numeric(x[[as.character(col)]])) {
       stop_glue(
         "Calculating a {stat} here is not appropriate\n",
         "since the `{col}` variable is not numeric."
@@ -162,10 +162,10 @@ check_for_numeric_stat <- function(x, stat){
   }
 }
 
-check_for_factor_stat <- function(x, stat, explanatory_variable){
+check_for_factor_stat <- function(x, stat, explanatory_variable) {
 
-  if (stat %in% c("diff in means", "diff in medians", "diff in props", "F")){
-    if (!is.factor(explanatory_variable)){
+  if (stat %in% c("diff in means", "diff in medians", "diff in props", "F")) {
+    if (!is.factor(explanatory_variable)) {
       stop_glue(
         'The explanatory variable of `{attr(x, "explanatory")}` is not ',
         "appropriate\n",
@@ -175,24 +175,24 @@ check_for_factor_stat <- function(x, stat, explanatory_variable){
   }
 }
 
-check_point_params <- function(x, stat){
+check_point_params <- function(x, stat) {
 
   param_names <- attr(attr(x, "params"), "names")
   hyp_text <- 'to be set in `hypothesize()`.'
-  if(!is.null(attr(x, "null"))){
-    if(stat %in% c("mean", "median", "sd", "prop")){
-      if( (stat == "mean" && !("mu" %in% param_names)) )
+  if (!is.null(attr(x, "null"))) {
+    if (stat %in% c("mean", "median", "sd", "prop")) {
+      if ((stat == "mean" && !("mu" %in% param_names)))
         stop_glue('`stat == "mean"` requires `"mu"` {hyp_text}')
-      if ( (!(stat == "mean") && ("mu" %in% param_names)) )
+      if ((!(stat == "mean") && ("mu" %in% param_names)))
         stop_glue('`"mu"` does not correspond to `stat = "{stat}"`.')
-      if( (stat == "median" && !("med" %in% param_names) ) )
+      if ((stat == "median" && !("med" %in% param_names)))
         stop_glue('`stat == "median"` requires `"med"` {hyp_text}')
-      if ( (!(stat == "median") && ("med" %in% param_names)) )
+      if ((!(stat == "median") && ("med" %in% param_names)))
         stop_glue('`"med"` does not correspond to `stat = "{stat}"`.')
       ## Tests unable to get to
       # if( (stat == "sigma" && !("sd" %in% param_names)) )
       #   stop_glue('`stat == "sd"` requires `"sigma"` {hyp_text}')
-      if ( (!(stat == "sd") && ("sigma" %in% param_names)) )
+      if ((!(stat == "sd") && ("sigma" %in% param_names)))
         stop_glue('`"sigma"` does not correspond to `stat = "{stat}"`.')
 
       ## Tests unable to get to
@@ -209,8 +209,8 @@ parse_params <- function(dots, x) {
   sig_ind <- grep("sigma", names(dots))
 
   # error: cannot specify more than one of props, means, medians, or sds
-  if ( length(p_ind) + length(mu_ind) + length(med_ind)
-       + length(sig_ind) != 1 ){
+  if (length(p_ind) + length(mu_ind) + length(med_ind)
+       + length(sig_ind) != 1) {
     stop_glue(
       'Parameter values can be only one of `p`, `mu`, `med`, or `sigma`.'
     )
@@ -226,7 +226,7 @@ parse_params <- function(dots, x) {
         stop_glue("A point null regarding a proportion requires ",
                   "that `success` be indicated in `specify()`.")
       }
-      if(dots$p < 0 || dots$p > 1)
+      if (dots$p < 0 || dots$p > 1)
         stop_glue(
           "The value suggested for `p` is not between 0 and 1, inclusive."
         )
@@ -235,7 +235,7 @@ parse_params <- function(dots, x) {
       dots$p <- append(dots$p, 1 - dots$p)
       names(dots$p) <- c(attr(x, "success"), missing_lev)
     } else {
-      if(sum(dots$p) != 1){
+      if (sum(dots$p) != 1) {
         stop_glue("Make sure the hypothesized values for the `p` parameters ",
                   "sum to 1. Please try again.")
       }
@@ -250,7 +250,7 @@ parse_params <- function(dots, x) {
   return(unlist(dots))
 }
 
-hypothesize_checks <- function(x, null){
+hypothesize_checks <- function(x, null) {
   # error: x is not a dataframe
   if (!sum(class(x) %in% c("data.frame", "tbl", "tbl_df", "grouped_df"))) {
     stop_glue("x must be a data.frame or tibble")
@@ -268,13 +268,13 @@ hypothesize_checks <- function(x, null){
   #              'for `null` argument.')
   #  }
 
-  if(!has_response(x)){
+  if (!has_response(x)) {
     stop_glue(
       "The response variable is not set. Make sure to `specify()` it first."
     )
   }
 
-  if(null == "independence" && !has_explanatory(x)){
+  if (null == "independence" && !has_explanatory(x)) {
     stop_glue('Please `specify()` an explanatory and a response variable ',
               'when testing\n',
               'a null hypothesis of `"independence"`.')
@@ -282,22 +282,22 @@ hypothesize_checks <- function(x, null){
 }
 
 check_direction <- function(direction = c("less", "greater", "two_sided",
-                                          "left", "right", "both")){
+                                          "left", "right", "both")) {
   check_type(direction, is.character)
 
-  if(!(direction %in% c("less", "greater", "two_sided",
-                        "left", "right", "both"))){
+  if (!(direction %in% c("less", "greater", "two_sided",
+                        "left", "right", "both"))) {
     stop_glue('The provided value for `direction` is not appropriate. ',
               'Possible values are "less", "greater", "two_sided", ',
               '"left", "right", or "both".')
   }
 }
 
-check_obs_stat <- function(obs_stat){
-  if(!is.null(obs_stat)){
-    if("data.frame" %in% class(obs_stat)){
+check_obs_stat <- function(obs_stat) {
+  if (!is.null(obs_stat)) {
+    if ("data.frame" %in% class(obs_stat)) {
       check_type(obs_stat, is.data.frame)
-      if( (nrow(obs_stat) != 1) || (ncol(obs_stat) != 1) )
+      if ((nrow(obs_stat) != 1) || (ncol(obs_stat) != 1))
         warning_glue("The first row and first column value of the given ",
                      "`obs_stat` will be used.")
 
@@ -305,7 +305,7 @@ check_obs_stat <- function(obs_stat){
       obs_stat <- obs_stat[[1]][[1]]
       check_type(obs_stat, is.numeric)
     }
-    else{
+    else {
       check_type(obs_stat, is.numeric)
     }
   }
@@ -314,9 +314,9 @@ check_obs_stat <- function(obs_stat){
 }
 
 #' Check object type
-#' 
+#'
 #' Throw an error in case object is not of desired type.
-#' 
+#'
 #' @param x An object to check.
 #' @param predicate A function to perform check. A good idea is to use function
 #'   named `is.*()` or `is_*()` with possible `<package>::` prefix.
@@ -324,7 +324,7 @@ check_obs_stat <- function(obs_stat){
 #'   original name of supplied `predicate`: all alphanumeric with '_' and '.'
 #'   characters (until the name end) after the first appearance of either `is.`
 #'   or `is_`. In case of a doubt supply `type` explicitly.
-#' 
+#'
 #' @examples
 #' \dontrun{
 #' x <- 1
@@ -332,7 +332,7 @@ check_obs_stat <- function(obs_stat){
 #' check_type(x, is.logical)
 #' check_type(x, rlang::is_string, "character of length 1")
 #' }
-#' 
+#'
 #' @keywords internal
 #' @noRd
 check_type <- function(x, predicate, type = NULL) {
diff --git a/R/visualize.R b/R/visualize.R
index 7129b050..6cd293c7 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -1,8 +1,8 @@
 #' Visualize statistical inference
-#' 
+#'
 #' Visualize the distribution of the simulation-based inferential statistics or
 #' the theoretical distribution (or both!).
-#' 
+#'
 #' @param data The output from [calculate()].
 #' @param bins The number of bins in the histogram.
 #' @param method A string giving the method to display. Options are
@@ -29,12 +29,12 @@
 #' @param ci_fill A character or hex string specifying the color to shade the
 #'   confidence interval.
 #' @param ... Other arguments passed along to ggplot2.
-#' 
+#'
 #' @return A ggplot object showing the simulation-based distribution as a
 #'   histogram or bar graph. Also used to show the theoretical curves.
-#' 
+#'
 #' @examples
-#' # Permutations to create a simulation-based null distribution for 
+#' # Permutations to create a simulation-based null distribution for
 #' # one numerical response and one categorical predictor
 #' # using t statistic
 #' mtcars %>%
@@ -44,8 +44,8 @@
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "t", order = c("1", "0")) %>%
 #'   visualize(method = "simulation") #default method
-#' 
-#' # Theoretical t distribution for 
+#'
+#' # Theoretical t distribution for
 #' # one numerical response and one categorical predictor
 #' # using t statistic
 #' mtcars %>%
@@ -55,7 +55,7 @@
 #'   # generate() is not needed since we are not doing simulation
 #'   calculate(stat = "t", order = c("1", "0")) %>%
 #'   visualize(method = "theoretical")
-#' 
+#'
 #' # Overlay theoretical distribution on top of randomized t-statistics
 #' mtcars %>%
 #'   dplyr::mutate(am = factor(am)) %>%
@@ -64,7 +64,7 @@
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "t", order = c("1", "0")) %>%
 #'   visualize(method = "both")
-#' 
+#'
 #' @importFrom ggplot2 ggplot geom_histogram aes stat_function ggtitle
 #' @importFrom ggplot2 xlab ylab geom_vline geom_rect geom_bar
 #' @importFrom stats dt qt df qf dnorm qnorm dchisq qchisq
@@ -85,32 +85,32 @@ visualize <- function(data, bins = 15, method = "simulation",
   check_type(dens_color, is.character)
   check_type(obs_stat_color, is.character)
   check_type(pvalue_fill, is.character)
-  if(!is.null(direction))
+  if (!is.null(direction))
     check_type(direction, is.character)
-  if(is.data.frame(endpoints) &&
-     ( (nrow(endpoints) != 1) || (ncol(endpoints) != 2) ) ){
+  if (is.data.frame(endpoints) &&
+     ((nrow(endpoints) != 1) || (ncol(endpoints) != 2))) {
     stop_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
     )
   }
-  if(is.vector(endpoints) && ( length(endpoints) != 2) ) {
+  if (is.vector(endpoints) && (length(endpoints) != 2)) {
     warning_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector. ",
       "Using the first two entries as the `endpoints`."
     )
     endpoints <- endpoints[1:2]
   }
-  if(is.data.frame(endpoints))
+  if (is.data.frame(endpoints))
     endpoints <- unlist(endpoints)
   obs_stat <- check_obs_stat(obs_stat)
-  if(!is.null(direction) &&
+  if (!is.null(direction) &&
      (is.null(obs_stat) + is.null(endpoints)) != 1)
     stop_glue(
       "Shading requires either `endpoints` values for a confidence interval ",
       "or the observed statistic `obs_stat` to be provided."
     )
 
-  if(method == "simulation"){
+  if (method == "simulation") {
 
     infer_plot <- visualize_simulation(data = data, bins = bins,
                                        dens_color = dens_color,
@@ -122,7 +122,7 @@ visualize <- function(data, bins = 15, method = "simulation",
                                        ci_fill = ci_fill,
                                        ...)
 
-  } else if(method == "theoretical"){
+  } else if (method == "theoretical") {
 
     infer_plot <- visualize_theoretical(data = data,
                                         dens_color = dens_color,
@@ -135,13 +135,13 @@ visualize <- function(data, bins = 15, method = "simulation",
                                         ...)
 
 
-  } else if(method == "both"){
+  } else if (method == "both") {
 
-    if(!("stat" %in% names(data)))
+    if (!("stat" %in% names(data)))
       stop_glue('`generate()` and `calculate()` are both required ',
                 'to be done prior to `visualize(method = "both")`')
 
-    if(("replicate" %in% names(data)) &&
+    if (("replicate" %in% names(data)) &&
          length(unique(data$replicate)) < 100)
       warning_glue(
         "With only {length(unique(data$stat))} replicates, it may be ",
@@ -163,13 +163,13 @@ visualize <- function(data, bins = 15, method = "simulation",
               '`"simulation"` is the default.')
   }
 
-  if(!is.null(obs_stat)){#&& !is.null(direction)
+  if (!is.null(obs_stat)) { # && !is.null(direction)
     infer_plot <- infer_plot +
       geom_vline(xintercept = obs_stat, size = 2, color = obs_stat_color, ...)
   }
 
-  if(!is.null(endpoints)){
-    if(!is.null(obs_stat))
+  if (!is.null(endpoints)) {
+    if (!is.null(obs_stat))
       warning_glue("Values for both `endpoints` and `obs_stat` were given ",
                    "when only one should be set. Ignoring `obs_stat` values.")
     infer_plot <- infer_plot +
@@ -183,7 +183,7 @@ visualize <- function(data, bins = 15, method = "simulation",
 
 
 theory_t_plot <- function(deg_freedom, statistic_text = "t",
-                          dens_color = dens_color, ...){
+                          dens_color = dens_color, ...) {
   ggplot(data.frame(x = c(qt(0.001, deg_freedom),
                           qt(0.999, deg_freedom)))) +
     stat_function(mapping = aes(x), fun = dt, args = list(df = deg_freedom),
@@ -200,7 +200,7 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
                         bins,
                         pvalue_fill,
                         endpoints,
-                        ci_fill, ...){
+                        ci_fill, ...) {
 
   infer_t_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
@@ -222,7 +222,7 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
 
 theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom,
                               statistic_text = "F",
-                              dens_color = dens_color, ...){
+                              dens_color = dens_color, ...) {
   ggplot(data.frame(x = c(qf(0.001, deg_freedom_top, deg_freedom_bottom),
                           qf(0.999, deg_freedom_top, deg_freedom_bottom)))) +
     stat_function(mapping = aes(x), fun = df,
@@ -242,9 +242,9 @@ both_anova_plot <- function(data, deg_freedom_top,
                             endpoints,
                             pvalue_fill,
                             ci_fill,
-                            ...){
+                            ...) {
 
-  if(!is.null(direction) && !(direction %in% c("greater", "right")))
+  if (!is.null(direction) && !(direction %in% c("greater", "right")))
     warning_glue(
       "F usually corresponds to right-tailed tests. Proceed with caution."
     )
@@ -268,7 +268,7 @@ both_anova_plot <- function(data, deg_freedom_top,
     ylab("")
 }
 
-theory_z_plot <- function(statistic_text = "z", dens_color = dens_color,  ...){
+theory_z_plot <- function(statistic_text = "z", dens_color = dens_color,  ...) {
 
   ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) +
     stat_function(mapping = aes(x), fun = dnorm, color = dens_color) +
@@ -285,7 +285,7 @@ both_z_plot <- function(data, statistic_text = "z",
                         bins,
                         endpoints,
                         ci_fill,
-                        ...){
+                        ...) {
 
   infer_z_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
@@ -306,7 +306,7 @@ both_z_plot <- function(data, statistic_text = "z",
 
 theory_chisq_plot <- function(deg_freedom,
                               statistic_text = "Chi-Square",
-                              dens_color = dens_color, ...){
+                              dens_color = dens_color, ...) {
   ggplot(data.frame(x = c(qchisq(0.001, deg_freedom),
                           qchisq(0.999, deg_freedom)))) +
     stat_function(mapping = aes(x), fun = dchisq,
@@ -325,9 +325,9 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                             endpoints,
                             pvalue_fill = pvalue_fill,
                             ci_fill = ci_fill,
-                            ...){
+                            ...) {
 
-  if(!is.null(direction) && !(direction %in% c("greater", "right")))
+  if (!is.null(direction) && !(direction %in% c("greater", "right")))
     warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                  "Proceed with caution.")
 
@@ -359,21 +359,21 @@ shade_density_check <- function(data,
                                 endpoints,
                                 ci_fill, ...) {
 
-  if(is.null(direction) || is.null(obs_stat)){
-    if(density){
+  if (is.null(direction) || is.null(obs_stat)) {
+    if (density) {
       gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
         geom_histogram(bins = bins, color = "white",
                        mapping = aes(y = ..density..), ...)
-    } #else {
+    } # else {
       # Not sure if needed? Can't get tests to find it
-      #gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
+      # gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
       #  geom_histogram(bins = bins, color = "white", ...)
-    #}
+    # }
   }
 
-  if(xor(!is.null(obs_stat), !is.null(endpoints))){
-    if(!is.null(direction)){
-      if(density){
+  if (xor(!is.null(obs_stat), !is.null(endpoints))) {
+    if (!is.null(direction)) {
+      if (density) {
         gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
           geom_histogram(bins = bins, color = "white",
                          mapping = aes(y = ..density..), ...)
@@ -382,21 +382,21 @@ shade_density_check <- function(data,
           geom_histogram(bins = bins, color = "white", ...)
       }
 
-      if(direction %in% c("less", "left")){
+      if (direction %in% c("less", "left")) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
                     ...)
       }
-      if(direction %in% c("greater", "right")){
+      if (direction %in% c("greater", "right")) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
                     ...)
       }
 
-      if(direction %in% c("two_sided", "both") &&
-         obs_stat >= stats::median(data$stat)){
+      if (direction %in% c("two_sided", "both") &&
+         obs_stat >= stats::median(data$stat)) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0,
@@ -412,8 +412,8 @@ shade_density_check <- function(data,
           )
       }
 
-      if(direction %in% c("two_sided", "both") &&
-         obs_stat < stats::median(data$stat)){
+      if (direction %in% c("two_sided", "both") &&
+         obs_stat < stats::median(data$stat)) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0,
@@ -429,7 +429,7 @@ shade_density_check <- function(data,
     }
 
 
-    if(direction == "between"){
+    if (direction == "between") {
       gg_plot <- gg_plot +
         geom_rect(fill = ci_fill, alpha = 0.01,
                   aes(xmin = endpoints[1],
@@ -450,8 +450,8 @@ visualize_simulation <- function(data, bins,
                                  pvalue_fill,
                                  endpoints,
                                  ci_fill, ...) {
-  if(is.null(direction)){
-    if(length(unique(data$stat)) >= 10)
+  if (is.null(direction)) {
+    if (length(unique(data$stat)) >= 10)
       infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
         geom_histogram(bins = bins, color = "white", ...)
     else
@@ -487,23 +487,23 @@ visualize_theoretical <- function(data,
     "method. {{infer}} currently does not check these for you."
   )
 
-  if(!is.null(attr(data, "stat")) &&
+  if (!is.null(attr(data, "stat")) &&
      !(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
     warning_glue(
       "Your `calculate`d statistic and the theoretical distribution are on ",
       "different scales. Displaying only the theoretical distribution."
     )
 
-  if(attr(data, "theory_type") %in%
-     c("Two sample t", "Slope with t", "One sample t")){
+  if (attr(data, "theory_type") %in%
+     c("Two sample t", "Slope with t", "One sample t")) {
     infer_plot <- theory_t_plot(deg_freedom = attr(data, "distr_param"),
                                 statistic_text = "t",
                                 dens_color = dens_color)
   }
 
-  else if(attr(data, "theory_type") == "ANOVA"){
+  else if (attr(data, "theory_type") == "ANOVA") {
 
-    if(!is.null(direction) && !(direction %in% c("greater", "right")))
+    if (!is.null(direction) && !(direction %in% c("greater", "right")))
       warning_glue(
         "F usually corresponds to right-tailed tests. Proceed with caution."
       )
@@ -515,16 +515,16 @@ visualize_theoretical <- function(data,
       dens_color = dens_color)
   }
 
-  else if(attr(data, "theory_type") %in%
-          c("One sample prop z", "Two sample props z")){
+  else if (attr(data, "theory_type") %in%
+          c("One sample prop z", "Two sample props z")) {
     infer_plot <- theory_z_plot(statistic_text = "z",
                                 dens_color = dens_color)
   }
 
-  else if(attr(data, "theory_type") %in%
-          c("Chi-square test of indep", "Chi-square Goodness of Fit")){
+  else if (attr(data, "theory_type") %in%
+          c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
 
-    if(!is.null(direction) && !(direction %in% c("greater", "right")))
+    if (!is.null(direction) && !(direction %in% c("greater", "right")))
       warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                    "Proceed with caution.")
 
@@ -540,16 +540,16 @@ visualize_theoretical <- function(data,
 
   # Move into its own function
 
-  if(!is.null(obs_stat)){
-    if(!is.null(direction)){
-      if(direction %in% c("less", "left")){
+  if (!is.null(obs_stat)) {
+    if (!is.null(direction)) {
+      if (direction %in% c("less", "left")) {
         infer_plot <- infer_plot +
           geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
                     aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
                     ...)
       }
-      if(direction %in% c("greater", "right")){
+      if (direction %in% c("greater", "right")) {
         infer_plot <- infer_plot +
           geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
@@ -558,9 +558,9 @@ visualize_theoretical <- function(data,
                     ...)
       }
 
-      # Assuming two-tailed shading will only happen with theoretical 
+      # Assuming two-tailed shading will only happen with theoretical
       # distributions centered at 0
-      if(direction %in% c("two_sided", "both") && obs_stat >= 0){
+      if (direction %in% c("two_sided", "both") && obs_stat >= 0) {
         infer_plot <- infer_plot +
           geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
@@ -572,7 +572,7 @@ visualize_theoretical <- function(data,
                     ...)
       }
 
-      if(direction %in% c("two_sided", "both") && obs_stat < 0){
+      if (direction %in% c("two_sided", "both") && obs_stat < 0) {
         infer_plot <- infer_plot +
           geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
                     alpha = 0.6,
@@ -605,11 +605,11 @@ visualize_both <- function(data, bins,
     "method. `infer` currently does not check these for you."
   )
 
-  if(!(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
+  if (!(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
     stop_glue("Your `calculate`d statistic and the theoretical distribution ",
               "are on different scales. Use a standardized `stat` instead.")
 
-  if(attr(data, "theory_type") %in% c("Two sample t", "Slope with t")){
+  if (attr(data, "theory_type") %in% c("Two sample t", "Slope with t")) {
 
     infer_plot <- both_t_plot(data = data,
                               deg_freedom = attr(data, "distr_param"),
@@ -623,7 +623,7 @@ visualize_both <- function(data, bins,
                               ci_fill = ci_fill)
   }
 
-  else if(attr(data, "theory_type") == "ANOVA"){
+  else if (attr(data, "theory_type") == "ANOVA") {
     infer_plot <- both_anova_plot(
       data = data,
       deg_freedom_top = attr(data, "distr_param"),
@@ -638,8 +638,8 @@ visualize_both <- function(data, bins,
       ci_fill = ci_fill)
   }
 
-  else if(attr(data, "theory_type") %in%
-          c("One sample prop z", "Two sample props z")){
+  else if (attr(data, "theory_type") %in%
+          c("One sample prop z", "Two sample props z")) {
     infer_plot <- both_z_plot(data = data,
                               statistic_text = "z",
                               dens_color = dens_color,
@@ -651,9 +651,9 @@ visualize_both <- function(data, bins,
                               ci_fill = ci_fill)
   }
 
-  else if(
+  else if (
     attr(data, "theory_type") %in%
-    c("Chi-square test of indep", "Chi-square Goodness of Fit")){
+    c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
     infer_plot <- both_chisq_plot(data = data,
                                   deg_freedom = attr(data, "distr_param"),
                                   statistic_text = "Chi-Square",
diff --git a/R/wrappers.R b/R/wrappers.R
index 72e07f55..d3c034e4 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -21,32 +21,32 @@
 #'   interval or not. `TRUE` by default.
 #' @param conf_level A numeric value between 0 and 1. Default value is 0.95.
 #' @param ... For passing in other arguments to [t.test()][stats::t.test()].
-#' 
+#'
 #' @examples
 #' # t test for comparing mpg against automatic/manual
 #' mtcars %>%
 #'   dplyr::mutate(am = factor(am)) %>%
 #'   t_test(mpg ~ am, order = c("1", "0"), alternative = "less")
-#' 
+#'
 #' @importFrom rlang f_lhs
 #' @importFrom rlang f_rhs
 #' @export
-t_test <- function(data, formula, #response = NULL, explanatory = NULL,
+t_test <- function(data, formula, # response = NULL, explanatory = NULL,
                    order = NULL,
                    alternative = "two_sided", mu = 0,
                    conf_int = TRUE,
                    conf_level = 0.95,
-                   ...){
+                   ...) {
 
   check_conf_level(conf_level)
 
   # Match with old "dot" syntax
-  if(alternative == "two_sided")
+  if (alternative == "two_sided")
     alternative <- "two.sided"
 
   ### Only currently working with formula interface
 #  if (hasArg(formula)) {
-  if(!is.null(f_rhs(formula))){
+  if (!is.null(f_rhs(formula))) {
 
     data[[as.character(f_rhs(formula))]] <-
       factor(data[[as.character(f_rhs(formula))]],
@@ -73,7 +73,7 @@ t_test <- function(data, formula, #response = NULL, explanatory = NULL,
       broom::glance()
   }
 
-  if(conf_int){
+  if (conf_int) {
     results <- prelim %>%
       dplyr::select(statistic, t_df = parameter, p_value = p.value,
                     alternative,
@@ -104,16 +104,16 @@ t_test <- function(data, formula, #response = NULL, explanatory = NULL,
 }
 
 #' Tidy t-test statistic
-#' 
+#'
 #' A shortcut wrapper function to get the observed test statistic for a t test.
 #'
 #' @param data A data frame that can be coerced into a [tibble][tibble::tibble].
 #' @param formula A formula with the response variable on the left and the
 #'   explanatory on the right.
 #' @param ... Pass in arguments to \\{infer\\} functions.
-#' 
+#'
 #' @export
-t_stat <- function(data, formula, ...){
+t_stat <- function(data, formula, ...) {
   data %>%
     t_test(formula = formula, ...) %>%
     dplyr::select(statistic)
@@ -128,19 +128,19 @@ t_stat <- function(data, formula, ...){
 #' @param formula A formula with the response variable on the left and the
 #'   explanatory on the right.
 #' @param ... Additional arguments for [chisq.test()][stats::chisq.test()].
-#' 
+#'
 #' @examples
 #' # chisq test for comparing number of cylinders against automatic/manual
 #' mtcars %>%
 #'   dplyr::mutate(cyl = factor(cyl), am = factor(am)) %>%
 #'   chisq_test(cyl ~ am)
-#' 
+#'
 #' @importFrom rlang f_lhs f_rhs
 #' @export
-chisq_test <- function(data, formula, #response = NULL, explanatory = NULL,
-                       ...){
+chisq_test <- function(data, formula, # response = NULL, explanatory = NULL,
+                       ...) {
 
-  if(is.null(f_rhs(formula)))
+  if (is.null(f_rhs(formula)))
     stop_glue(
       "`chisq_test()` currently only has functionality for ",
       "Chi-Square Test of Independence, not for Chi-Square Goodness of Fit."
@@ -149,14 +149,14 @@ chisq_test <- function(data, formula, #response = NULL, explanatory = NULL,
   explanatory_var <- f_rhs(formula)
   response_var <- f_lhs(formula)
 
-  df <- data[ , as.character(c(response_var, explanatory_var))]
+  df <- data[, as.character(c(response_var, explanatory_var))]
   stats::chisq.test(table(df), ...) %>%
     broom::glance() %>%
     dplyr::select(statistic, chisq_df = parameter, p_value = p.value)
 }
 
 #' Tidy chi-squared test statistic
-#' 
+#'
 #' A shortcut wrapper function to get the observed test statistic for a chisq
 #' test. Uses [chisq.test()][stats::chisq.test()], which applies a continuity
 #' correction.
@@ -165,11 +165,11 @@ chisq_test <- function(data, formula, #response = NULL, explanatory = NULL,
 #' @param formula A formula with the response variable on the left and the
 #'   explanatory on the right.
 #' @param ... Additional arguments for [chisq.test()][stats::chisq.test()].
-#' 
+#'
 #' @export
-chisq_stat <- function(data, formula, ...){
+chisq_stat <- function(data, formula, ...) {
 
-  if(is.null(f_rhs(formula))){
+  if (is.null(f_rhs(formula))) {
     stop_glue(
       "`chisq_stat()` currently only has functionality for ",
       "Chi-Square Test of Independence, not for Chi-Square Goodness of Fit. ",
@@ -183,8 +183,8 @@ chisq_stat <- function(data, formula, ...){
 }
 
 
-check_conf_level <- function(conf_level){
-  if(class(conf_level) != "numeric" |
+check_conf_level <- function(conf_level) {
+  if (class(conf_level) != "numeric" |
      conf_level < 0 |
      conf_level > 1)
     stop_glue("The `conf_level` argument must be a number between 0 and 1.")
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 34933f46..34856237 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -186,7 +186,7 @@ test_that("chi-square matches chisq.test value", {
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
   infer_way <- calculate(gen_iris8, stat = "Chisq")
-  #chisq.test way
+  # chisq.test way
   trad_way <- gen_iris8 %>%
     dplyr::group_by(replicate) %>%
     dplyr::do(broom::tidy(stats::chisq.test(table(.$Petal.Length.Group,
@@ -204,7 +204,7 @@ test_that("chi-square matches chisq.test value", {
                       "virginica" = 1/3)) %>%
     generate(reps = 10, type = "simulate")
   infer_way <- calculate(gen_iris9, stat = "Chisq")
-  #chisq.test way
+  # chisq.test way
   trad_way <- gen_iris9 %>%
     dplyr::group_by(replicate) %>%
     dplyr::do(broom::tidy(stats::chisq.test(table(.$Species)))) %>%
@@ -219,7 +219,7 @@ test_that("chi-square matches chisq.test value", {
                       "virginica" = 0.1)) %>%
     generate(reps = 10, type = "simulate")
   infer_way <- calculate(gen_iris9a, stat = "Chisq")
-  #chisq.test way
+  # chisq.test way
   trad_way <- gen_iris9a %>%
     dplyr::group_by(replicate) %>%
     dplyr::do(broom::tidy(stats::chisq.test(table(.$Species),
@@ -312,16 +312,16 @@ test_that("order being given when not needed gives warning", {
 
 ## Breaks oldrel build. Commented out for now.
 # test_that("warning given if calculate without generate", {
-#   expect_warning(iris %>% 
-#                    specify(Species ~ NULL) %>% 
-#                    hypothesize(null = "point", 
+#   expect_warning(iris %>%
+#                    specify(Species ~ NULL) %>%
+#                    hypothesize(null = "point",
 #                                p = c("setosa" = 0.4,
 #                                      "versicolor" = 0.4,
-#                                      "virginica" = 0.2)) %>% 
-#                    #generate(reps = 10, type = "simulate") %>% 
+#                                      "virginica" = 0.2)) %>%
+#                    #generate(reps = 10, type = "simulate") %>%
 #                    calculate(stat = "Chisq")
 #   )
-#   
+#
 # })
 
 test_that("specify() %>% calculate() works", {
diff --git a/tests/testthat/test-hypothesize.R b/tests/testthat/test-hypothesize.R
index 828a815d..717e0a78 100644
--- a/tests/testthat/test-hypothesize.R
+++ b/tests/testthat/test-hypothesize.R
@@ -66,7 +66,7 @@ test_that("auto `type` works (hypothesize)", {
   expect_equal(attr(slopes, "type"), "permute")
 })
 
-test_that("hypothesize arguments function",{
+test_that("hypothesize arguments function", {
 
   mtcars_f <- dplyr::mutate(mtcars, cyl = factor(cyl))
   mtcars_s <- mtcars_f %>% specify(response = mpg)
@@ -81,7 +81,7 @@ test_that("hypothesize arguments function",{
   expect_error(mtcars_s %>% hypothesize(null = "independence"))
   expect_error(mtcars_s %>% hypothesize(null = "point"))
   # Produces error on win-build
-#  expect_warning(mtcars_s %>% 
+#  expect_warning(mtcars_s %>%
 #                   hypothesize(null = c("point", "independence"), mu = 3))
 
   expect_error(mtcars %>% dplyr::select(vs) %>%
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 55fc5ad0..fce2f025 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -61,7 +61,7 @@ test_that("visualize basic tests", {
                   calculate(stat = "slope") %>%
                   visualize(obs_stat = obs_slope, direction = "right"))
 
-  #obs_stat not specified
+  # obs_stat not specified
   expect_error(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
                           success = "large") %>%
@@ -138,7 +138,7 @@ test_that("visualize basic tests", {
                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
                   hypothesize(null = "independence") %>%
                   generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "t", order = c("small", "large") ) %>%
+                  calculate(stat = "t", order = c("small", "large")) %>%
                   visualize(method = "both", direction = "left",
                             obs_stat = -obs_t)
   )
@@ -146,8 +146,8 @@ test_that("visualize basic tests", {
   expect_warning(iris_tbl %>%
                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
                   hypothesize(null = "independence") %>%
- #                 generate(reps = 100, type = "permute") %>% 
-                  calculate(stat = "t", order = c("small", "large") ) %>%
+ #                 generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "t", order = c("small", "large")) %>%
                   visualize(method = "theoretical", direction = "left",
                             obs_stat = -obs_t)
   )
@@ -196,7 +196,7 @@ test_that("visualize basic tests", {
                   specify(Sepal.Width.Group ~ Species,
                           success = "large") %>%
                   hypothesize(null = "independence") %>%
-                  #calculate(stat = "Chisq") %>% 
+                  # calculate(stat = "Chisq") %>%
                   visualize(method = "theoretical", obs_stat = obs_F,
                             direction = "right")
   )
@@ -212,15 +212,15 @@ test_that("visualize basic tests", {
                   visualize(method = "both")
   )
 
-  #traditional instead of theoretical
+  # traditional instead of theoretical
   expect_error(iris_tbl %>%
                   specify(Species ~ NULL) %>%
                   hypothesize(null = "point",
                               p = c("setosa" = 0.4,
                                     "versicolor" = 0.4,
                                     "virginica" = 0.2)) %>%
-#                  generate(reps = 100, type = "simulate") %>% 
-#                  calculate(stat = "Chisq") %>% 
+#                  generate(reps = 100, type = "simulate") %>%
+#                  calculate(stat = "Chisq") %>%
                   visualize(method = "traditional")
   )
 
@@ -230,8 +230,8 @@ test_that("visualize basic tests", {
                              p = c("setosa" = 0.4,
                                    "versicolor" = 0.4,
                                    "virginica" = 0.2)) %>%
-                 #generate(reps = 100, type = "simulate") %>% 
-                 #calculate(stat = "Chisq") %>% 
+                 # generate(reps = 100, type = "simulate") %>%
+                 # calculate(stat = "Chisq") %>%
                  visualize(method = "theoretical")
   )
 
@@ -269,8 +269,8 @@ test_that("visualize basic tests", {
   expect_warning(iris_tbl %>%
                   specify(Sepal.Width.Group ~ NULL, success = "small") %>%
                   hypothesize(null = "point", p = 0.8) %>%
-#                 generate(reps = 100, type = "simulate") %>% 
-#                  calculate(stat = "z") %>% 
+#                 generate(reps = 100, type = "simulate") %>%
+#                  calculate(stat = "z") %>%
                   visualize(method = "theoretical",
                             obs_stat = 2, # Should probably update
                             direction = "both")
@@ -322,10 +322,10 @@ test_that('method = "both" behaves nicely', {
                  specify(Petal.Width ~ NULL) %>%
                  hypothesize(null = "point", mu = 4) %>%
                  generate(reps = 100, type = "bootstrap") %>%
-                 #    calculate(stat = "mean") %>% 
+                 #    calculate(stat = "mean") %>%
                  visualize(method = "both"))
 
-  #  
+  #
   expect_warning(iris_tbl %>%
                    specify(Petal.Width ~ Sepal.Length.Group) %>%
                    hypothesize(null = "point", mu = 4) %>%
@@ -356,7 +356,7 @@ test_that("Traditional right-tailed tests have warning if not right-tailed", {
                    specify(Sepal.Width.Group ~ Species,
                            success = "large") %>%
                    hypothesize(null = "independence") %>%
- #                  generate(reps = 100, type = "permute") %>% 
+ #                  generate(reps = 100, type = "permute") %>%
                    calculate(stat = "Chisq") %>%
                    visualize(method = "theoretical", obs_stat = 2,
                              direction = "left")
@@ -364,7 +364,7 @@ test_that("Traditional right-tailed tests have warning if not right-tailed", {
   expect_warning(iris_tbl %>%
                    specify(Sepal.Length ~ Species) %>%
                    hypothesize(null = "independence") %>%
-  #                 generate(reps = 100, type = "permute") %>% 
+  #                 generate(reps = 100, type = "permute") %>%
                    calculate(stat = "F") %>%
                    visualize(method = "theoretical", obs_stat = 2,
                              direction = "two_sided")
@@ -372,7 +372,7 @@ test_that("Traditional right-tailed tests have warning if not right-tailed", {
 
 })
 
-test_that("confidence interval plots are working",{
+test_that("confidence interval plots are working", {
 
   iris_boot <- iris_tbl %>%
     specify(Sepal.Width.Group ~ Sepal.Length.Group,
diff --git a/tests/testthat/test-wrappers.R b/tests/testthat/test-wrappers.R
index b43a331d..81c058a8 100644
--- a/tests/testthat/test-wrappers.R
+++ b/tests/testthat/test-wrappers.R
@@ -58,7 +58,7 @@ test_that("_stat functions work", {
 #  another_way <- iris3 %>%
 #    chisq_test(Species ~ NULL) %>%
 #    dplyr::select(statistic)
-#  obs_stat_way <- iris3 %>% 
+#  obs_stat_way <- iris3 %>%
 #    chisq_stat(Species ~ NULL)
 #  expect_equivalent(another_way, obs_stat_way)
 

From ba37ef513c4bfdb02ed0c154920077046cdb7c3e Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 13:44:10 +0300
Subject: [PATCH 04/78] Update indenting (manually).

---
 R/calculate.R                      | 120 ++++++------
 R/conf_int.R                       |  13 +-
 R/generate.R                       |  39 ++--
 R/hypothesize.R                    |  16 +-
 R/infer.R                          |   3 +-
 R/p_value.R                        |  61 ++++---
 R/rep_sample_n.R                   |   6 +-
 R/set_params.R                     |  51 +++---
 R/specify.R                        |  14 +-
 R/utils.R                          |  63 ++++---
 R/visualize.R                      | 130 ++++++-------
 R/wrappers.R                       |  16 +-
 tests/testthat/test-calculate.R    |   6 -
 tests/testthat/test-conf_int.R     |   4 +-
 tests/testthat/test-generate.R     | 168 +++++++++--------
 tests/testthat/test-hypothesize.R  |   9 +-
 tests/testthat/test-p_value.R      |   4 +-
 tests/testthat/test-rep_sample_n.R |   6 +-
 tests/testthat/test-specify.R      |   3 -
 tests/testthat/test-visualize.R    | 284 +++++++++++++----------------
 tests/testthat/test-wrappers.R     |   7 +-
 21 files changed, 480 insertions(+), 543 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 668b9680..041c4080 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -52,33 +52,34 @@ calculate <- function(x,
   check_args_and_attr(x, explanatory_variable(x), response_variable(x), stat)
   check_point_params(x, stat)
 
-  if (!has_response(x))
+  if (!has_response(x)) {
     stop_glue(
       "The response variable is not set. Make sure to `specify()` it first."
     )
+  }
 
   if (is.null(attr(x, "generate")) || !attr(x, "generate")) {
     if (is.null(attr(x, "null"))) {
       x$replicate <- 1L
-    }
-    else if (stat %in% c(
-      "mean",
-      "median",
-      "sd",
-      "prop",
-      "diff in means",
-      "diff in medians",
-      "diff in props",
-      "slope",
-      "correlation"
-    ))
+    } else if (
+      stat %in% c(
+        "mean",
+        "median",
+        "sd",
+        "prop",
+        "diff in means",
+        "diff in medians",
+        "diff in props",
+        "slope",
+        "correlation"
+      )
+    ) {
       stop_glue(
         "Theoretical distributions do not exist (or have not been ",
         "implemented) for `stat` = \"{stat}\". Are you missing ",
         "a `generate()` step?"
       )
-
-    else if (!(stat %in% c("Chisq", "prop"))) {
+    } else if (!(stat %in% c("Chisq", "prop"))) {
       # From `hypothesize()` to `calculate()`
       # Catch-all if generate was not called
 #      warning_glue("You unexpectantly went from `hypothesize()` to ",
@@ -87,10 +88,12 @@ calculate <- function(x,
       return(x)
     }
   }
-
-  if (stat %in% c("diff in means", "diff in medians", "diff in props")  ||
-      (!is.null(attr(x, "theory_type")) &&
-       attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))) {
+  
+  if (
+    stat %in% c("diff in means", "diff in medians", "diff in props") ||
+    (!is.null(attr(x, "theory_type")) &&
+    attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))
+  ) {
     check_order(x, explanatory_variable(x), order)
   }
 
@@ -113,27 +116,30 @@ calculate <- function(x,
   result <- calc_impl(structure(stat, class = gsub(" ", "_", stat)),
                       x, order, ...)
 
-  if ("NULL" %in% class(result))
+  if ("NULL" %in% class(result)) {
     stop_glue(
       "Your choice of `stat` is invalid for the ",
       "types of variables `specify`ed."
     )
-#  else
-#    class(result) <- append("infer", class(result))
+  }
+#   else {
+#     class(result) <- append("infer", class(result))
+#   }
 
   result <- set_attributes(to = result, from = x)
   attr(result, "stat") <- stat
 
   # For returning a 1x1 observed statistic value
-  if (nrow(result) == 1)
+  if (nrow(result) == 1) {
     result <- select(result, stat)
+  }
 
   return(result)
 }
 
-calc_impl <-
-  function(type, x, order, ...)
-    UseMethod("calc_impl", type)
+calc_impl <- function(type, x, order, ...) {
+  UseMethod("calc_impl", type)
+}
 
 
 calc_impl.mean <- function(stat, x, order, ...) {
@@ -142,7 +148,6 @@ calc_impl.mean <- function(stat, x, order, ...) {
   x %>%
     dplyr::group_by(replicate) %>%
     dplyr::summarize(stat = mean(!!(sym(col)), ...))
-
 }
 
 calc_impl.median <- function(stat, x, order, ...) {
@@ -165,18 +170,19 @@ calc_impl.prop <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
 
   ## No longer needed with implementation of `check_point_params()`
-  # if(!is.factor(x[[col]])){
+  # if (!is.factor(x[[col]])) {
   #   stop_glue(
   #     "Calculating a {stat} here is not appropriate since the `{col}` ",
   #     "variable is not a factor."
   #   )
   # }
 
-  if (is.null(attr(x, "success")))
+  if (is.null(attr(x, "success"))) {
     stop_glue(
       'To calculate a proportion, the `"success"` argument ',
       'must be provided in `specify()`.'
     )
+  }
 
   success <- attr(x, "success")
   x %>%
@@ -243,16 +249,13 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
        dplyr::summarize(stat = stats::chisq.test(table(!!(
          attr(x, "response")
        )), p = attr(x, "params"))$stat)
-
     } else {
       # Straight from `specify()`
         stop_glue("In order to calculate a Chi-Square Goodness of Fit ",
                   "statistic, hypothesized values must be given for the `p` ",
                   "parameter in the `hypothesize()` function prior to ",
                   "using `calculate()`")
-
     }
-
   } else {
     # This is not matching with chisq.test
     # obs_tab <- x %>%
@@ -268,7 +271,6 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     #                                - expected)^2 / expected, ...))
 
     # Chi-Square Test of Independence
-
     result <- x %>%
       dplyr::do(broom::tidy(suppressWarnings(stats::chisq.test(table(
         .[[as.character(attr(x, "response"))]],
@@ -276,11 +278,11 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
       ))))) %>%
       dplyr::ungroup()
 
-    if (!is.null(attr(x, "generate")))
-       result <-
-        result %>% dplyr::select(replicate, stat = statistic)
-    else
+    if (!is.null(attr(x, "generate"))) {
+      result <- result %>% dplyr::select(replicate, stat = statistic)
+    } else {
       result <- result %>% dplyr::select(stat = statistic)
+    }
 
     attr(result, "response") <- attr(x, "response")
     attr(result, "success") <- attr(x, "success")
@@ -292,7 +294,6 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     attr(result, "theory_type") <- attr(x, "theory_type")
 
     result
-
   }
 }
 
@@ -354,9 +355,8 @@ calc_impl.t <- function(stat, x, order, ...) {
           stat = stats::t.test(!!attr(x, "response"),
                                ...
                                )[["statistic"]])
-    }
-    # For hypothesis testing
-    else {
+    } else {
+      # For hypothesis testing
       x %>%
         dplyr::summarize(stat = stats::t.test(
           !!attr(x, "response"),
@@ -400,29 +400,29 @@ calc_impl.z <- function(stat, x, order, ...) {
       dplyr::select(-total_suc, -n1, -n2)
 
     df_out
-
-  } else
+  } else if (attr(x, "theory_type") == "One sample prop z") {
     # One sample proportion
-    if (attr(x, "theory_type") == "One sample prop z") {
-      # When `hypothesize()` has been called
-      success <- attr(x, "success")
+    
+    # When `hypothesize()` has been called
+    success <- attr(x, "success")
 
-      p0 <- attr(x, "params")[1]
-      num_rows <- nrow(x) / length(unique(x$replicate))
+    p0 <- attr(x, "params")[1]
+    num_rows <- nrow(x) / length(unique(x$replicate))
 
-      col <- attr(x, "response")
-      #    if(is.null(success))
-      #      success <- quo(get_par_levels(x)[1])
-      # Error given instead
+    col <- attr(x, "response")
+#     if (is.null(success)) {
+#       success <- quo(get_par_levels(x)[1])
+#     }
+#     Error given instead
 
-      df_out <- x %>%
-        dplyr::summarize(stat = (mean(
-          rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
-        ) - p0) / sqrt((p0 * (1 - p0)) / num_rows))
+    df_out <- x %>%
+      dplyr::summarize(stat = (mean(
+        rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
+      ) - p0) / sqrt((p0 * (1 - p0)) / num_rows))
 
-      df_out
+    df_out
 
-      # Straight from `specify()` doesn't make sense
-      # since standardizing requires a hypothesized value
-    }
+    # Straight from `specify()` doesn't make sense
+    # since standardizing requires a hypothesized value
+  }
 }
diff --git a/R/conf_int.R b/R/conf_int.R
index 887a58ad..6fbadc06 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -37,7 +37,6 @@ NULL
 #' @export
 conf_int <- function(x, level = 0.95, type = "percentile",
                      point_estimate = NULL) {
-
   check_ci_args(x, level, type, point_estimate)
 
   if (type == "percentile") {
@@ -58,12 +57,12 @@ conf_int <- function(x, level = 0.95, type = "percentile",
 }
 
 check_ci_args <- function(x, level, type, point_estimate) {
-
   if (!is.null(point_estimate)) {
-    if (!is.data.frame(point_estimate))
+    if (!is.data.frame(point_estimate)) {
       check_type(point_estimate, is.numeric)
-    else
+    } else {
       check_type(point_estimate, is.data.frame)
+    }
   }
   check_type(x, is.data.frame)
   check_type(level, is.numeric)
@@ -75,12 +74,14 @@ check_ci_args <- function(x, level, type, point_estimate) {
     stop_glue('The options for `type` are "percentile" or "se".')
   }
 
-  if (type == "se" && is.null(point_estimate))
+  if (type == "se" && is.null(point_estimate)) {
     stop_glue('A numeric value needs to be given for `point_estimate` ',
               'for `type = "se"')
+  }
 
-  if (type == "se" && is.vector(point_estimate))
+  if (type == "se" && is.vector(point_estimate)) {
     check_type(point_estimate, is.numeric)
+  }
 }
 
 
diff --git a/R/generate.R b/R/generate.R
index 2c86a47d..9f10c488 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -22,7 +22,6 @@
 #' @importFrom dplyr group_by
 #' @export
 generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
-
   auto_type <- attr(x, "type")
 
   if (!is.null(auto_type)) {
@@ -30,13 +29,14 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
       stop_glue("Supply not `NULL` value of `type`.")
     }
 
-    if (auto_type != type)
+    if (auto_type != type) {
       stop_glue(
         "You have specified `type = \"{type}\"`, but `type` is expected to be ",
         "`\"{auto_type}\"`. Please try again with appropriate `type` value."
       )
-    else
+    } else {
       type <- auto_type
+    }
   }
 
   attr(x, "generate") <- TRUE
@@ -62,16 +62,14 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
   if (type == "bootstrap") {
     return(bootstrap(x, reps, ...))
-  }
-  else if (type == "permute") {
+  } else if (type == "permute") {
     return(permute(x, reps, ...))
-  }
-  else if (type == "simulate") {
+  } else if (type == "simulate") {
     return(simulate(x, reps, ...))
-  }
-#  else if (!(type %in% c("bootstrap", "permute", "simulate")))
-#    stop_glue("Choose one of the available options for `type`: ",
-#              '`"bootstrap"`, `"permute"`, or `"simulate"`')
+  } # else if (!(type %in% c("bootstrap", "permute", "simulate"))) {
+    # stop_glue("Choose one of the available options for `type`: ",
+    #           '`"bootstrap"`, `"permute"`, or `"simulate"`')
+  # }
 }
 
 bootstrap <- function(x, reps = 1, ...) {
@@ -80,21 +78,20 @@ bootstrap <- function(x, reps = 1, ...) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
     if (attr(attr(x, "params"), "names") == "mu") {
-
       col <- as.character(attr(x, "response"))
-#      if(attr(x, "theory_type") != "One sample t"){
-        x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
+#      if (attr(x, "theory_type") != "One sample t") {
+      x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
 #      }
 
       # Standardize after centering above
       #####
       # Determining whether or not to implement this t transformation
       #####
-#      else {
-#        std_error <- stats::sd(x[[col]], na.rm = TRUE) /
-#                        sqrt(length(x[[col]]))
-#        x[[col]] <- ( x[[col]] - mean(x[[col]], na.rm = TRUE) ) / std_error
-#      }
+      # else {
+      #   std_error <- stats::sd(x[[col]], na.rm = TRUE) /
+      #     sqrt(length(x[[col]]))
+      #   x[[col]] <- (x[[col]] - mean(x[[col]], na.rm = TRUE)) / std_error
+      # }
     }
 
     # Similarly for median
@@ -109,7 +106,7 @@ bootstrap <- function(x, reps = 1, ...) {
 
     # Similarly for sd
     ## Temporarily removed since this implementation does not scale correctly
-    # else if(attr(attr(x, "params"), "names") == "sigma"){
+    # else if (attr(attr(x, "params"), "names") == "sigma") {
     #   col <- as.character(attr(x, "response"))
     #   x[[col]] <- x[[col]] -
     #     stats::sd(x[[col]], na.rm = TRUE) + attr(x, "params")
@@ -126,7 +123,6 @@ bootstrap <- function(x, reps = 1, ...) {
 }
 
 #' @importFrom dplyr bind_rows group_by
-
 permute <- function(x, reps = 1, ...) {
   df_out <- replicate(reps, permute_once(x), simplify = FALSE) %>%
     dplyr::bind_rows() %>%
@@ -150,7 +146,6 @@ permute_once <- function(x, ...) {
     x[as.character(attr(x, "response"))] <- y_prime
     return(x)
   }
-
 }
 
 #' @importFrom dplyr pull
diff --git a/R/hypothesize.R b/R/hypothesize.R
index 62f8046f..af9a6b5c 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -19,7 +19,6 @@
 #'
 #' @export
 hypothesize <- function(x, null, ...) {
-
   hypothesize_checks(x, null)
 
   attr(x, "null") <- null
@@ -49,26 +48,31 @@ hypothesize <- function(x, null, ...) {
 
   }
 
-  if (!is.null(null) && null == "independence")
+  if (!is.null(null) && null == "independence") {
     attr(x, "type") <- "permute"
+  }
 
   # Check one proportion test set up correctly
   if (null == "point") {
     if (is.factor(response_variable(x))) {
-      if (!any(grepl("p", attr(attr(x, "params"), "names"))))
+      if (!any(grepl("p", attr(attr(x, "params"), "names")))) {
         stop_glue('Testing one categorical variable requires `p` ',
                   'to be used as a parameter.')
+      }
     }
   }
 
   # Check one numeric test set up correctly
   ## Not currently able to reach in testing as other checks
   ## already produce errors
-  # if(null == "point"){
-  #   if(!is.factor(response_variable(x))
-  #      & !any(grepl("mu|med|sigma", attr(attr(x, "params"), "names"))))
+  # if (null == "point") {
+  #   if (
+  #     !is.factor(response_variable(x))
+  #     & !any(grepl("mu|med|sigma", attr(attr(x, "params"), "names")))
+  #   ) {
   #     stop_glue('Testing one numerical variable requires one of ',
   #               '`mu`, `med`, or `sd` to be used as a parameter.')
+  #   }
   # }
 
   return(tibble::as_tibble(x))
diff --git a/R/infer.R b/R/infer.R
index 679908d8..084a98c8 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -14,10 +14,11 @@ NULL
 
 ## quiets concerns of R CMD check re: the .'s that appear in pipelines
 ## From Jenny Bryan's googlesheets package
-if (getRversion() >= "2.15.1")
+if (getRversion() >= "2.15.1") {
   utils::globalVariables(c("prop", "stat", "xbar", "xtilde", "x", "..density..",
                            "statistic", ".", "parameter", "p.value",
                            "xmin", "xmax", "density", "denom",
                            "diff_prop", "group_num", "n1", "n2",
                            "num_suc", "p_hat", "total_suc", "explan", "probs",
                            "conf.low", "conf.high"))
+}
diff --git a/R/p_value.R b/R/p_value.R
index 8cceab14..fe349ede 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -32,7 +32,6 @@ NULL
 #' @rdname get_pvalue
 #' @export
 p_value <- function(x, obs_stat, direction) {
-
   check_type(x, is.data.frame)
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
@@ -40,17 +39,21 @@ p_value <- function(x, obs_stat, direction) {
   is_simulation_based <- !is.null(attr(x, "generate")) &&
     attr(x, "generate")
 
-  if (is_simulation_based)
+  if (is_simulation_based) {
     pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
-                                        direction = direction)
+                                       direction = direction)
+  }
 
   ## Theoretical-based p-value
   # Could be more specific
-  # else if(is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param")))
+  # else if (
+  #   is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param"))
+  # ) {
   #   stop_glue("Attributes have not been set appropriately. ",
   #             "Check your {{infer}} pipeline again.")
+  # }
 
-  # if(!("stat" %in% names(x))){
+  # if (!("stat" %in% names(x))) {
   #    # Theoretical distribution
   #  which_distribution(x,
   #                     theory_type <- attr(x, "theory_type"),
@@ -62,16 +65,13 @@ p_value <- function(x, obs_stat, direction) {
 }
 
 simulation_based_p_value <- function(x, obs_stat, direction) {
-
   if (direction %in% c("less", "left")) {
-   p_value <- x %>%
+    p_value <- x %>%
       dplyr::summarize(p_value = mean(stat <= obs_stat))
-  }
-  else if (direction %in% c("greater", "right")) {
-   p_value <- x %>%
+  } else if (direction %in% c("greater", "right")) {
+    p_value <- x %>%
       dplyr::summarize(p_value = mean(stat >= obs_stat))
-  }
-  else {
+  } else {
     p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
   }
 
@@ -79,51 +79,52 @@ simulation_based_p_value <- function(x, obs_stat, direction) {
 }
 
 two_sided_p_value <- function(x, obs_stat) {
-
   if (stats::median(x$stat) >= obs_stat) {
     basic_p_value <- get_percentile(x$stat, obs_stat) +
       (1 - get_percentile(x$stat, stats::median(x$stat) +
-                       stats::median(x$stat) - obs_stat))
+                            stats::median(x$stat) - obs_stat))
   } else {
     basic_p_value <- 1 - get_percentile(x$stat, obs_stat) +
       (get_percentile(x$stat, stats::median(x$stat) +
-                            stats::median(x$stat) - obs_stat))
+                        stats::median(x$stat) - obs_stat))
   }
 
-  if (basic_p_value >= 1)
+  if (basic_p_value >= 1) {
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
     # scenario instead of using >=
     return(tibble::tibble(p_value = 1))
-  else
+  } else {
     return(tibble::tibble(p_value = basic_p_value))
+  }
 }
 
 #' @rdname get_pvalue
 #' @export
 get_pvalue <- p_value
 
-# which_distribution <- function(x, theory_type, obs_stat, direction){
-#
+# which_distribution <- function(x, theory_type, obs_stat, direction) {
 #   param <- attr(x, "distr_param")
-#   if(!is.null(attr(x, "distr_param2")))
+#   if (!is.null(attr(x, "distr_param2"))) {
 #     param2 <- attr(x, "distr_param2")
-#
-#   if(theory_type == "Two sample t")
+#   }
+# 
+#   if (theory_type == "Two sample t") {
 #     return(pt(q = obs_stat,
-#                              df = param,
-#                              lower.tail = set_lower_tail(direction))
-#          )
-#
+#               df = param,
+#               lower.tail = set_lower_tail(direction))
+#     )
+#   }
 # }
 
 # theory_t_pvalue <-
 
-# set_lower_tail <- function(direction){
-#   if(direction %in% c("greater", "right"))
+# set_lower_tail <- function(direction) {
+#   if (direction %in% c("greater", "right")) {
 #     lower_tail <- FALSE
-#   else
+#   } else {
 #     lower_tail <- TRUE
-#
+#   }
+# 
 #   lower_tail
 # }
diff --git a/R/rep_sample_n.R b/R/rep_sample_n.R
index b9079387..0dc80c99 100644
--- a/R/rep_sample_n.R
+++ b/R/rep_sample_n.R
@@ -49,17 +49,19 @@ rep_sample_n <- function(tbl, size, replace = FALSE, reps = 1, prob = NULL) {
   check_type(size, is.numeric)
   check_type(replace, is.logical)
   check_type(reps, is.numeric)
-  if (!is.null(prob))
+  if (!is.null(prob)) {
     check_type(prob, is.numeric)
+  }
 
   # assign non-uniform probabilities
   # there should be a better way!!
   # prob needs to be nrow(tbl) -- not just number of factor levels
   if (!is.null(prob)) {
-    if (length(prob) != n)
+    if (length(prob) != n) {
       stop_glue(
         "The argument `prob` must have length `nrow(tbl)` = {nrow(tbl)}"
       )
+    }
 
     prob <- tibble::tibble(vals = levels(dplyr::pull(tbl, 1))) %>%
       dplyr::mutate(probs = prob) %>%
diff --git a/R/set_params.R b/R/set_params.R
index 77717f74..827d90b1 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -4,7 +4,6 @@
 #'
 #' @noRd
 set_params <- function(x) {
-
   attr(x, "theory_type") <- NULL
 
   if (!is.null(attr(x, "response"))) {
@@ -21,15 +20,15 @@ set_params <- function(x) {
       attr(x, "theory_type") <- "One sample t"
       attr(x, "distr_param") <- x %>%
         dplyr::summarize(df = stats::t.test(
-            response_variable(x))[["parameter"]]
-          ) %>%
+          response_variable(x))[["parameter"]]
+        ) %>%
         dplyr::pull()
       attr(x, "type") <- "bootstrap"
-    }
-
-    # One prop
-    else if (attr(x, "response_type") == "factor" && (num_response_levels == 2)) {
-
+    } else if (
+      
+      # One prop
+      attr(x, "response_type") == "factor" && (num_response_levels == 2)
+    ) {
       # No parameters since standard normal
       attr(x, "theory_type") <- "One sample prop z"
       # Changed to `"simulate"` when `p` provided in `hypothesize()`
@@ -39,20 +38,18 @@ set_params <- function(x) {
       attr(x, "distr_param") <- num_response_levels - 1
       attr(x, "type") <- "simulate"
     }
-
   }
 
   # Two variables
   if (!is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
       !is.null(attr(x, "response_type")) &&
       !is.null(attr(x, "explanatory_type"))) {
-
     attr(x, "type") <- "bootstrap"
 
     # Response is numeric, explanatory is categorical
     if (attr(x, "response_type") %in% c("integer", "numeric") &
-       attr(x, "explanatory_type") == "factor") {
-
+        attr(x, "explanatory_type") == "factor") {
+      
       # Two sample means (t distribution)
       if (length(levels(explanatory_variable(x))) == 2) {
         attr(x, "theory_type") <- "Two sample t"
@@ -60,40 +57,40 @@ set_params <- function(x) {
         # in aggregation w/ calculate()/generate()
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = stats::t.test(
-              !!attr(x, "response") ~ !!attr(x, "explanatory"))[["parameter"]]
-            ) %>%
+            !!attr(x, "response") ~ !!attr(x, "explanatory"))[["parameter"]]
+          ) %>%
           dplyr::pull()
       } else {
+        
         # >2 sample means (F distribution)
         attr(x, "theory_type") <- "ANOVA"
         # Get numerator and denominator degrees of freedom
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df1 = stats::anova(stats::aov(
-              !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[1]
-            ) %>%
+            !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[1]
+          ) %>%
           dplyr::pull()
         attr(x, "distr_param2") <- x %>%
           dplyr::summarize(df2 = stats::anova(stats::aov(
-              !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[2]
-            ) %>%
+            !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[2]
+          ) %>%
           dplyr::pull()
       }
     }
 
     # Response is categorical, explanatory is categorical
     if (attr(x, "response_type") == "factor" &
-       attr(x, "explanatory_type") == "factor") {
-
+        attr(x, "explanatory_type") == "factor") {
       attr(x, "type") <- "bootstrap"
 
       # Two sample proportions (z distribution)
       # Parameter(s) not needed since standard normal
       if (length(levels(response_variable(x))) == 2 &
-         length(levels(explanatory_variable(x))) == 2) {
+          length(levels(explanatory_variable(x))) == 2) {
         attr(x, "theory_type") <- "Two sample props z"
-      }
-      # >2 sample proportions (chi-square test of indep)
-      else {
+      } else {
+        
+        # >2 sample proportions (chi-square test of indep)
         attr(x, "theory_type") <- "Chi-square test of indep"
         attr(x, "distr_param") <- x %>%
           dplyr::summarize(df = suppressWarnings(stats::chisq.test(
@@ -105,11 +102,11 @@ set_params <- function(x) {
 
     # Response is numeric, explanatory is numeric
     if (attr(x, "response_type") %in% c("integer", "numeric") &
-       attr(x, "explanatory_type") %in% c("integer", "numeric")) {
+        attr(x, "explanatory_type") %in% c("integer", "numeric")) {
       response_string <- as.character(attr(x, "response"))
       explanatory_string <- as.character(attr(x, "explanatory"))
-    attr(x, "theory_type") <- "Slope/correlation with t"
-    attr(x, "distr_param") <- nrow(x) - 2
+      attr(x, "theory_type") <- "Slope/correlation with t"
+      attr(x, "distr_param") <- nrow(x) - 2
     }
   }
 
diff --git a/R/specify.R b/R/specify.R
index c3a3ab6b..f0e33193 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -119,20 +119,22 @@ specify <- function(x, formula, response = NULL,
   # To help determine theoretical distribution to plot
   attr(x, "response_type") <- class(response_variable(x))
 
-  if (is.null(attr(x, "explanatory")))
+  if (is.null(attr(x, "explanatory"))) {
     attr(x, "explanatory_type") <- NULL
-  else
+  } else {
     attr(x, "explanatory_type") <- class(explanatory_variable(x))
+  }
 
   if (attr(x, "response_type") == "factor" && is.null(success) &&
-     length(levels(response_variable(x))) == 2 &&
-     (is.null(attr(x, "explanatory_type")) ||
-     (!is.null(attr(x, "explanatory_type")) &&
-     length(levels(explanatory_variable(x))) == 2)))
+      length(levels(response_variable(x))) == 2 &&
+      (is.null(attr(x, "explanatory_type")) ||
+      (!is.null(attr(x, "explanatory_type")) &&
+      length(levels(explanatory_variable(x))) == 2))) {
     stop_glue(
       'A level of the response variable `{attr(x, "response")}` ',
       'needs to be specified for the `success` argument in `specify()`.'
     )
+  }
 
   # Determine appropriate parameters for theoretical distribution fit
   x <- set_params(x)
diff --git a/R/utils.R b/R/utils.R
index feb18799..69a77962 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -101,22 +101,25 @@ check_order <- function(x, explanatory_variable, order) {
               '`order = c("first", "second")` means `("first" - "second")`. ',
               "Check `?calculate` for details.")
   } else {
-    if (xor(is.na(order[1]), is.na(order[2])))
+    if (xor(is.na(order[1]), is.na(order[2]))) {
       stop_glue(
         "Only one level specified in `order`. Both levels need to be specified."
       )
-    if (length(order) > 2)
+    }
+    if (length(order) > 2) {
       stop_glue("`order` is expecting only two entries.")
-    if (order[1] %in% unique_explanatory_variable == FALSE)
+    }
+    if (order[1] %in% unique_explanatory_variable == FALSE) {
       stop_glue("{order[1]} is not a level of the explanatory variable.")
-    if (order[2] %in% unique_explanatory_variable == FALSE)
+    }
+    if (order[2] %in% unique_explanatory_variable == FALSE) {
       stop_glue("{order[2]} is not a level of the explanatory variable.")
+    }
   }
 }
 
 check_args_and_attr <- function(x, explanatory_variable, response_variable,
                                 stat) {
-
   # Could also do `stat <- match.arg(stat)`
   # but that's not as helpful to beginners with the cryptic error msg
   if (!stat %in% c("mean", "median", "sd", "prop",
@@ -126,9 +129,10 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
               "Check your spelling and `?calculate` for current options.")
   }
 
-  if (!("replicate" %in% names(x)) && !is.null(attr(x, "generate")))
+  if (!("replicate" %in% names(x)) && !is.null(attr(x, "generate"))) {
     warning_glue('A `generate()` step was not performed prior to ',
                  '`calculate()`. Review carefully.')
+  }
 
   if (stat %in% c("F", "slope", "diff in means", "diff in medians")) {
     if (has_explanatory(x) && !is.numeric(response_variable(x))) {
@@ -163,7 +167,6 @@ check_for_numeric_stat <- function(x, stat) {
 }
 
 check_for_factor_stat <- function(x, stat, explanatory_variable) {
-
   if (stat %in% c("diff in means", "diff in medians", "diff in props", "F")) {
     if (!is.factor(explanatory_variable)) {
       stop_glue(
@@ -176,28 +179,34 @@ check_for_factor_stat <- function(x, stat, explanatory_variable) {
 }
 
 check_point_params <- function(x, stat) {
-
   param_names <- attr(attr(x, "params"), "names")
   hyp_text <- 'to be set in `hypothesize()`.'
   if (!is.null(attr(x, "null"))) {
     if (stat %in% c("mean", "median", "sd", "prop")) {
-      if ((stat == "mean" && !("mu" %in% param_names)))
+      if ((stat == "mean" && !("mu" %in% param_names))) {
         stop_glue('`stat == "mean"` requires `"mu"` {hyp_text}')
-      if ((!(stat == "mean") && ("mu" %in% param_names)))
+      }
+      if ((!(stat == "mean") && ("mu" %in% param_names))) {
         stop_glue('`"mu"` does not correspond to `stat = "{stat}"`.')
-      if ((stat == "median" && !("med" %in% param_names)))
+      }
+      if ((stat == "median" && !("med" %in% param_names))) {
         stop_glue('`stat == "median"` requires `"med"` {hyp_text}')
-      if ((!(stat == "median") && ("med" %in% param_names)))
+      }
+      if ((!(stat == "median") && ("med" %in% param_names))) {
         stop_glue('`"med"` does not correspond to `stat = "{stat}"`.')
+      }
       ## Tests unable to get to
-      # if( (stat == "sigma" && !("sd" %in% param_names)) )
+      # if ((stat == "sigma" && !("sd" %in% param_names))) {
       #   stop_glue('`stat == "sd"` requires `"sigma"` {hyp_text}')
-      if ((!(stat == "sd") && ("sigma" %in% param_names)))
+      # }
+      if ((!(stat == "sd") && ("sigma" %in% param_names))) {
         stop_glue('`"sigma"` does not correspond to `stat = "{stat}"`.')
+      }
 
       ## Tests unable to get to
-      # if(stat == "prop" && !(any(grepl("p.", param_names))))
+      # if (stat == "prop" && !(any(grepl("p.", param_names)))) {
       #   stop_glue('`stat == "prop"` requires `"p"` {hyp_text}')
+      # }
     }
   }
 }
@@ -210,7 +219,7 @@ parse_params <- function(dots, x) {
 
   # error: cannot specify more than one of props, means, medians, or sds
   if (length(p_ind) + length(mu_ind) + length(med_ind)
-       + length(sig_ind) != 1) {
+      + length(sig_ind) != 1) {
     stop_glue(
       'Parameter values can be only one of `p`, `mu`, `med`, or `sigma`.'
     )
@@ -221,17 +230,17 @@ parse_params <- function(dots, x) {
   # 0 index of dots
   if (length(p_ind)) {
     if (length(dots[[p_ind]]) == 1) {
-
       if (attr(x, "null") == "point" && is.null(attr(x, "success"))) {
         stop_glue("A point null regarding a proportion requires ",
                   "that `success` be indicated in `specify()`.")
       }
-      if (dots$p < 0 || dots$p > 1)
+      if (dots$p < 0 || dots$p > 1) {
         stop_glue(
           "The value suggested for `p` is not between 0 and 1, inclusive."
         )
+      }
       missing_lev <- base::setdiff(unique(pull(x, !!attr(x, "response"))),
-                             attr(x, "success"))
+                                   attr(x, "success"))
       dots$p <- append(dots$p, 1 - dots$p)
       names(dots$p) <- c(attr(x, "success"), missing_lev)
     } else {
@@ -242,7 +251,7 @@ parse_params <- function(dots, x) {
     }
   }
 
-  # if (sum(dots[[p_ind]]) != 1){
+  # if (sum(dots[[p_ind]]) != 1) {
   #   dots[[p_ind]] <- dots[[p_ind]]/sum(dots[[p_ind]])
   #   warning_glue("Proportions do not sum to 1, normalizing automatically.")
   # }
@@ -263,10 +272,10 @@ hypothesize_checks <- function(x, null) {
     )
   }
 
-  #  if (length(null) != 1) {
-  #    stop_glue('Choose between either `"independence"` or `"point"` ',
-  #              'for `null` argument.')
-  #  }
+   # if (length(null) != 1) {
+   #   stop_glue('Choose between either `"independence"` or `"point"` ',
+   #             'for `null` argument.')
+   # }
 
   if (!has_response(x)) {
     stop_glue(
@@ -297,15 +306,15 @@ check_obs_stat <- function(obs_stat) {
   if (!is.null(obs_stat)) {
     if ("data.frame" %in% class(obs_stat)) {
       check_type(obs_stat, is.data.frame)
-      if ((nrow(obs_stat) != 1) || (ncol(obs_stat) != 1))
+      if ((nrow(obs_stat) != 1) || (ncol(obs_stat) != 1)) {
         warning_glue("The first row and first column value of the given ",
                      "`obs_stat` will be used.")
+      }
 
       # [[1]] is used in case `stat` is not specified as name of 1x1
       obs_stat <- obs_stat[[1]][[1]]
       check_type(obs_stat, is.numeric)
-    }
-    else {
+    } else {
       check_type(obs_stat, is.numeric)
     }
   }
diff --git a/R/visualize.R b/R/visualize.R
index 6cd293c7..121dbf7d 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -78,17 +78,17 @@ visualize <- function(data, bins = 15, method = "simulation",
                       endpoints = NULL,
                       endpoints_color = "mediumaquamarine",
                       ci_fill = "turquoise", ...) {
-
   check_type(data, is.data.frame)
   check_type(bins, is.numeric)
   check_type(method, is.character)
   check_type(dens_color, is.character)
   check_type(obs_stat_color, is.character)
   check_type(pvalue_fill, is.character)
-  if (!is.null(direction))
+  if (!is.null(direction)) {
     check_type(direction, is.character)
+  }
   if (is.data.frame(endpoints) &&
-     ((nrow(endpoints) != 1) || (ncol(endpoints) != 2))) {
+      ((nrow(endpoints) != 1) || (ncol(endpoints) != 2))) {
     stop_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
     )
@@ -100,18 +100,19 @@ visualize <- function(data, bins = 15, method = "simulation",
     )
     endpoints <- endpoints[1:2]
   }
-  if (is.data.frame(endpoints))
+  if (is.data.frame(endpoints)) {
     endpoints <- unlist(endpoints)
+  }
   obs_stat <- check_obs_stat(obs_stat)
   if (!is.null(direction) &&
-     (is.null(obs_stat) + is.null(endpoints)) != 1)
+      (is.null(obs_stat) + is.null(endpoints)) != 1) {
     stop_glue(
       "Shading requires either `endpoints` values for a confidence interval ",
       "or the observed statistic `obs_stat` to be provided."
     )
+  }
 
   if (method == "simulation") {
-
     infer_plot <- visualize_simulation(data = data, bins = bins,
                                        dens_color = dens_color,
                                        obs_stat = obs_stat,
@@ -121,9 +122,7 @@ visualize <- function(data, bins = 15, method = "simulation",
                                        endpoints = endpoints,
                                        ci_fill = ci_fill,
                                        ...)
-
   } else if (method == "theoretical") {
-
     infer_plot <- visualize_theoretical(data = data,
                                         dens_color = dens_color,
                                         obs_stat = obs_stat,
@@ -133,20 +132,19 @@ visualize <- function(data, bins = 15, method = "simulation",
                                         endpoints = endpoints,
                                         ci_fill = ci_fill,
                                         ...)
-
-
   } else if (method == "both") {
-
-    if (!("stat" %in% names(data)))
+    if (!("stat" %in% names(data))) {
       stop_glue('`generate()` and `calculate()` are both required ',
                 'to be done prior to `visualize(method = "both")`')
+    }
 
     if (("replicate" %in% names(data)) &&
-         length(unique(data$replicate)) < 100)
+        length(unique(data$replicate)) < 100) {
       warning_glue(
         "With only {length(unique(data$stat))} replicates, it may be ",
         "difficult to see the relationship between simulation and theory."
       )
+    }
 
     infer_plot <- visualize_both(data = data, bins = bins,
                                  dens_color = dens_color,
@@ -169,9 +167,10 @@ visualize <- function(data, bins = 15, method = "simulation",
   }
 
   if (!is.null(endpoints)) {
-    if (!is.null(obs_stat))
+    if (!is.null(obs_stat)) {
       warning_glue("Values for both `endpoints` and `obs_stat` were given ",
                    "when only one should be set. Ignoring `obs_stat` values.")
+    }
     infer_plot <- infer_plot +
       geom_vline(xintercept = endpoints, size = 2,
                  color = endpoints_color,
@@ -181,7 +180,6 @@ visualize <- function(data, bins = 15, method = "simulation",
   infer_plot
 }
 
-
 theory_t_plot <- function(deg_freedom, statistic_text = "t",
                           dens_color = dens_color, ...) {
   ggplot(data.frame(x = c(qt(0.001, deg_freedom),
@@ -201,7 +199,6 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
                         pvalue_fill,
                         endpoints,
                         ci_fill, ...) {
-
   infer_t_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
                                       direction = direction,
@@ -243,11 +240,11 @@ both_anova_plot <- function(data, deg_freedom_top,
                             pvalue_fill,
                             ci_fill,
                             ...) {
-
-  if (!is.null(direction) && !(direction %in% c("greater", "right")))
+  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
     warning_glue(
       "F usually corresponds to right-tailed tests. Proceed with caution."
     )
+  }
 
   infer_anova_plot <- shade_density_check(data = data,
                                           obs_stat = obs_stat,
@@ -269,7 +266,6 @@ both_anova_plot <- function(data, deg_freedom_top,
 }
 
 theory_z_plot <- function(statistic_text = "z", dens_color = dens_color,  ...) {
-
   ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) +
     stat_function(mapping = aes(x), fun = dnorm, color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
@@ -286,7 +282,6 @@ both_z_plot <- function(data, statistic_text = "z",
                         endpoints,
                         ci_fill,
                         ...) {
-
   infer_z_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
                                       direction = direction,
@@ -294,7 +289,6 @@ both_z_plot <- function(data, statistic_text = "z",
                                       endpoints = endpoints,
                                       pvalue_fill = pvalue_fill,
                                       ci_fill = ci_fill)
-
   infer_z_plot +
     stat_function(fun = dnorm, color = dens_color) +
     ggtitle(glue_null(
@@ -326,10 +320,10 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                             pvalue_fill = pvalue_fill,
                             ci_fill = ci_fill,
                             ...) {
-
-  if (!is.null(direction) && !(direction %in% c("greater", "right")))
+  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
     warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                  "Proceed with caution.")
+  }
 
   infer_chisq_plot <- shade_density_check(data = data,
                                           obs_stat = obs_stat,
@@ -349,7 +343,6 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
     ylab("")
 }
 
-
 shade_density_check <- function(data,
                                 obs_stat,
                                 direction,
@@ -358,7 +351,6 @@ shade_density_check <- function(data,
                                 pvalue_fill,
                                 endpoints,
                                 ci_fill, ...) {
-
   if (is.null(direction) || is.null(obs_stat)) {
     if (density) {
       gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
@@ -396,7 +388,7 @@ shade_density_check <- function(data,
       }
 
       if (direction %in% c("two_sided", "both") &&
-         obs_stat >= stats::median(data$stat)) {
+          obs_stat >= stats::median(data$stat)) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0,
@@ -413,7 +405,7 @@ shade_density_check <- function(data,
       }
 
       if (direction %in% c("two_sided", "both") &&
-         obs_stat < stats::median(data$stat)) {
+          obs_stat < stats::median(data$stat)) {
         gg_plot <- gg_plot +
           geom_rect(fill = pvalue_fill, alpha = 0.01,
                     mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0,
@@ -428,7 +420,6 @@ shade_density_check <- function(data,
       }
     }
 
-
     if (direction == "between") {
       gg_plot <- gg_plot +
         geom_rect(fill = ci_fill, alpha = 0.01,
@@ -436,9 +427,8 @@ shade_density_check <- function(data,
                       xmax = endpoints[2], ymin = 0, ymax = Inf),
                   ...)
     }
-
   }
-    gg_plot
+  gg_plot
 }
 
 visualize_simulation <- function(data, bins,
@@ -451,13 +441,14 @@ visualize_simulation <- function(data, bins,
                                  endpoints,
                                  ci_fill, ...) {
   if (is.null(direction)) {
-    if (length(unique(data$stat)) >= 10)
+    if (length(unique(data$stat)) >= 10) {
       infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
         geom_histogram(bins = bins, color = "white", ...)
-    else
+    } else {
       infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
         geom_bar(...) +
         xlab("stat")
+    }
   } else {
     infer_plot <- shade_density_check(data = data,
                                       obs_stat = obs_stat,
@@ -466,8 +457,7 @@ visualize_simulation <- function(data, bins,
                                       density = FALSE,
                                       pvalue_fill = pvalue_fill,
                                       endpoints = endpoints,
-                                      ci_fill = ci_fill
-    )
+                                      ci_fill = ci_fill)
   }
   infer_plot
 }
@@ -481,65 +471,60 @@ visualize_theoretical <- function(data,
                                   endpoints,
                                   ci_fill,
                                   ...) {
-
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. {{infer}} currently does not check these for you."
   )
 
   if (!is.null(attr(data, "stat")) &&
-     !(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
+      !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))) {
     warning_glue(
       "Your `calculate`d statistic and the theoretical distribution are on ",
       "different scales. Displaying only the theoretical distribution."
     )
+  }
 
   if (attr(data, "theory_type") %in%
-     c("Two sample t", "Slope with t", "One sample t")) {
+      c("Two sample t", "Slope with t", "One sample t")) {
     infer_plot <- theory_t_plot(deg_freedom = attr(data, "distr_param"),
                                 statistic_text = "t",
                                 dens_color = dens_color)
-  }
-
-  else if (attr(data, "theory_type") == "ANOVA") {
-
-    if (!is.null(direction) && !(direction %in% c("greater", "right")))
+  } else if (attr(data, "theory_type") == "ANOVA") {
+    
+    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
       warning_glue(
         "F usually corresponds to right-tailed tests. Proceed with caution."
       )
+    }
 
     infer_plot <- theory_anova_plot(
       deg_freedom_top = attr(data, "distr_param"),
       deg_freedom_bottom = attr(data, "distr_param2"),
       statistic_text = "F",
       dens_color = dens_color)
-  }
-
-  else if (attr(data, "theory_type") %in%
-          c("One sample prop z", "Two sample props z")) {
+  } else if (attr(data, "theory_type") %in%
+             c("One sample prop z", "Two sample props z")) {
+    
     infer_plot <- theory_z_plot(statistic_text = "z",
                                 dens_color = dens_color)
-  }
+  } else if (attr(data, "theory_type") %in%
+             c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
 
-  else if (attr(data, "theory_type") %in%
-          c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
-
-    if (!is.null(direction) && !(direction %in% c("greater", "right")))
+    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
       warning_glue("Chi-square usually corresponds to right-tailed tests. ",
                    "Proceed with caution.")
+    }
 
     infer_plot <- theory_chisq_plot(deg_freedom = attr(data, "distr_param"),
                                     statistic_text = "Chi-Square",
                                     dens_color = dens_color)
-  }
-
-#  else
-#    stop_glue(
-#      '"{attr(data, "theory_type")}" is not implemented (possibly yet).'
-#    )
+  } # else {
+    # stop_glue(
+    #   '"{attr(data, "theory_type")}" is not implemented (possibly yet).'
+    # )
+  # }
 
   # Move into its own function
-
   if (!is.null(obs_stat)) {
     if (!is.null(direction)) {
       if (direction %in% c("less", "left")) {
@@ -599,18 +584,17 @@ visualize_both <- function(data, bins,
                            pvalue_fill,
                            endpoints,
                            ci_fill, ...) {
-
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. `infer` currently does not check these for you."
   )
 
-  if (!(attr(data, "stat") %in% c("t", "z", "Chisq", "F")))
+  if (!(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))) {
     stop_glue("Your `calculate`d statistic and the theoretical distribution ",
               "are on different scales. Use a standardized `stat` instead.")
+  }
 
   if (attr(data, "theory_type") %in% c("Two sample t", "Slope with t")) {
-
     infer_plot <- both_t_plot(data = data,
                               deg_freedom = attr(data, "distr_param"),
                               statistic_text = "t",
@@ -621,9 +605,7 @@ visualize_both <- function(data, bins,
                               pvalue_fill = pvalue_fill,
                               endpoints = endpoints,
                               ci_fill = ci_fill)
-  }
-
-  else if (attr(data, "theory_type") == "ANOVA") {
+  } else if (attr(data, "theory_type") == "ANOVA") {
     infer_plot <- both_anova_plot(
       data = data,
       deg_freedom_top = attr(data, "distr_param"),
@@ -636,10 +618,8 @@ visualize_both <- function(data, bins,
       pvalue_fill = pvalue_fill,
       endpoints = endpoints,
       ci_fill = ci_fill)
-  }
-
-  else if (attr(data, "theory_type") %in%
-          c("One sample prop z", "Two sample props z")) {
+  } else if (attr(data, "theory_type") %in%
+             c("One sample prop z", "Two sample props z")) {
     infer_plot <- both_z_plot(data = data,
                               statistic_text = "z",
                               dens_color = dens_color,
@@ -649,11 +629,10 @@ visualize_both <- function(data, bins,
                               pvalue_fill = pvalue_fill,
                               endpoints = endpoints,
                               ci_fill = ci_fill)
-  }
-
-  else if (
+  } else if (
     attr(data, "theory_type") %in%
-    c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
+    c("Chi-square test of indep", "Chi-square Goodness of Fit")
+  ) {
     infer_plot <- both_chisq_plot(data = data,
                                   deg_freedom = attr(data, "distr_param"),
                                   statistic_text = "Chi-Square",
@@ -664,10 +643,9 @@ visualize_both <- function(data, bins,
                                   pvalue_fill = pvalue_fill,
                                   endpoints = endpoints,
                                   ci_fill = ci_fill)
-  }
-
-#  else
-#    stop_glue('"{attr(data, "theory_type")}" is not implemented yet.')
+  } # else {
+    # stop_glue('"{attr(data, "theory_type")}" is not implemented yet.')
+  # }
 
   infer_plot
 }
diff --git a/R/wrappers.R b/R/wrappers.R
index d3c034e4..17c54eee 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -37,17 +37,16 @@ t_test <- function(data, formula, # response = NULL, explanatory = NULL,
                    conf_int = TRUE,
                    conf_level = 0.95,
                    ...) {
-
   check_conf_level(conf_level)
 
   # Match with old "dot" syntax
-  if (alternative == "two_sided")
+  if (alternative == "two_sided") {
     alternative <- "two.sided"
+  }
 
   ### Only currently working with formula interface
 #  if (hasArg(formula)) {
   if (!is.null(f_rhs(formula))) {
-
     data[[as.character(f_rhs(formula))]] <-
       factor(data[[as.character(f_rhs(formula))]],
              levels = c(order[1], order[2]))
@@ -139,12 +138,12 @@ t_stat <- function(data, formula, ...) {
 #' @export
 chisq_test <- function(data, formula, # response = NULL, explanatory = NULL,
                        ...) {
-
-  if (is.null(f_rhs(formula)))
+  if (is.null(f_rhs(formula))) {
     stop_glue(
       "`chisq_test()` currently only has functionality for ",
       "Chi-Square Test of Independence, not for Chi-Square Goodness of Fit."
     )
+  }
   ## Only currently working with formula interface
   explanatory_var <- f_rhs(formula)
   response_var <- f_lhs(formula)
@@ -168,7 +167,6 @@ chisq_test <- function(data, formula, # response = NULL, explanatory = NULL,
 #'
 #' @export
 chisq_stat <- function(data, formula, ...) {
-
   if (is.null(f_rhs(formula))) {
     stop_glue(
       "`chisq_stat()` currently only has functionality for ",
@@ -182,10 +180,10 @@ chisq_stat <- function(data, formula, ...) {
   }
 }
 
-
 check_conf_level <- function(conf_level) {
   if (class(conf_level) != "numeric" |
-     conf_level < 0 |
-     conf_level > 1)
+      conf_level < 0 |
+      conf_level > 1) {
     stop_glue("The `conf_level` argument must be a number between 0 and 1.")
+  }
 }
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 34856237..f682ff99 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -226,7 +226,6 @@ test_that("chi-square matches chisq.test value", {
                                             p = c(0.8, 0.1, 0.1)))) %>%
     dplyr::select(replicate, stat = statistic)
   expect_equal(infer_way, trad_way)
-
 })
 
 test_that("`order` is working", {
@@ -256,7 +255,6 @@ test_that("`order` is working", {
                          order = c(">5", "<=4", ">4")))
   # order not given
   expect_error(calculate(gen_iris11, stat = "diff in means"))
-
 })
 
 test_that('success is working for stat = "prop"', {
@@ -270,7 +268,6 @@ test_that('success is working for stat = "prop"', {
                   calculate(stat = "prop"))
   expect_silent(gen_iris12 %>%
                   calculate(stat = "z"))
-
 })
 
 test_that("NULL response gives error", {
@@ -348,11 +345,9 @@ test_that("One sample t hypothesis test is working", {
       generate(reps = 10) %>%
       calculate(stat = "t")
   )
-
 })
 
 test_that("specify done before calculate", {
-
   iris_mean <- iris_tbl %>%
     dplyr::select(stat = Sepal.Width)
   expect_error(calculate(iris_mean, stat = "mean"))
@@ -392,5 +387,4 @@ test_that("One sample t bootstrap is working", {
       generate(reps = 10) %>%
       calculate(stat = "t")
   )
-
 })
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
index 6dcc63ae..6413db4a 100644
--- a/tests/testthat/test-conf_int.R
+++ b/tests/testthat/test-conf_int.R
@@ -13,7 +13,7 @@ iris_calc <- iris_tbl %>%
   generate(reps = 1000) %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
-obs_diff <-  iris_tbl %>%
+obs_diff <- iris_tbl %>%
   specify(Sepal.Length.Group ~ Sepal.Width.Group,
           success = "<=5") %>%
   calculate(stat = "diff in props", order = c("large", "small"))
@@ -25,7 +25,7 @@ test_that("basics work", {
   expect_silent(
     test_df %>%
       conf_int()
-    )
+  )
   expect_error(
     test_df %>%
       conf_int(type = "other")
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index 02ed4b43..e92d5f77 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -2,10 +2,10 @@ context("generate")
 
 mtcars <- as.data.frame(mtcars) %>%
   dplyr::mutate(cyl = factor(cyl),
-         vs = factor(vs),
-         am = factor(am),
-         gear = factor(gear),
-         carb = factor(carb))
+                vs = factor(vs),
+                am = factor(am),
+                gear = factor(gear),
+                carb = factor(carb))
 
 hyp_prop <- mtcars %>%
   specify(response = am, success = "1") %>%
@@ -44,7 +44,6 @@ hyp_anova <- mtcars %>%
   hypothesize(null = "independence")
 
 test_that("cohesion with type argument", {
-
   expect_error(generate(hyp_prop, type = "bootstrap"))
   expect_error(generate(hyp_diff_in_props, type = "bootstrap"))
   expect_error(generate(hyp_chisq_gof, type = "bootstrap"))
@@ -70,11 +69,9 @@ test_that("cohesion with type argument", {
   expect_error(generate(hyp_mean, type = "permute"))
   expect_silent(generate(hyp_diff_in_means, type = "permute"))
   expect_silent(generate(hyp_anova, type = "permute"))
-
 })
 
 test_that("sensible output", {
-
   expect_equal(nrow(mtcars) * 500,
                nrow(generate(hyp_prop, reps = 500, type = "simulate")))
   expect_silent(generate(hyp_mean, reps = 1, type = "bootstrap"))
@@ -154,86 +151,85 @@ test_that("auto `type` works (generate)", {
     specify(mpg ~ hp) %>%
     generate(reps = 100)
 
-    expect_equal(attr(one_mean, "type"), "bootstrap")
-    expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")
-    expect_equal(attr(one_median, "type"), "bootstrap")
-    expect_equal(attr(one_prop, "type"), "simulate")
-    expect_equal(attr(two_props, "type"), "permute")
-    expect_equal(attr(gof_chisq, "type"), "simulate")
-    expect_equal(attr(indep_chisq, "type"), "permute")
-    expect_equal(attr(two_means, "type"), "permute")
-    expect_equal(attr(anova_f, "type"), "permute")
-    expect_equal(attr(slopes, "type"), "permute")
-    expect_equal(attr(one_nonshift_prop, "type"), "bootstrap")
-    expect_equal(attr(two_means_boot, "type"), "bootstrap")
-    expect_equal(attr(two_props_boot, "type"), "bootstrap")
-    expect_equal(attr(slope_boot, "type"), "bootstrap")
-
-    expect_error(mtcars %>%
-      specify(response = mpg) %>% # formula alt: mpg ~ NULL
-      hypothesize(null = "point", mu = 25) %>%
-      generate(reps = 100, type = "permute"))
-
-    expect_error(mtcars %>%
-      specify(response = mpg) %>%
-      generate(reps = 100, type = "simulate"))
-
-    expect_error(mtcars %>%
-      specify(response = mpg) %>% # formula alt: mpg ~ NULL
-      hypothesize(null = "point", med = 26) %>%
-      generate(reps = 100, type = "permute"))
-
-    expect_error(mtcars %>%
-      specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-      hypothesize(null = "point", p = .25) %>%
-      generate(reps = 100, type = "bootstrap"))
-
-    expect_error(mtcars %>%
-      specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
-      hypothesize(null = "independence") %>%
-      generate(reps = 100, type = "bootstrap"))
-
-    expect_error(mtcars %>%
-      specify(cyl ~ NULL) %>% # alt: response = cyl
-      hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-      generate(reps = 100, type = "bootstrap"))
-
-    expect_error(mtcars %>%
-      specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
-      hypothesize(null = "independence") %>%
-      generate(reps = 100, type = "simulate"))
-
-    expect_error(mtcars %>%
-      specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-      hypothesize(null = "independence") %>%
-      generate(reps = 100, type = "bootstrap"))
-
-    expect_error(mtcars %>%
-      specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
-      hypothesize(null = "independence") %>%
-      generate(reps = 100, type = "simulate"))
-
-    expect_error(mtcars %>%
-      specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
-      hypothesize(null = "independence") %>%
-      generate(reps = 100, type = "bootstrap"))
-
-    expect_error(mtcars %>%
-      specify(response = am, success = "1") %>%
-      generate(reps = 100, type = "simulate"))
-
-    expect_error(mtcars %>%
-      specify(mpg ~ am) %>%
-      generate(reps = 100, type = "permute"))
-
-    expect_error(mtcars %>%
-      specify(am ~ vs, success = "1") %>%
-      generate(reps = 100, type = "simulate"))
-
-    expect_error(mtcars %>%
-      specify(mpg ~ hp) %>%
-      generate(reps = 100, type = "simulate"))
+  expect_equal(attr(one_mean, "type"), "bootstrap")
+  expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")
+  expect_equal(attr(one_median, "type"), "bootstrap")
+  expect_equal(attr(one_prop, "type"), "simulate")
+  expect_equal(attr(two_props, "type"), "permute")
+  expect_equal(attr(gof_chisq, "type"), "simulate")
+  expect_equal(attr(indep_chisq, "type"), "permute")
+  expect_equal(attr(two_means, "type"), "permute")
+  expect_equal(attr(anova_f, "type"), "permute")
+  expect_equal(attr(slopes, "type"), "permute")
+  expect_equal(attr(one_nonshift_prop, "type"), "bootstrap")
+  expect_equal(attr(two_means_boot, "type"), "bootstrap")
+  expect_equal(attr(two_props_boot, "type"), "bootstrap")
+  expect_equal(attr(slope_boot, "type"), "bootstrap")
+
+  expect_error(mtcars %>%
+    specify(response = mpg) %>% # formula alt: mpg ~ NULL
+    hypothesize(null = "point", mu = 25) %>%
+    generate(reps = 100, type = "permute"))
+
+  expect_error(mtcars %>%
+    specify(response = mpg) %>%
+    generate(reps = 100, type = "simulate"))
+
+  expect_error(mtcars %>%
+    specify(response = mpg) %>% # formula alt: mpg ~ NULL
+    hypothesize(null = "point", med = 26) %>%
+    generate(reps = 100, type = "permute"))
 
+  expect_error(mtcars %>%
+    specify(response = am, success = "1") %>% # formula alt: am ~ NULL
+    hypothesize(null = "point", p = .25) %>%
+    generate(reps = 100, type = "bootstrap"))
+
+  expect_error(mtcars %>%
+    specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
+    hypothesize(null = "independence") %>%
+    generate(reps = 100, type = "bootstrap"))
+
+  expect_error(mtcars %>%
+    specify(cyl ~ NULL) %>% # alt: response = cyl
+    hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
+    generate(reps = 100, type = "bootstrap"))
+
+  expect_error(mtcars %>%
+    specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
+    hypothesize(null = "independence") %>%
+    generate(reps = 100, type = "simulate"))
+
+  expect_error(mtcars %>%
+    specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
+    hypothesize(null = "independence") %>%
+    generate(reps = 100, type = "bootstrap"))
+
+  expect_error(mtcars %>%
+    specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
+    hypothesize(null = "independence") %>%
+    generate(reps = 100, type = "simulate"))
+
+  expect_error(mtcars %>%
+    specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
+    hypothesize(null = "independence") %>%
+    generate(reps = 100, type = "bootstrap"))
+
+  expect_error(mtcars %>%
+    specify(response = am, success = "1") %>%
+    generate(reps = 100, type = "simulate"))
+
+  expect_error(mtcars %>%
+    specify(mpg ~ am) %>%
+    generate(reps = 100, type = "permute"))
+
+  expect_error(mtcars %>%
+    specify(am ~ vs, success = "1") %>%
+    generate(reps = 100, type = "simulate"))
+
+  expect_error(mtcars %>%
+    specify(mpg ~ hp) %>%
+    generate(reps = 100, type = "simulate"))
 })
 
 test_that("mismatches lead to error", {
diff --git a/tests/testthat/test-hypothesize.R b/tests/testthat/test-hypothesize.R
index 717e0a78..3ffbc004 100644
--- a/tests/testthat/test-hypothesize.R
+++ b/tests/testthat/test-hypothesize.R
@@ -2,10 +2,10 @@ context("hypothesize")
 
 mtcars <- as.data.frame(mtcars) %>%
   dplyr::mutate(cyl = factor(cyl),
-         vs = factor(vs),
-         am = factor(am),
-         gear = factor(gear),
-         carb = factor(carb))
+                vs = factor(vs),
+                am = factor(am),
+                gear = factor(gear),
+                carb = factor(carb))
 
 one_mean <- mtcars %>%
   specify(response = mpg) %>% # formula alt: mpg ~ NULL
@@ -67,7 +67,6 @@ test_that("auto `type` works (hypothesize)", {
 })
 
 test_that("hypothesize arguments function", {
-
   mtcars_f <- dplyr::mutate(mtcars, cyl = factor(cyl))
   mtcars_s <- mtcars_f %>% specify(response = mpg)
   matrix1 <- matrix(data = NA, nrow = 3, ncol = 3)
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index 75e6ff4b..7ea6d232 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -20,7 +20,7 @@ test_that("direction is appropriate", {
   expect_error(
     test_df %>%
       p_value(obs_stat = 0.5, direction = "righ")
-    )
+  )
 })
 
 test_that("p_value makes sense", {
@@ -29,7 +29,7 @@ test_that("p_value makes sense", {
       p_value(obs_stat = 0.1, direction = "right") %>%
       dplyr::pull(),
     expected = 0.1
-    )
+  )
   expect_gt(
     iris_calc %>%
       p_value(obs_stat = -0.1, direction = "greater") %>%
diff --git a/tests/testthat/test-rep_sample_n.R b/tests/testthat/test-rep_sample_n.R
index 8e9f9544..a6e0eca9 100644
--- a/tests/testthat/test-rep_sample_n.R
+++ b/tests/testthat/test-rep_sample_n.R
@@ -10,8 +10,8 @@ test_that("rep_sample_n works", {
   expect_silent(population %>%
                   rep_sample_n(size = 2, reps = 10))
   expect_error(population %>%
-                  rep_sample_n(size = 2, reps = 10,
-                               prob = rep(x = 1/5, times = 100)))
+                 rep_sample_n(size = 2, reps = 10,
+                              prob = rep(x = 1/5, times = 100)))
   expect_error(population %>%
                  rep_sample_n(size = 2, reps = 10,
                               prob = c(1/2, 1/2)))
@@ -21,6 +21,4 @@ test_that("rep_sample_n works", {
   test_rep <- population %>%
     rep_sample_n(size = 2, reps = 10)
   expect_equal(c("replicate", names(population)), names(test_rep))
-
-
 })
diff --git a/tests/testthat/test-specify.R b/tests/testthat/test-specify.R
index 3aec3e63..f858e3be 100644
--- a/tests/testthat/test-specify.R
+++ b/tests/testthat/test-specify.R
@@ -31,7 +31,6 @@ test_that("auto `type` works (specify)", {
 })
 
 test_that("data argument", {
-
   expect_error(specify(blah ~ cyl))
   expect_error(specify(1:3))
   expect_is(mtcars, "data.frame")
@@ -39,7 +38,6 @@ test_that("data argument", {
 })
 
 test_that("response and explanatory arguments", {
-
   expect_error(specify(mtcars, response = blah))
   expect_error(specify(mtcars, response = "blah"))
   expect_error(specify(mtcars, formula = mpg ~ blah))
@@ -54,7 +52,6 @@ test_that("response and explanatory arguments", {
 })
 
 test_that("success argument", {
-
   expect_error(specify(mtcars, response = vs, success = 1))
   expect_error(specify(mtcars, response = vs, success = "bogus"))
   expect_error(specify(mtcars, response = mpg, success = "1"))
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index fce2f025..6973527d 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -63,32 +63,29 @@ test_that("visualize basic tests", {
 
   # obs_stat not specified
   expect_error(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "diff in props",
-                            order = c(">5", "<=5")) %>%
-                  visualize(direction = "both")
-                )
-
-  expect_silent(iris_tbl %>%
                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
                          success = "large") %>%
                  hypothesize(null = "independence") %>%
                  generate(reps = 100, type = "permute") %>%
                  calculate(stat = "diff in props",
                            order = c(">5", "<=5")) %>%
-                 visualize(direction = "both", obs_stat = obs_diff)
-  )
+                 visualize(direction = "both"))
 
-  expect_warning(iris_tbl %>%
+  expect_silent(iris_tbl %>%
                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
                           success = "large") %>%
                   hypothesize(null = "independence") %>%
-                  calculate(stat = "z", order = c(">5", "<=5")) %>%
-                  visualize(method = "theoretical")
-  )
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "diff in props",
+                            order = c(">5", "<=5")) %>%
+                  visualize(direction = "both", obs_stat = obs_diff))
+
+  expect_warning(iris_tbl %>%
+                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
+                   calculate(stat = "z", order = c(">5", "<=5")) %>%
+                   visualize(method = "theoretical"))
 
   # diff in props and z on different scales
   expect_error(expect_warning(iris_tbl %>%
@@ -103,137 +100,123 @@ test_that("visualize basic tests", {
   ))
 
   expect_silent(iris_tbl %>%
-                 specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>%
-                 hypothesize(null = "independence") %>%
-                 generate(reps = 100, type = "permute") %>%
-                 calculate(stat = "diff in props",
-                           order = c(">5", "<=5")) %>%
-                 visualize()
-  )
-
-  expect_warning(iris_tbl %>%
-                 specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>%
-                 hypothesize(null = "independence") %>%
-                 generate(reps = 100, type = "permute") %>%
-                 calculate(stat = "z",
-                           order = c(">5", "<=5")) %>%
-                 visualize(method = "both", direction = "both",
-                           obs_stat = obs_z)
-  )
+                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
+                          success = "large") %>%
+                  hypothesize(null = "independence") %>%
+                  generate(reps = 100, type = "permute") %>%
+                  calculate(stat = "diff in props",
+                            order = c(">5", "<=5")) %>%
+                  visualize())
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
+                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
                           success = "large") %>%
                   hypothesize(null = "independence") %>%
                   generate(reps = 100, type = "permute") %>%
                   calculate(stat = "z",
-                            order = c("<=5", ">5")) %>%
+                            order = c(">5", "<=5")) %>%
                   visualize(method = "both", direction = "both",
-                            obs_stat = -obs_z)
-  )
+                            obs_stat = obs_z))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Sepal.Width.Group) %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "t", order = c("small", "large")) %>%
-                  visualize(method = "both", direction = "left",
-                            obs_stat = -obs_t)
-  )
+                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "z",
+                             order = c("<=5", ">5")) %>%
+                   visualize(method = "both", direction = "both",
+                             obs_stat = -obs_z))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Sepal.Width.Group) %>%
-                  hypothesize(null = "independence") %>%
- #                 generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "t", order = c("small", "large")) %>%
-                  visualize(method = "theoretical", direction = "left",
-                            obs_stat = -obs_t)
-  )
+                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "t", order = c("small", "large")) %>%
+                   visualize(method = "both", direction = "left",
+                             obs_stat = -obs_t))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Sepal.Length.Group) %>%
-                  hypothesize(null = "independence") %>%
-                  visualize(method = "theoretical")
-  )
+                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
+                   hypothesize(null = "independence") %>%
+#                    generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "t", order = c("small", "large")) %>%
+                   visualize(method = "theoretical", direction = "left",
+                             obs_stat = -obs_t))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Species) %>%
-                  hypothesize(null = "independence") %>%
-                  visualize(method = "theoretical")
-  )
+                   specify(Sepal.Length ~ Sepal.Length.Group) %>%
+                   hypothesize(null = "independence") %>%
+                   visualize(method = "theoretical"))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Species) %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "F") %>%
-                  visualize(method = "both", obs_stat = obs_F,
-                            direction = "right")
-  )
+                   specify(Sepal.Length ~ Species) %>%
+                   hypothesize(null = "independence") %>%
+                   visualize(method = "theoretical"))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Length ~ Species) %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "F") %>%
-                  visualize(method = "both", obs_stat = obs_F,
-                            direction = "left")
-  )
+                   specify(Sepal.Length ~ Species) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "F") %>%
+                   visualize(method = "both", obs_stat = obs_F,
+                             direction = "right"))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Species,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "Chisq") %>%
-                  visualize(method = "both", obs_stat = obs_F,
-                            direction = "right")
-  )
+                   specify(Sepal.Length ~ Species) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "F") %>%
+                   visualize(method = "both", obs_stat = obs_F,
+                             direction = "left"))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Species,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  # calculate(stat = "Chisq") %>%
-                  visualize(method = "theoretical", obs_stat = obs_F,
-                            direction = "right")
-  )
+                   specify(Sepal.Width.Group ~ Species,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "Chisq") %>%
+                   visualize(method = "both", obs_stat = obs_F,
+                             direction = "right"))
 
   expect_warning(iris_tbl %>%
-                  specify(Species ~ NULL) %>%
-                  hypothesize(null = "point",
-                              p = c("setosa" = 0.4,
-                                    "versicolor" = 0.4,
-                                    "virginica" = 0.2)) %>%
-                  generate(reps = 100, type = "simulate") %>%
-                  calculate(stat = "Chisq") %>%
-                  visualize(method = "both")
-  )
+                   specify(Sepal.Width.Group ~ Species,
+                           success = "large") %>%
+                   hypothesize(null = "independence") %>%
+                   # calculate(stat = "Chisq") %>%
+                   visualize(method = "theoretical", obs_stat = obs_F,
+                             direction = "right"))
+
+  expect_warning(iris_tbl %>%
+                   specify(Species ~ NULL) %>%
+                   hypothesize(null = "point",
+                               p = c("setosa" = 0.4,
+                                     "versicolor" = 0.4,
+                                     "virginica" = 0.2)) %>%
+                   generate(reps = 100, type = "simulate") %>%
+                   calculate(stat = "Chisq") %>%
+                   visualize(method = "both"))
 
   # traditional instead of theoretical
   expect_error(iris_tbl %>%
-                  specify(Species ~ NULL) %>%
-                  hypothesize(null = "point",
-                              p = c("setosa" = 0.4,
-                                    "versicolor" = 0.4,
-                                    "virginica" = 0.2)) %>%
-#                  generate(reps = 100, type = "simulate") %>%
-#                  calculate(stat = "Chisq") %>%
-                  visualize(method = "traditional")
-  )
-
-  expect_warning(iris_tbl %>%
                  specify(Species ~ NULL) %>%
                  hypothesize(null = "point",
                              p = c("setosa" = 0.4,
                                    "versicolor" = 0.4,
                                    "virginica" = 0.2)) %>%
-                 # generate(reps = 100, type = "simulate") %>%
-                 # calculate(stat = "Chisq") %>%
-                 visualize(method = "theoretical")
-  )
+#                 generate(reps = 100, type = "simulate") %>%
+#                 calculate(stat = "Chisq") %>%
+                 visualize(method = "traditional"))
+
+  expect_warning(iris_tbl %>%
+                   specify(Species ~ NULL) %>%
+                   hypothesize(null = "point",
+                               p = c("setosa" = 0.4,
+                                     "versicolor" = 0.4,
+                                     "virginica" = 0.2)) %>%
+                   # generate(reps = 100, type = "simulate") %>%
+                   # calculate(stat = "Chisq") %>%
+                   visualize(method = "theoretical"))
 
   expect_silent(iris_tbl %>%
                   specify(Petal.Width ~ Sepal.Width.Group) %>%
@@ -257,24 +240,22 @@ test_that("visualize basic tests", {
   ))
 
   expect_warning(iris_tbl %>%
-                 specify(Petal.Width ~ Sepal.Width.Group) %>%
-                 hypothesize(null = "independence") %>%
-                 generate(reps = 100, type = "permute") %>%
-                 calculate(stat = "diff in means",
-                           order = c("large", "small")) %>%
-                 visualize(method = "theoretical", direction = "both",
-                           obs_stat = obs_diff_mean)
-  )
+                   specify(Petal.Width ~ Sepal.Width.Group) %>%
+                   hypothesize(null = "independence") %>%
+                   generate(reps = 100, type = "permute") %>%
+                   calculate(stat = "diff in means",
+                             order = c("large", "small")) %>%
+                   visualize(method = "theoretical", direction = "both",
+                             obs_stat = obs_diff_mean))
 
   expect_warning(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ NULL, success = "small") %>%
-                  hypothesize(null = "point", p = 0.8) %>%
-#                 generate(reps = 100, type = "simulate") %>%
-#                  calculate(stat = "z") %>%
-                  visualize(method = "theoretical",
-                            obs_stat = 2, # Should probably update
-                            direction = "both")
-  )
+                   specify(Sepal.Width.Group ~ NULL, success = "small") %>%
+                   hypothesize(null = "point", p = 0.8) %>%
+#                    generate(reps = 100, type = "simulate") %>%
+#                    calculate(stat = "z") %>%
+                   visualize(method = "theoretical",
+                             obs_stat = 2, # Should probably update
+                             direction = "both"))
 
   expect_silent(iris_tbl %>%
                   specify(Petal.Width ~ NULL) %>%
@@ -282,10 +263,7 @@ test_that("visualize basic tests", {
                   generate(reps = 100, type = "bootstrap") %>%
                   calculate(stat = "mean") %>%
                   visualize(direction = "left",
-                            obs_stat = mean(iris$Petal.Width))
-  )
-
-
+                            obs_stat = mean(iris$Petal.Width)))
 })
 
 test_that("get_percentile works", {
@@ -297,21 +275,18 @@ test_that("obs_stat as a data.frame works", {
                   specify(Petal.Width ~ NULL) %>%
                   calculate(stat = "mean")
   expect_silent(iris_tbl %>%
-                 specify(Petal.Width ~ NULL) %>%
-                 hypothesize(null = "point", mu = 4) %>%
+                  specify(Petal.Width ~ NULL) %>%
+                  hypothesize(null = "point", mu = 4) %>%
                   generate(reps = 100, type = "bootstrap") %>%
-                 calculate(stat = "mean") %>%
-                 visualize(obs_stat = mean_petal_width)
-  )
+                  calculate(stat = "mean") %>%
+                  visualize(obs_stat = mean_petal_width))
   mean_df_test <- data.frame(x = c(4.1, 1), y = c(1, 2))
   expect_warning(iris_tbl %>%
                    specify(Petal.Width ~ NULL) %>%
                    hypothesize(null = "point", mu = 4) %>%
                    generate(reps = 100, type = "bootstrap") %>%
                    calculate(stat = "mean") %>%
-                   visualize(obs_stat = mean_df_test)
-                 )
-
+                   visualize(obs_stat = mean_df_test))
 })
 
 
@@ -322,17 +297,15 @@ test_that('method = "both" behaves nicely', {
                  specify(Petal.Width ~ NULL) %>%
                  hypothesize(null = "point", mu = 4) %>%
                  generate(reps = 100, type = "bootstrap") %>%
-                 #    calculate(stat = "mean") %>%
+#                  calculate(stat = "mean") %>%
                  visualize(method = "both"))
 
-  #
   expect_warning(iris_tbl %>%
                    specify(Petal.Width ~ Sepal.Length.Group) %>%
                    hypothesize(null = "point", mu = 4) %>%
                    generate(reps = 10, type = "bootstrap") %>%
                    calculate(stat = "t", order = c(">5", "<=5")) %>%
-                   visualize(method = "both")
-  )
+                   visualize(method = "both"))
 })
 
 test_that("Traditional right-tailed tests have warning if not right-tailed", {
@@ -342,38 +315,32 @@ test_that("Traditional right-tailed tests have warning if not right-tailed", {
                    hypothesize(null = "independence") %>%
                    generate(reps = 100, type = "permute") %>%
                    calculate(stat = "Chisq") %>%
-                   visualize(method = "both", obs_stat = 2, direction = "left")
-                 )
+                   visualize(method = "both", obs_stat = 2, direction = "left"))
   expect_warning(iris_tbl %>%
                    specify(Sepal.Length ~ Species) %>%
                    hypothesize(null = "independence") %>%
                    generate(reps = 100, type = "permute") %>%
                    calculate(stat = "F") %>%
                    visualize(method = "both", obs_stat = 2,
-                             direction = "two_sided")
-  )
+                             direction = "two_sided"))
   expect_warning(iris_tbl %>%
                    specify(Sepal.Width.Group ~ Species,
                            success = "large") %>%
                    hypothesize(null = "independence") %>%
- #                  generate(reps = 100, type = "permute") %>%
+#                    generate(reps = 100, type = "permute") %>%
                    calculate(stat = "Chisq") %>%
                    visualize(method = "theoretical", obs_stat = 2,
-                             direction = "left")
-  )
+                             direction = "left"))
   expect_warning(iris_tbl %>%
                    specify(Sepal.Length ~ Species) %>%
                    hypothesize(null = "independence") %>%
-  #                 generate(reps = 100, type = "permute") %>%
+#                    generate(reps = 100, type = "permute") %>%
                    calculate(stat = "F") %>%
                    visualize(method = "theoretical", obs_stat = 2,
-                             direction = "two_sided")
-  )
-
+                             direction = "two_sided"))
 })
 
 test_that("confidence interval plots are working", {
-
   iris_boot <- iris_tbl %>%
     specify(Sepal.Width.Group ~ Sepal.Length.Group,
             success = "large") %>%
@@ -402,5 +369,4 @@ test_that("confidence interval plots are working", {
   expect_warning(
     iris_boot %>% visualize(obs_stat = 3, endpoints = perc_ci)
   )
-
 })
diff --git a/tests/testthat/test-wrappers.R b/tests/testthat/test-wrappers.R
index 81c058a8..028ebaa7 100644
--- a/tests/testthat/test-wrappers.R
+++ b/tests/testthat/test-wrappers.R
@@ -47,7 +47,7 @@ test_that("_stat functions work", {
   one_more <- chisq.test(
     table(iris3$Species,
                 iris3$Sepal.Length.Group)
-    )$statistic
+  )$statistic
 
   expect_equivalent(another_way, obs_stat_way)
   expect_equivalent(one_more, dplyr::pull(obs_stat_way))
@@ -66,7 +66,7 @@ test_that("_stat functions work", {
   expect_silent(
     iris2 %>% t_stat(Sepal.Width ~ Species,
                                  order = c("virginica", "versicolor"))
-    )
+  )
   another_way <- iris2 %>%
     t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor")) %>%
     dplyr::select(statistic)
@@ -89,7 +89,7 @@ test_that("conf_int argument works", {
     names(iris2 %>%
       t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor"),
              conf_int = FALSE)),
-      c("statistic", "t_df", "p_value", "alternative")
+    c("statistic", "t_df", "p_value", "alternative")
   )
   expect_equal(
     names(iris2 %>%
@@ -137,5 +137,4 @@ test_that("conf_int argument works", {
   expect_false(
     shortcut_no_var_equal == shortcut_var_equal
   )
-
 })

From ea190f90b3344687aefa5d200202ee55b6546b39 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 16:53:52 +0300
Subject: [PATCH 05/78] Update long lines (manually).

---
 R/calculate.R                      | 218 ++++++-----
 R/conf_int.R                       |  14 +-
 R/generate.R                       |  55 +--
 R/hypothesize.R                    |  24 +-
 R/infer.R                          |  14 +-
 R/p_value.R                        |  44 ++-
 R/rep_sample_n.R                   |  14 +-
 R/set_params.R                     |  72 ++--
 R/specify.R                        |  57 ++-
 R/utils.R                          |  99 +++--
 R/visualize.R                      | 555 +++++++++++++++-----------
 R/wrappers.R                       |  83 ++--
 tests/testthat/test-calculate.R    | 229 ++++++-----
 tests/testthat/test-conf_int.R     |  51 +--
 tests/testthat/test-generate.R     |  89 +++--
 tests/testthat/test-hypothesize.R  |  68 ++--
 tests/testthat/test-p_value.R      |  22 +-
 tests/testthat/test-print.R        |  10 +-
 tests/testthat/test-rep_sample_n.R |  28 +-
 tests/testthat/test-specify.R      |  24 +-
 tests/testthat/test-visualize.R    | 603 +++++++++++++++--------------
 tests/testthat/test-wrappers.R     |  96 +++--
 22 files changed, 1372 insertions(+), 1097 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 041c4080..488b3db0 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -29,19 +29,9 @@
 #' @export
 calculate <- function(x,
                       stat = c(
-                        "mean",
-                        "median",
-                        "sd",
-                        "prop",
-                        "diff in means",
-                        "diff in medians",
-                        "diff in props",
-                        "Chisq",
-                        "F",
-                        "slope",
-                        "correlation",
-                        "t",
-                        "z"
+                        "mean", "median", "sd", "prop", "diff in means",
+                        "diff in medians", "diff in props", "Chisq", "F",
+                        "slope", "correlation", "t", "z"
                       ),
                       order = NULL,
                       ...) {
@@ -63,15 +53,8 @@ calculate <- function(x,
       x$replicate <- 1L
     } else if (
       stat %in% c(
-        "mean",
-        "median",
-        "sd",
-        "prop",
-        "diff in means",
-        "diff in medians",
-        "diff in props",
-        "slope",
-        "correlation"
+        "mean", "median", "sd", "prop", "diff in means", "diff in medians",
+        "diff in props", "slope", "correlation"
       )
     ) {
       stop_glue(
@@ -113,13 +96,13 @@ calculate <- function(x,
   }
 
   # Use S3 method to match correct calculation
-  result <- calc_impl(structure(stat, class = gsub(" ", "_", stat)),
-                      x, order, ...)
+  result <- calc_impl(
+    structure(stat, class = gsub(" ", "_", stat)), x, order, ...
+  )
 
   if ("NULL" %in% class(result)) {
     stop_glue(
-      "Your choice of `stat` is invalid for the ",
-      "types of variables `specify`ed."
+      "Your choice of `stat` is invalid for the types of variables `specify`ed."
     )
   }
 #   else {
@@ -141,7 +124,6 @@ calc_impl <- function(type, x, order, ...) {
   UseMethod("calc_impl", type)
 }
 
-
 calc_impl.mean <- function(stat, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
 
@@ -179,44 +161,45 @@ calc_impl.prop <- function(stat, x, order, ...) {
 
   if (is.null(attr(x, "success"))) {
     stop_glue(
-      'To calculate a proportion, the `"success"` argument ',
-      'must be provided in `specify()`.'
+      'To calculate a proportion, the `"success"` argument must be provided ',
+      'in `specify()`.'
     )
   }
 
   success <- attr(x, "success")
   x %>%
     dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = mean(!!sym(col) == success,
-           # rlang::eval_tidy(col) == rlang::eval_tidy(success),
-                                 ...))
+    dplyr::summarize(
+      stat = mean(
+        # rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
+        !!sym(col) == success, ...
+      )
+    )
 }
 
-
 calc_impl.F <- function(stat, x, order, ...) {
   x %>%
-    dplyr::summarize(stat = stats::anova(stats::lm(!!(
-      attr(x, "response")
-    ) ~ !!(
-      attr(x, "explanatory")
-    )))$`F value`[1])
+    dplyr::summarize(
+      stat = stats::anova(
+        stats::lm(!!(attr(x, "response")) ~ !!(attr(x, "explanatory")))
+      )$`F value`[1]
+    )
 }
 
-
-
 calc_impl.slope <- function(stat, x, order, ...) {
   x %>%
-    dplyr::summarize(stat = stats::coef(stats::lm(!!(
-      attr(x, "response")
-    ) ~ !!(
-      attr(x, "explanatory")
-    )))[2])
+    dplyr::summarize(
+      stat = stats::coef(
+        stats::lm(!!(attr(x, "response")) ~ !!(attr(x, "explanatory")))
+      )[2]
+    )
 }
 
 calc_impl.correlation <- function(stat, x, order, ...) {
   x %>%
-    dplyr::summarize(stat = stats::cor(!!attr(x, "explanatory"),
-                                !!attr(x, "response")))
+    dplyr::summarize(
+      stat = stats::cor(!!attr(x, "explanatory"), !!attr(x, "response"))
+    )
 }
 
 calc_impl.diff_in_means <- function(stat, x, order, ...) {
@@ -224,18 +207,21 @@ calc_impl.diff_in_means <- function(stat, x, order, ...) {
     dplyr::group_by(replicate, !!attr(x, "explanatory")) %>%
     dplyr::summarize(xbar = mean(!!attr(x, "response"), ...)) %>%
     dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = xbar[!!(attr(x, "explanatory")) == order[1]]
-                     - xbar[!!(attr(x, "explanatory")) == order[2]])
+    dplyr::summarize(
+      stat = xbar[!!(attr(x, "explanatory")) == order[1]] -
+        xbar[!!(attr(x, "explanatory")) == order[2]]
+    )
 }
 
 calc_impl.diff_in_medians <- function(stat, x, order, ...) {
   x %>%
     dplyr::group_by(replicate, !!(attr(x, "explanatory"))) %>%
-    dplyr::summarize(xtilde =
-                       stats::median(!!attr(x, "response"), ...)) %>%
+    dplyr::summarize(xtilde = stats::median(!!attr(x, "response"), ...)) %>%
     dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = xtilde[!!(attr(x, "explanatory")) == order[1]]
-                     - xtilde[!!(attr(x, "explanatory")) == order[2]])
+    dplyr::summarize(
+      stat = xtilde[!!(attr(x, "explanatory")) == order[1]] -
+        xtilde[!!(attr(x, "explanatory")) == order[2]]
+    )
 }
 
 calc_impl.Chisq <- function(stat, x, order, ...) {
@@ -246,36 +232,47 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     if (!is.null(attr(x, "params"))) {
       # When `hypothesize()` has been called
       x %>%
-       dplyr::summarize(stat = stats::chisq.test(table(!!(
-         attr(x, "response")
-       )), p = attr(x, "params"))$stat)
+        dplyr::summarize(
+          stat = stats::chisq.test(
+            table(!!(attr(x, "response"))), p = attr(x, "params")
+          )$stat
+        )
     } else {
       # Straight from `specify()`
-        stop_glue("In order to calculate a Chi-Square Goodness of Fit ",
-                  "statistic, hypothesized values must be given for the `p` ",
-                  "parameter in the `hypothesize()` function prior to ",
-                  "using `calculate()`")
+      stop_glue(
+        "In order to calculate a Chi-Square Goodness of Fit statistic, ",
+        "hypothesized values must be given for the `p` parameter in the ",
+        "`hypothesize()` function prior to using `calculate()`"
+      )
     }
   } else {
     # This is not matching with chisq.test
     # obs_tab <- x %>%
     #   dplyr::filter(replicate == 1) %>%
     #   dplyr::ungroup() %>%
-    #   dplyr::select(!!attr(x, "response"),
-    #                 !!(attr(x, "explanatory"))) %>%
+    #   dplyr::select(!!attr(x, "response"), !!(attr(x, "explanatory"))) %>%
     #   table()
     # expected <- outer(rowSums(obs_tab), colSums(obs_tab)) / n
     # df_out <- x %>%
-    #   dplyr::summarize(stat = sum((table(!!(attr(x, "response")),
-    #                                      !!(attr(x, "explanatory")))
-    #                                - expected)^2 / expected, ...))
+    #   dplyr::summarize(
+    #     stat = sum(
+    #       (table(!!(attr(x, "response")), !!(attr(x, "explanatory"))) -
+    #          expected)^2 / expected,
+    #       ...)
+    #   )
 
     # Chi-Square Test of Independence
     result <- x %>%
-      dplyr::do(broom::tidy(suppressWarnings(stats::chisq.test(table(
-        .[[as.character(attr(x, "response"))]],
-        .[[as.character(attr(x, "explanatory"))]]
-      ))))) %>%
+      dplyr::do(
+        broom::tidy(
+          suppressWarnings(stats::chisq.test(
+            table(
+              .[[as.character(attr(x, "response"))]],
+              .[[as.character(attr(x, "explanatory"))]]
+            )
+          ))
+        )
+      ) %>%
       dplyr::ungroup()
 
     if (!is.null(attr(x, "generate"))) {
@@ -304,8 +301,10 @@ calc_impl.diff_in_props <- function(stat, x, order, ...) {
   x %>%
     dplyr::group_by(replicate, !!attr(x, "explanatory")) %>%
     dplyr::summarize(prop = mean(!!sym(col) == success, ...)) %>%
-    dplyr::summarize(stat = prop[!!attr(x, "explanatory") == order[1]]
-                     - prop[!!attr(x, "explanatory") == order[2]])
+    dplyr::summarize(
+      stat = prop[!!attr(x, "explanatory") == order[1]] -
+        prop[!!attr(x, "explanatory") == order[2]]
+    )
 }
 
 calc_impl.t <- function(stat, x, order, ...) {
@@ -316,33 +315,39 @@ calc_impl.t <- function(stat, x, order, ...) {
     x <- reorder_explanatory(x, order)
 
     df_out <- x %>%
-      dplyr::summarize(stat = stats::t.test(
-        !!attr(x, "response") ~ !!attr(x, "explanatory"), ...
-      )[["statistic"]])
+      dplyr::summarize(
+        stat = stats::t.test(
+          !!attr(x, "response") ~ !!attr(x, "explanatory"), ...
+        )[["statistic"]]
+      )
   }
 
   # Standardized slope and standardized correlation are commented out
   # since there currently is no way to specify which one and
   # the standardization formulas are different.
   # # Standardized slope
-  # else if ( (attr(x, "theory_type") == "Slope/correlation with t") &&
-  #           stat == "slope"){
+  # else if (
+  #   (attr(x, "theory_type") == "Slope/correlation with t") && stat == "slope"
+  # ) {
   #   explan_string <- as.character(attr(x, "explanatory"))
-  #
+  # 
   #   x %>%
-  #     dplyr::summarize(stat = summary(stats::lm(
-  #       !!attr(x, "response") ~ !!attr(x, "explanatory")
-  #     ))[["coefficients"]][explan_string, "t value"])
+  #     dplyr::summarize(
+  #       stat = summary(stats::lm(
+  #         !!attr(x, "response") ~ !!attr(x, "explanatory")
+  #       ))[["coefficients"]][explan_string, "t value"]
+  #     )
   # }
-  #
+  # 
   # # Standardized correlation
-  # else if ( (attr(x, "theory_type") == "Slope/correlation with t") &&
-  #           stat == "correlation"){
-  #
+  # else if (
+  #   (attr(x, "theory_type") == "Slope/correlation with t") &&
+  #   stat == "correlation"
+  # ) {
   #   x %>%
-  #     dplyr::summarize(corr = cor(!!attr(x, "explanatory"),
-  #                                 !!attr(x, "response"))
-  #                      ) %>%
+  #     dplyr::summarize(
+  #       corr = cor(!!attr(x, "explanatory"), !!attr(x, "response"))
+  #     ) %>%
   #     dplyr::mutate(stat = corr * (sqrt(nrow(x) - 2)) / sqrt(1 - corr ^ 2))
   # }
 
@@ -352,16 +357,16 @@ calc_impl.t <- function(stat, x, order, ...) {
     if (is.null(attr(x, "null"))) {
       x %>%
         dplyr::summarize(
-          stat = stats::t.test(!!attr(x, "response"),
-                               ...
-                               )[["statistic"]])
+          stat = stats::t.test(!!attr(x, "response"), ...)[["statistic"]]
+        )
     } else {
       # For hypothesis testing
       x %>%
-        dplyr::summarize(stat = stats::t.test(
-          !!attr(x, "response"),
-          mu = attr(x, "params"),
-          ...)[["statistic"]])
+        dplyr::summarize(
+          stat = stats::t.test(
+            !!attr(x, "response"), mu = attr(x, "params"), ...
+          )[["statistic"]]
+        )
     }
   }
 }
@@ -372,29 +377,26 @@ calc_impl.z <- function(stat, x, order, ...) {
     col <- attr(x, "response")
     success <- attr(x, "success")
 
-    x$explan <- factor(explanatory_variable(x),
-                       levels = c(order[1], order[2]))
+    x$explan <- factor(
+      explanatory_variable(x), levels = c(order[1], order[2])
+    )
 
     aggregated <- x %>%
       dplyr::group_by(replicate, explan) %>%
       dplyr::summarize(
         group_num = n(),
-        prop = mean(rlang::eval_tidy(col) ==
-                      rlang::eval_tidy(success)),
-        num_suc = sum(rlang::eval_tidy(col) ==
-                        rlang::eval_tidy(success))
+        prop = mean(rlang::eval_tidy(col) == rlang::eval_tidy(success)),
+        num_suc = sum(rlang::eval_tidy(col) == rlang::eval_tidy(success))
       )
 
     df_out <- aggregated %>%
       dplyr::summarize(
-        diff_prop = prop[explan == order[1]]
-                  - prop[explan == order[2]],
+        diff_prop = prop[explan == order[1]] - prop[explan == order[2]],
         total_suc = sum(num_suc),
         n1 = group_num[1],
         n2 = group_num[2],
         p_hat = total_suc / (n1 + n2),
-        denom = sqrt(p_hat * (1 - p_hat) / n1
-                     + p_hat * (1 - p_hat) / n2),
+        denom = sqrt(p_hat * (1 - p_hat) / n1 + p_hat * (1 - p_hat) / n2),
         stat = diff_prop / denom
       ) %>%
       dplyr::select(-total_suc, -n1, -n2)
@@ -416,9 +418,11 @@ calc_impl.z <- function(stat, x, order, ...) {
 #     Error given instead
 
     df_out <- x %>%
-      dplyr::summarize(stat = (mean(
-        rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
-      ) - p0) / sqrt((p0 * (1 - p0)) / num_rows))
+      dplyr::summarize(
+        stat = (
+          mean(rlang::eval_tidy(col) == rlang::eval_tidy(success), ...) - p0
+        ) / sqrt((p0 * (1 - p0)) / num_rows)
+      )
 
     df_out
 
diff --git a/R/conf_int.R b/R/conf_int.R
index 6fbadc06..089fdff9 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -40,8 +40,10 @@ conf_int <- function(x, level = 0.95, type = "percentile",
   check_ci_args(x, level, type, point_estimate)
 
   if (type == "percentile") {
-    ci_vec <- stats::quantile(x[["stat"]],
-                      probs = c((1 - level) / 2, level + (1 - level) / 2))
+    ci_vec <- stats::quantile(
+      x[["stat"]],
+      probs = c((1 - level) / 2, level + (1 - level) / 2)
+    )
 
     ci <- tibble::tibble(ci_vec[1], ci_vec[2])
     names(ci) <- names(ci_vec)
@@ -50,7 +52,8 @@ conf_int <- function(x, level = 0.95, type = "percentile",
     multiplier <- stats::qnorm(1 - (1 - level) / 2)
     ci <- tibble::tibble(
       lower = point_estimate - multiplier * stats::sd(x[["stat"]]),
-      upper = point_estimate + multiplier * stats::sd(x[["stat"]]))
+      upper = point_estimate + multiplier * stats::sd(x[["stat"]])
+    )
   }
 
   return(ci)
@@ -75,8 +78,9 @@ check_ci_args <- function(x, level, type, point_estimate) {
   }
 
   if (type == "se" && is.null(point_estimate)) {
-    stop_glue('A numeric value needs to be given for `point_estimate` ',
-              'for `type = "se"')
+    stop_glue(
+      'A numeric value needs to be given for `point_estimate` for `type = "se"'
+    )
   }
 
   if (type == "se" && is.vector(point_estimate)) {
diff --git a/R/generate.R b/R/generate.R
index 9f10c488..ed0c475d 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -41,23 +41,32 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
   attr(x, "generate") <- TRUE
 
-  if (type == "permute" &&
-      any(is.null(attr(x, "response")), is.null(attr(x, "explanatory")))) {
-    stop_glue("Please `specify()` an explanatory and a response variable ",
-              "when permuting.")
+  if (
+    type == "permute" &&
+    any(is.null(attr(x, "response")), is.null(attr(x, "explanatory")))
+  ) {
+    stop_glue(
+      "Please `specify()` an explanatory and a response variable when ",
+      "permuting."
+    )
   }
 ## Can't get to these anymore with tests
-#  if (type == "simulate" &&
-#      attr(x, "null") != "point" &&
-#      !(length(grep("p.", names(attr(x, "params")))) >= 1)) {
-#    stop_glue("Simulation requires a `point` null hypothesis on proportions.")
-#  }
-#  if (type == "bootstrap" &&
-#        !(attr(attr(x, "params"), "names") %in% c("mu", "med", "sigma")) &&
-#        !is.null(attr(x, "null"))
-#      ) {
-#    stop_glue("Bootstrapping is inappropriate in this setting. ",
-#              "Consider using `type = permute` or `type = simulate`.")
+#   if (
+#     type == "simulate" &&
+#     attr(x, "null") != "point" &&
+#     !(length(grep("p.", names(attr(x, "params")))) >= 1)
+#   ) {
+#     stop_glue("Simulation requires a `point` null hypothesis on proportions.")
+#   }
+#   if (
+#     type == "bootstrap" &&
+#     !(attr(attr(x, "params"), "names") %in% c("mu", "med", "sigma")) &&
+#     !is.null(attr(x, "null"))
+#   ) {
+#     stop_glue(
+#       "Bootstrapping is inappropriate in this setting. ",
+#       "Consider using `type = permute` or `type = simulate`."
+#     )
 #  }
 
   if (type == "bootstrap") {
@@ -67,8 +76,10 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
   } else if (type == "simulate") {
     return(simulate(x, reps, ...))
   } # else if (!(type %in% c("bootstrap", "permute", "simulate"))) {
-    # stop_glue("Choose one of the available options for `type`: ",
-    #           '`"bootstrap"`, `"permute"`, or `"simulate"`')
+    # stop_glue(
+    #   "Choose one of the available options for `type`: ",
+    #   '`"bootstrap"`, `"permute"`, or `"simulate"`'
+    # )
   # }
 }
 
@@ -154,11 +165,11 @@ permute_once <- function(x, ...) {
 simulate <- function(x, reps = 1, ...) {
   fct_levels <- as.character(unique(dplyr::pull(x, !!attr(x, "response"))))
 
-  col_simmed <- unlist(replicate(reps, sample(fct_levels,
-                                              size = nrow(x),
-                                              replace = TRUE,
-                                              prob = format_params(x)),
-                                 simplify = FALSE))
+  col_simmed <- unlist(replicate(
+    reps,
+    sample(fct_levels, size = nrow(x), replace = TRUE, prob = format_params(x)),
+    simplify = FALSE
+  ))
 
   rep_tbl <- tibble::tibble(
     !!attr(x, "response") := as.factor(col_simmed),
diff --git a/R/hypothesize.R b/R/hypothesize.R
index af9a6b5c..48cd20dc 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -26,13 +26,17 @@ hypothesize <- function(x, null, ...) {
   dots <- list(...)
 
   if ((null == "point") && (length(dots) == 0)) {
-    stop_glue("Provide a parameter and a value to check such as `mu = 30` ",
-              "for the point hypothesis.")
+    stop_glue(
+      "Provide a parameter and a value to check such as `mu = 30` for the ",
+      "point hypothesis."
+    )
   }
 
   if ((null == "independence") && (length(dots) > 0)) {
-    warning_glue("Parameter values are not specified when testing that two ",
-                 "variables are independent.")
+    warning_glue(
+      "Parameter values are not specified when testing that two variables are ",
+      "independent."
+    )
   }
 
   if ((length(dots) > 0) && (null == "point")) {
@@ -56,8 +60,10 @@ hypothesize <- function(x, null, ...) {
   if (null == "point") {
     if (is.factor(response_variable(x))) {
       if (!any(grepl("p", attr(attr(x, "params"), "names")))) {
-        stop_glue('Testing one categorical variable requires `p` ',
-                  'to be used as a parameter.')
+        stop_glue(
+          'Testing one categorical variable requires `p` to be used as a ',
+          'parameter.'
+        )
       }
     }
   }
@@ -70,8 +76,10 @@ hypothesize <- function(x, null, ...) {
   #     !is.factor(response_variable(x))
   #     & !any(grepl("mu|med|sigma", attr(attr(x, "params"), "names")))
   #   ) {
-  #     stop_glue('Testing one numerical variable requires one of ',
-  #               '`mu`, `med`, or `sd` to be used as a parameter.')
+  #     stop_glue(
+  #       'Testing one numerical variable requires one of ',
+  #       '`mu`, `med`, or `sd` to be used as a parameter.'
+  #     )
   #   }
   # }
 
diff --git a/R/infer.R b/R/infer.R
index 084a98c8..a6621b57 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -15,10 +15,12 @@ NULL
 ## quiets concerns of R CMD check re: the .'s that appear in pipelines
 ## From Jenny Bryan's googlesheets package
 if (getRversion() >= "2.15.1") {
-  utils::globalVariables(c("prop", "stat", "xbar", "xtilde", "x", "..density..",
-                           "statistic", ".", "parameter", "p.value",
-                           "xmin", "xmax", "density", "denom",
-                           "diff_prop", "group_num", "n1", "n2",
-                           "num_suc", "p_hat", "total_suc", "explan", "probs",
-                           "conf.low", "conf.high"))
+  utils::globalVariables(
+    c(
+      "prop", "stat", "xbar", "xtilde", "x", "..density..", "statistic", ".",
+      "parameter", "p.value", "xmin", "xmax", "density", "denom", "diff_prop",
+      "group_num", "n1", "n2", "num_suc", "p_hat", "total_suc", "explan",
+      "probs", "conf.low", "conf.high"
+    )
+  )
 }
diff --git a/R/p_value.R b/R/p_value.R
index fe349ede..09026d02 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -36,12 +36,12 @@ p_value <- function(x, obs_stat, direction) {
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
 
-  is_simulation_based <- !is.null(attr(x, "generate")) &&
-    attr(x, "generate")
+  is_simulation_based <- !is.null(attr(x, "generate")) && attr(x, "generate")
 
   if (is_simulation_based) {
-    pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
-                                       direction = direction)
+    pvalue <- simulation_based_p_value(
+      x = x, obs_stat = obs_stat, direction = direction
+    )
   }
 
   ## Theoretical-based p-value
@@ -49,16 +49,20 @@ p_value <- function(x, obs_stat, direction) {
   # else if (
   #   is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param"))
   # ) {
-  #   stop_glue("Attributes have not been set appropriately. ",
-  #             "Check your {{infer}} pipeline again.")
+  #   stop_glue(
+  #     "Attributes have not been set appropriately. ",
+  #     "Check your {{infer}} pipeline again."
+  #   )
   # }
-
+  #
   # if (!("stat" %in% names(x))) {
-  #    # Theoretical distribution
-  #  which_distribution(x,
-  #                     theory_type <- attr(x, "theory_type"),
-  #                     obs_stat = obs_stat,
-  #                     direction = direction)
+  #   # Theoretical distribution
+  #   which_distribution(
+  #     x,
+  #     theory_type = attr(x, "theory_type"),
+  #     obs_stat = obs_stat,
+  #     direction = direction
+  #   )
   # }
 
   return(pvalue)
@@ -81,12 +85,15 @@ simulation_based_p_value <- function(x, obs_stat, direction) {
 two_sided_p_value <- function(x, obs_stat) {
   if (stats::median(x$stat) >= obs_stat) {
     basic_p_value <- get_percentile(x$stat, obs_stat) +
-      (1 - get_percentile(x$stat, stats::median(x$stat) +
-                            stats::median(x$stat) - obs_stat))
+      (1 - get_percentile(
+        x$stat, stats::median(x$stat) + stats::median(x$stat) - obs_stat
+        )
+      )
   } else {
     basic_p_value <- 1 - get_percentile(x$stat, obs_stat) +
-      (get_percentile(x$stat, stats::median(x$stat) +
-                        stats::median(x$stat) - obs_stat))
+      get_percentile(
+        x$stat, stats::median(x$stat) + stats::median(x$stat) - obs_stat
+      )
   }
 
   if (basic_p_value >= 1) {
@@ -110,9 +117,8 @@ get_pvalue <- p_value
 #   }
 # 
 #   if (theory_type == "Two sample t") {
-#     return(pt(q = obs_stat,
-#               df = param,
-#               lower.tail = set_lower_tail(direction))
+#     return(
+#       pt(q = obs_stat, df = param, lower.tail = set_lower_tail(direction))
 #     )
 #   }
 # }
diff --git a/R/rep_sample_n.R b/R/rep_sample_n.R
index 0dc80c99..23e813be 100644
--- a/R/rep_sample_n.R
+++ b/R/rep_sample_n.R
@@ -70,11 +70,15 @@ rep_sample_n <- function(tbl, size, replace = FALSE, reps = 1, prob = NULL) {
       dplyr::pull()
   }
 
-  i <- unlist(replicate(reps, sample.int(n, size, replace = replace,
-                                         prob = prob),
-                        simplify = FALSE))
-  rep_tbl <- cbind(replicate = rep(1:reps, rep(size, reps)),
-                   tbl[i, ])
+  i <- unlist(replicate(
+    reps,
+    sample.int(n, size, replace = replace, prob = prob),
+    simplify = FALSE
+  ))
+  rep_tbl <- cbind(
+    replicate = rep(1:reps, rep(size, reps)),
+    tbl[i, ]
+  )
   rep_tbl <- tibble::as_tibble(rep_tbl)
   names(rep_tbl)[-1] <- names(tbl)
   dplyr::group_by(rep_tbl, replicate)
diff --git a/R/set_params.R b/R/set_params.R
index 827d90b1..4a741843 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -11,16 +11,18 @@ set_params <- function(x) {
   }
 
   # One variable
-  if (!is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
-      !is.null(attr(x, "response_type")) &&
-      is.null(attr(x, "explanatory_type"))) {
+  if (
+    !is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
+    !is.null(attr(x, "response_type")) &&
+    is.null(attr(x, "explanatory_type"))
+  ) {
 
     # One mean
     if (attr(x, "response_type") %in% c("integer", "numeric")) {
       attr(x, "theory_type") <- "One sample t"
       attr(x, "distr_param") <- x %>%
-        dplyr::summarize(df = stats::t.test(
-          response_variable(x))[["parameter"]]
+        dplyr::summarize(
+          df = stats::t.test(response_variable(x))[["parameter"]]
         ) %>%
         dplyr::pull()
       attr(x, "type") <- "bootstrap"
@@ -41,14 +43,18 @@ set_params <- function(x) {
   }
 
   # Two variables
-  if (!is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
-      !is.null(attr(x, "response_type")) &&
-      !is.null(attr(x, "explanatory_type"))) {
+  if (
+    !is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
+    !is.null(attr(x, "response_type")) &&
+    !is.null(attr(x, "explanatory_type"))
+  ) {
     attr(x, "type") <- "bootstrap"
 
     # Response is numeric, explanatory is categorical
-    if (attr(x, "response_type") %in% c("integer", "numeric") &
-        attr(x, "explanatory_type") == "factor") {
+    if (
+      attr(x, "response_type") %in% c("integer", "numeric") &
+      attr(x, "explanatory_type") == "factor"
+    ) {
       
       # Two sample means (t distribution)
       if (length(levels(explanatory_variable(x))) == 2) {
@@ -56,8 +62,10 @@ set_params <- function(x) {
         # Keep track of Satterthwaite degrees of freedom since lost when
         # in aggregation w/ calculate()/generate()
         attr(x, "distr_param") <- x %>%
-          dplyr::summarize(df = stats::t.test(
-            !!attr(x, "response") ~ !!attr(x, "explanatory"))[["parameter"]]
+          dplyr::summarize(
+            df = stats::t.test(
+              !!attr(x, "response") ~ !!attr(x, "explanatory")
+            )[["parameter"]]
           ) %>%
           dplyr::pull()
       } else {
@@ -66,43 +74,57 @@ set_params <- function(x) {
         attr(x, "theory_type") <- "ANOVA"
         # Get numerator and denominator degrees of freedom
         attr(x, "distr_param") <- x %>%
-          dplyr::summarize(df1 = stats::anova(stats::aov(
-            !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[1]
+          dplyr::summarize(
+            df1 = stats::anova(stats::aov(
+              !!attr(x, "response") ~ !!attr(x, "explanatory")
+            ))$Df[1]
           ) %>%
           dplyr::pull()
         attr(x, "distr_param2") <- x %>%
-          dplyr::summarize(df2 = stats::anova(stats::aov(
-            !!attr(x, "response") ~ !!attr(x, "explanatory")))$Df[2]
+          dplyr::summarize(
+            df2 = stats::anova(stats::aov(
+              !!attr(x, "response") ~ !!attr(x, "explanatory")
+            ))$Df[2]
           ) %>%
           dplyr::pull()
       }
     }
 
     # Response is categorical, explanatory is categorical
-    if (attr(x, "response_type") == "factor" &
-        attr(x, "explanatory_type") == "factor") {
+    if (
+      attr(x, "response_type") == "factor" &
+      attr(x, "explanatory_type") == "factor"
+    ) {
       attr(x, "type") <- "bootstrap"
 
       # Two sample proportions (z distribution)
       # Parameter(s) not needed since standard normal
-      if (length(levels(response_variable(x))) == 2 &
-          length(levels(explanatory_variable(x))) == 2) {
+      if (
+        length(levels(response_variable(x))) == 2 &
+        length(levels(explanatory_variable(x))) == 2
+      ) {
         attr(x, "theory_type") <- "Two sample props z"
       } else {
         
         # >2 sample proportions (chi-square test of indep)
         attr(x, "theory_type") <- "Chi-square test of indep"
         attr(x, "distr_param") <- x %>%
-          dplyr::summarize(df = suppressWarnings(stats::chisq.test(
-            table(response_variable(x),
-                  explanatory_variable(x)))$parameter)) %>%
+          dplyr::summarize(
+            df = suppressWarnings(
+              stats::chisq.test(
+                table(response_variable(x), explanatory_variable(x))
+              )$parameter
+            )
+          ) %>%
           dplyr::pull()
       }
     }
 
     # Response is numeric, explanatory is numeric
-    if (attr(x, "response_type") %in% c("integer", "numeric") &
-        attr(x, "explanatory_type") %in% c("integer", "numeric")) {
+    if (
+      attr(x, "response_type") %in% c("integer", "numeric") &
+      attr(x, "explanatory_type") %in% c("integer", "numeric")
+    ) {
       response_string <- as.character(attr(x, "response"))
       explanatory_string <- as.character(attr(x, "explanatory"))
       attr(x, "theory_type") <- "Slope/correlation with t"
diff --git a/R/specify.R b/R/specify.R
index f0e33193..1293be5c 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -61,8 +61,10 @@ specify <- function(x, formula, response = NULL,
   }
 
   if (!(as.character(attr(x, "response")) %in% names(x))) {
-    stop_glue('The response variable `{attr(x, "response")}` ',
-              'cannot be found in this dataframe.')
+    stop_glue(
+      'The response variable `{attr(x, "response")}` cannot be found in this ',
+      'dataframe.'
+    )
   }
 
   response_col <- rlang::eval_tidy(attr(x, "response"), x)
@@ -70,13 +72,20 @@ specify <- function(x, formula, response = NULL,
   # if there's an explanatory var
   if (has_explanatory(x)) {
     if (!as.character(attr(x, "explanatory")) %in% names(x)) {
-      stop_glue('The explanatory variable `{attr(x, "explanatory")}` ',
-                'cannot be found in this dataframe.')
+      stop_glue(
+        'The explanatory variable `{attr(x, "explanatory")}` cannot be found ',
+        'in this dataframe.'
+      )
     }
-    if (identical(as.character(attr(x, "response")),
-                  as.character(attr(x, "explanatory")))) {
-      stop_glue("The response and explanatory variables must be different ",
-                "from one another.")
+    if (
+      identical(
+        as.character(attr(x, "response")), as.character(attr(x, "explanatory"))
+      )
+    ) {
+      stop_glue(
+        "The response and explanatory variables must be different from one ",
+        "another."
+      )
     }
     explanatory_col <- rlang::eval_tidy(attr(x, "explanatory"), x)
     if (is.character(explanatory_col)) {
@@ -91,22 +100,25 @@ specify <- function(x, formula, response = NULL,
       stop_glue("`success` must be a string.")
     }
     if (!is.factor(response_col)) {
-      stop_glue("`success` should only be specified if the response is ",
-                "a categorical variable.")
+      stop_glue(
+        "`success` should only be specified if the response is a categorical ",
+        "variable."
+      )
     }
     if (!(success %in% levels(response_col))) {
       stop_glue('{success} is not a valid level of {attr(x, "response")}.')
     }
     if (sum(table(response_col) > 0) > 2) {
-      stop_glue("`success` can only be used if the response has two levels. ",
-                "`filter()` can reduce a variable to two levels.")
+      stop_glue(
+        "`success` can only be used if the response has two levels. ",
+        "`filter()` can reduce a variable to two levels."
+      )
     }
   }
 
   x <- x %>%
     select(one_of(c(
-      as.character((attr(x, "response"))),
-      as.character(attr(x, "explanatory"))
+      as.character((attr(x, "response"))), as.character(attr(x, "explanatory"))
     )))
 
   is_complete <- stats::complete.cases(x)
@@ -115,7 +127,6 @@ specify <- function(x, formula, response = NULL,
     warning_glue("Removed {sum(!is_complete)} rows containing missing values.")
   }
 
-
   # To help determine theoretical distribution to plot
   attr(x, "response_type") <- class(response_variable(x))
 
@@ -125,14 +136,18 @@ specify <- function(x, formula, response = NULL,
     attr(x, "explanatory_type") <- class(explanatory_variable(x))
   }
 
-  if (attr(x, "response_type") == "factor" && is.null(success) &&
-      length(levels(response_variable(x))) == 2 &&
-      (is.null(attr(x, "explanatory_type")) ||
+  if (
+    attr(x, "response_type") == "factor" && is.null(success) &&
+    length(levels(response_variable(x))) == 2 &&
+    (
+      is.null(attr(x, "explanatory_type")) ||
       (!is.null(attr(x, "explanatory_type")) &&
-      length(levels(explanatory_variable(x))) == 2))) {
+       length(levels(explanatory_variable(x))) == 2)
+    )
+  ) {
     stop_glue(
-      'A level of the response variable `{attr(x, "response")}` ',
-      'needs to be specified for the `success` argument in `specify()`.'
+      'A level of the response variable `{attr(x, "response")}` needs to be ',
+      'specified for the `success` argument in `specify()`.'
     )
   }
 
diff --git a/R/utils.R b/R/utils.R
index 69a77962..26c7d307 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -35,9 +35,10 @@ response_variable <- function(x) {
 }
 
 reorder_explanatory <- function(x, order) {
-  x[[as.character(attr(x, "explanatory"))]] <-
-    factor(x[[as.character(attr(x, "explanatory"))]],
-           levels = c(order[1], order[2]))
+  x[[as.character(attr(x, "explanatory"))]] <- factor(
+    x[[as.character(attr(x, "explanatory"))]],
+    levels = c(order[1], order[2])
+  )
   x
 }
 
@@ -92,14 +93,18 @@ null_transformer <- function(text, envir) {
 check_order <- function(x, explanatory_variable, order) {
   unique_explanatory_variable <- unique(explanatory_variable)
   if (length(unique_explanatory_variable) != 2) {
-    stop_glue("Statistic is based on a difference; the explanatory variable ",
-              "should have two levels.")
+    stop_glue(
+      "Statistic is based on a difference; the explanatory variable should ",
+      "have two levels."
+    )
   }
   if (is.null(order)) {
-    stop_glue("Statistic is based on a difference; specify the `order` in ",
-              "which to subtract the levels of the explanatory variable. ",
-              '`order = c("first", "second")` means `("first" - "second")`. ',
-              "Check `?calculate` for details.")
+    stop_glue(
+      "Statistic is based on a difference; specify the `order` in which to ",
+      "subtract the levels of the explanatory variable. ",
+      '`order = c("first", "second")` means `("first" - "second")`. ',
+      "Check `?calculate` for details."
+    )
   } else {
     if (xor(is.na(order[1]), is.na(order[2]))) {
       stop_glue(
@@ -122,16 +127,23 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
                                 stat) {
   # Could also do `stat <- match.arg(stat)`
   # but that's not as helpful to beginners with the cryptic error msg
-  if (!stat %in% c("mean", "median", "sd", "prop",
-                   "diff in means", "diff in medians", "diff in props",
-                   "Chisq", "F", "slope", "correlation", "t", "z")) {
-    stop_glue("You specified a string for `stat` that is not implemented. ",
-              "Check your spelling and `?calculate` for current options.")
+  if (
+    !stat %in% c(
+      "mean", "median", "sd", "prop", "diff in means", "diff in medians",
+      "diff in props", "Chisq", "F", "slope", "correlation", "t", "z"
+    )
+  ) {
+    stop_glue(
+      "You specified a string for `stat` that is not implemented. ",
+      "Check your spelling and `?calculate` for current options."
+    )
   }
 
   if (!("replicate" %in% names(x)) && !is.null(attr(x, "generate"))) {
-    warning_glue('A `generate()` step was not performed prior to ',
-                 '`calculate()`. Review carefully.')
+    warning_glue(
+      'A `generate()` step was not performed prior to `calculate()`. ',
+      'Review carefully.'
+    )
   }
 
   if (stat %in% c("F", "slope", "diff in means", "diff in medians")) {
@@ -218,8 +230,9 @@ parse_params <- function(dots, x) {
   sig_ind <- grep("sigma", names(dots))
 
   # error: cannot specify more than one of props, means, medians, or sds
-  if (length(p_ind) + length(mu_ind) + length(med_ind)
-      + length(sig_ind) != 1) {
+  if (
+    length(p_ind) + length(mu_ind) + length(med_ind) + length(sig_ind) != 1
+  ) {
     stop_glue(
       'Parameter values can be only one of `p`, `mu`, `med`, or `sigma`.'
     )
@@ -231,22 +244,28 @@ parse_params <- function(dots, x) {
   if (length(p_ind)) {
     if (length(dots[[p_ind]]) == 1) {
       if (attr(x, "null") == "point" && is.null(attr(x, "success"))) {
-        stop_glue("A point null regarding a proportion requires ",
-                  "that `success` be indicated in `specify()`.")
+        stop_glue(
+          "A point null regarding a proportion requires that `success` ",
+          "be indicated in `specify()`."
+        )
       }
       if (dots$p < 0 || dots$p > 1) {
         stop_glue(
           "The value suggested for `p` is not between 0 and 1, inclusive."
         )
       }
-      missing_lev <- base::setdiff(unique(pull(x, !!attr(x, "response"))),
-                                   attr(x, "success"))
+      missing_lev <- base::setdiff(
+        unique(dplyr::pull(x, !!attr(x, "response"))),
+        attr(x, "success")
+      )
       dots$p <- append(dots$p, 1 - dots$p)
       names(dots$p) <- c(attr(x, "success"), missing_lev)
     } else {
       if (sum(dots$p) != 1) {
-        stop_glue("Make sure the hypothesized values for the `p` parameters ",
-                  "sum to 1. Please try again.")
+        stop_glue(
+          "Make sure the hypothesized values for the `p` parameters sum to 1. ",
+          "Please try again."
+        )
       }
     }
   }
@@ -273,8 +292,10 @@ hypothesize_checks <- function(x, null) {
   }
 
    # if (length(null) != 1) {
-   #   stop_glue('Choose between either `"independence"` or `"point"` ',
-   #             'for `null` argument.')
+   #   stop_glue(
+   #     'Choose between either `"independence"` or `"point"` for `null` ',
+   #     'argument.'
+   #    )
    # }
 
   if (!has_response(x)) {
@@ -284,9 +305,11 @@ hypothesize_checks <- function(x, null) {
   }
 
   if (null == "independence" && !has_explanatory(x)) {
-    stop_glue('Please `specify()` an explanatory and a response variable ',
-              'when testing\n',
-              'a null hypothesis of `"independence"`.')
+    stop_glue(
+      'Please `specify()` an explanatory and a response variable when ',
+      'testing\n',
+      'a null hypothesis of `"independence"`.'
+    )
   }
 }
 
@@ -294,11 +317,13 @@ check_direction <- function(direction = c("less", "greater", "two_sided",
                                           "left", "right", "both")) {
   check_type(direction, is.character)
 
-  if (!(direction %in% c("less", "greater", "two_sided",
-                        "left", "right", "both"))) {
-    stop_glue('The provided value for `direction` is not appropriate. ',
-              'Possible values are "less", "greater", "two_sided", ',
-              '"left", "right", or "both".')
+  if (
+    !(direction %in% c("less", "greater", "two_sided", "left", "right", "both"))
+  ) {
+    stop_glue(
+      'The provided value for `direction` is not appropriate. Possible values ',
+      'are "less", "greater", "two_sided", "left", "right", or "both".'
+    )
   }
 }
 
@@ -307,8 +332,10 @@ check_obs_stat <- function(obs_stat) {
     if ("data.frame" %in% class(obs_stat)) {
       check_type(obs_stat, is.data.frame)
       if ((nrow(obs_stat) != 1) || (ncol(obs_stat) != 1)) {
-        warning_glue("The first row and first column value of the given ",
-                     "`obs_stat` will be used.")
+        warning_glue(
+          "The first row and first column value of the given `obs_stat` will ",
+          "be used."
+        )
       }
 
       # [[1]] is used in case `stat` is not specified as name of 1x1
diff --git a/R/visualize.R b/R/visualize.R
index 121dbf7d..100cd5a3 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -77,7 +77,8 @@ visualize <- function(data, bins = 15, method = "simulation",
                       direction = NULL,
                       endpoints = NULL,
                       endpoints_color = "mediumaquamarine",
-                      ci_fill = "turquoise", ...) {
+                      ci_fill = "turquoise",
+                      ...) {
   check_type(data, is.data.frame)
   check_type(bins, is.numeric)
   check_type(method, is.character)
@@ -87,8 +88,10 @@ visualize <- function(data, bins = 15, method = "simulation",
   if (!is.null(direction)) {
     check_type(direction, is.character)
   }
-  if (is.data.frame(endpoints) &&
-      ((nrow(endpoints) != 1) || (ncol(endpoints) != 2))) {
+  if (
+    is.data.frame(endpoints) &&
+    ((nrow(endpoints) != 1) || (ncol(endpoints) != 2))
+  ) {
     stop_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
     )
@@ -104,8 +107,10 @@ visualize <- function(data, bins = 15, method = "simulation",
     endpoints <- unlist(endpoints)
   }
   obs_stat <- check_obs_stat(obs_stat)
-  if (!is.null(direction) &&
-      (is.null(obs_stat) + is.null(endpoints)) != 1) {
+  if (
+    !is.null(direction) &&
+    (is.null(obs_stat) + is.null(endpoints)) != 1
+  ) {
     stop_glue(
       "Shading requires either `endpoints` values for a confidence interval ",
       "or the observed statistic `obs_stat` to be provided."
@@ -113,52 +118,64 @@ visualize <- function(data, bins = 15, method = "simulation",
   }
 
   if (method == "simulation") {
-    infer_plot <- visualize_simulation(data = data, bins = bins,
-                                       dens_color = dens_color,
-                                       obs_stat = obs_stat,
-                                       obs_stat_color = obs_stat_color,
-                                       direction = direction,
-                                       pvalue_fill = pvalue_fill,
-                                       endpoints = endpoints,
-                                       ci_fill = ci_fill,
-                                       ...)
+    infer_plot <- visualize_simulation(
+      data = data,
+      bins = bins,
+      dens_color = dens_color,
+      obs_stat = obs_stat,
+      obs_stat_color = obs_stat_color,
+      direction = direction,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill,
+      ...
+    )
   } else if (method == "theoretical") {
-    infer_plot <- visualize_theoretical(data = data,
-                                        dens_color = dens_color,
-                                        obs_stat = obs_stat,
-                                        obs_stat_color = obs_stat_color,
-                                        direction = direction,
-                                        pvalue_fill = pvalue_fill,
-                                        endpoints = endpoints,
-                                        ci_fill = ci_fill,
-                                        ...)
+    infer_plot <- visualize_theoretical(
+      data = data,
+      dens_color = dens_color,
+      obs_stat = obs_stat,
+      obs_stat_color = obs_stat_color,
+      direction = direction,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill,
+      ...
+    )
   } else if (method == "both") {
     if (!("stat" %in% names(data))) {
-      stop_glue('`generate()` and `calculate()` are both required ',
-                'to be done prior to `visualize(method = "both")`')
+      stop_glue(
+        '`generate()` and `calculate()` are both required to be done prior ',
+        'to `visualize(method = "both")`'
+      )
     }
 
-    if (("replicate" %in% names(data)) &&
-        length(unique(data$replicate)) < 100) {
+    if (
+      ("replicate" %in% names(data)) && length(unique(data$replicate)) < 100
+    ) {
       warning_glue(
         "With only {length(unique(data$stat))} replicates, it may be ",
         "difficult to see the relationship between simulation and theory."
       )
     }
 
-    infer_plot <- visualize_both(data = data, bins = bins,
-                                 dens_color = dens_color,
-                                 obs_stat = obs_stat,
-                                 obs_stat_color = obs_stat_color,
-                                 direction = direction,
-                                 pvalue_fill = pvalue_fill,
-                                 endpoints = endpoints,
-                                 ci_fill = ci_fill,
-                                 ...)
+    infer_plot <- visualize_both(
+      data = data,
+      bins = bins,
+      dens_color = dens_color,
+      obs_stat = obs_stat,
+      obs_stat_color = obs_stat_color,
+      direction = direction,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill,
+      ...
+    )
   } else {
-    stop_glue("Provide `method` with one of three options: ",
-              '`"theoretical"`, `"both"`, or `"simulation"`. ',
-              '`"simulation"` is the default.')
+    stop_glue(
+      'Provide `method` with one of three options: `"theoretical"`, `"both"`, ',
+      'or `"simulation"`. `"simulation"` is the default.'
+    )
   }
 
   if (!is.null(obs_stat)) { # && !is.null(direction)
@@ -168,24 +185,31 @@ visualize <- function(data, bins = 15, method = "simulation",
 
   if (!is.null(endpoints)) {
     if (!is.null(obs_stat)) {
-      warning_glue("Values for both `endpoints` and `obs_stat` were given ",
-                   "when only one should be set. Ignoring `obs_stat` values.")
+      warning_glue(
+        "Values for both `endpoints` and `obs_stat` were given when only one ",
+        "should be set. Ignoring `obs_stat` values."
+      )
     }
     infer_plot <- infer_plot +
-      geom_vline(xintercept = endpoints, size = 2,
-                 color = endpoints_color,
-                 ...)
+      geom_vline(
+        xintercept = endpoints, size = 2, color = endpoints_color, ...
+      )
   }
 
   infer_plot
 }
 
 theory_t_plot <- function(deg_freedom, statistic_text = "t",
-                          dens_color = dens_color, ...) {
+                          dens_color = dens_color,
+                          ...) {
   ggplot(data.frame(x = c(qt(0.001, deg_freedom),
                           qt(0.999, deg_freedom)))) +
-    stat_function(mapping = aes(x), fun = dt, args = list(df = deg_freedom),
-                  color = dens_color) +
+    stat_function(
+      mapping = aes(x),
+      fun = dt,
+      args = list(df = deg_freedom),
+      color = dens_color
+    ) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
     ylab("")
@@ -198,18 +222,22 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
                         bins,
                         pvalue_fill,
                         endpoints,
-                        ci_fill, ...) {
-  infer_t_plot <- shade_density_check(data = data,
-                                      obs_stat = obs_stat,
-                                      direction = direction,
-                                      bins = bins,
-                                      endpoints = endpoints,
-                                      pvalue_fill = pvalue_fill,
-                                      ci_fill = ci_fill)
+                        ci_fill,
+                        ...) {
+  infer_t_plot <- shade_density_check(
+    data = data,
+    obs_stat = obs_stat,
+    direction = direction,
+    bins = bins,
+    endpoints = endpoints,
+    pvalue_fill = pvalue_fill,
+    ci_fill = ci_fill
+  )
 
   infer_t_plot +
-    stat_function(fun = dt, args = list(df = deg_freedom),
-                  color = dens_color) +
+    stat_function(
+      fun = dt, args = list(df = deg_freedom), color = dens_color
+    ) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
     )) +
@@ -219,12 +247,16 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
 
 theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom,
                               statistic_text = "F",
-                              dens_color = dens_color, ...) {
+                              dens_color = dens_color,
+                              ...) {
   ggplot(data.frame(x = c(qf(0.001, deg_freedom_top, deg_freedom_bottom),
                           qf(0.999, deg_freedom_top, deg_freedom_bottom)))) +
-    stat_function(mapping = aes(x), fun = df,
-                  args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
-                  color = dens_color) +
+    stat_function(
+      mapping = aes(x),
+      fun = df,
+      args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
+      color = dens_color
+    ) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
     ylab("")
@@ -246,18 +278,22 @@ both_anova_plot <- function(data, deg_freedom_top,
     )
   }
 
-  infer_anova_plot <- shade_density_check(data = data,
-                                          obs_stat = obs_stat,
-                                          direction = direction,
-                                          bins = bins,
-                                          endpoints = endpoints,
-                                          pvalue_fill = pvalue_fill,
-                                          ci_fill = ci_fill)
+  infer_anova_plot <- shade_density_check(
+    data = data,
+    obs_stat = obs_stat,
+    direction = direction,
+    bins = bins,
+    endpoints = endpoints,
+    pvalue_fill = pvalue_fill,
+    ci_fill = ci_fill
+  )
 
   infer_anova_plot <- infer_anova_plot +
-    stat_function(fun = df,
-                  args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
-                  color = dens_color) +
+    stat_function(
+      fun = df,
+      args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
+      color = dens_color
+    ) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
     )) +
@@ -265,7 +301,7 @@ both_anova_plot <- function(data, deg_freedom_top,
     ylab("")
 }
 
-theory_z_plot <- function(statistic_text = "z", dens_color = dens_color,  ...) {
+theory_z_plot <- function(statistic_text = "z", dens_color = dens_color, ...) {
   ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) +
     stat_function(mapping = aes(x), fun = dnorm, color = dens_color) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
@@ -282,13 +318,15 @@ both_z_plot <- function(data, statistic_text = "z",
                         endpoints,
                         ci_fill,
                         ...) {
-  infer_z_plot <- shade_density_check(data = data,
-                                      obs_stat = obs_stat,
-                                      direction = direction,
-                                      bins = bins,
-                                      endpoints = endpoints,
-                                      pvalue_fill = pvalue_fill,
-                                      ci_fill = ci_fill)
+  infer_z_plot <- shade_density_check(
+    data = data,
+    obs_stat = obs_stat,
+    direction = direction,
+    bins = bins,
+    endpoints = endpoints,
+    pvalue_fill = pvalue_fill,
+    ci_fill = ci_fill
+  )
   infer_z_plot +
     stat_function(fun = dnorm, color = dens_color) +
     ggtitle(glue_null(
@@ -300,12 +338,16 @@ both_z_plot <- function(data, statistic_text = "z",
 
 theory_chisq_plot <- function(deg_freedom,
                               statistic_text = "Chi-Square",
-                              dens_color = dens_color, ...) {
+                              dens_color = dens_color,
+                              ...) {
   ggplot(data.frame(x = c(qchisq(0.001, deg_freedom),
                           qchisq(0.999, deg_freedom)))) +
-    stat_function(mapping = aes(x), fun = dchisq,
-                  args = list(df = deg_freedom),
-                  color = dens_color) +
+    stat_function(
+      mapping = aes(x),
+      fun = dchisq,
+      args = list(df = deg_freedom),
+      color = dens_color
+    ) +
     ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
     xlab("") +
     ylab("")
@@ -325,17 +367,20 @@ both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                  "Proceed with caution.")
   }
 
-  infer_chisq_plot <- shade_density_check(data = data,
-                                          obs_stat = obs_stat,
-                                          direction = direction,
-                                          bins = bins,
-                                          endpoints = endpoints,
-                                          pvalue_fill = pvalue_fill,
-                                          ci_fill = ci_fill)
+  infer_chisq_plot <- shade_density_check(
+    data = data,
+    obs_stat = obs_stat,
+    direction = direction,
+    bins = bins,
+    endpoints = endpoints,
+    pvalue_fill = pvalue_fill,
+    ci_fill = ci_fill
+  )
 
   infer_chisq_plot +
-    stat_function(fun = dchisq, args = list(df = deg_freedom),
-                  color = dens_color) +
+    stat_function(
+      fun = dchisq, args = list(df = deg_freedom), color = dens_color
+    ) +
     ggtitle(glue_null(
       "Simulation-Based and Theoretical {statistic_text} Null Distributions"
     )) +
@@ -350,12 +395,14 @@ shade_density_check <- function(data,
                                 density = TRUE,
                                 pvalue_fill,
                                 endpoints,
-                                ci_fill, ...) {
+                                ci_fill,
+                                ...) {
   if (is.null(direction) || is.null(obs_stat)) {
     if (density) {
       gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-        geom_histogram(bins = bins, color = "white",
-                       mapping = aes(y = ..density..), ...)
+        geom_histogram(
+          bins = bins, color = "white", mapping = aes(y = ..density..), ...
+        )
     } # else {
       # Not sure if needed? Can't get tests to find it
       # gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
@@ -367,8 +414,9 @@ shade_density_check <- function(data,
     if (!is.null(direction)) {
       if (density) {
         gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-          geom_histogram(bins = bins, color = "white",
-                         mapping = aes(y = ..density..), ...)
+          geom_histogram(
+            bins = bins, color = "white", mapping = aes(y = ..density..), ...
+          )
       } else {
         gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
           geom_histogram(bins = bins, color = "white", ...)
@@ -376,56 +424,77 @@ shade_density_check <- function(data,
 
       if (direction %in% c("less", "left")) {
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
+            ...
+          )
       }
       if (direction %in% c("greater", "right")) {
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
+            ...
+          )
       }
 
-      if (direction %in% c("two_sided", "both") &&
-          obs_stat >= stats::median(data$stat)) {
+      if (
+        direction %in% c("two_sided", "both") &&
+        obs_stat >= stats::median(data$stat)
+      ) {
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0,
-                                  ymax = Inf), ...) +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(
-                      xmin = -Inf,
-                      xmax = stats::quantile(
-                        data$stat,
-                        probs = 1 - get_percentile(data$stat, obs_stat)
-                      ),
-                      ymin = 0, ymax = Inf, ...)
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
+            ...
+          ) +
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            mapping = aes(
+              xmin = -Inf,
+              xmax = stats::quantile(
+                data$stat, probs = 1 - get_percentile(data$stat, obs_stat)
+              ),
+              ymin = 0,
+              ymax = Inf,
+              ...
+            )
           )
       }
 
-      if (direction %in% c("two_sided", "both") &&
-          obs_stat < stats::median(data$stat)) {
+      if (
+        direction %in% c("two_sided", "both") &&
+        obs_stat < stats::median(data$stat)
+      ) {
         gg_plot <- gg_plot +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0,
-                                  ymax = Inf), ...) +
-          geom_rect(fill = pvalue_fill, alpha = 0.01,
-                    mapping = aes(
-                      xmin = stats::quantile(
-                        data$stat,
-                        probs = 1 - get_percentile(data$stat, obs_stat)
-                      ), xmax = Inf, ymin = 0, ymax = Inf, ...)
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
+            ...
+          ) +
+          geom_rect(
+            fill = pvalue_fill, alpha = 0.01,
+            mapping = aes(
+              xmin = stats::quantile(
+                data$stat, probs = 1 - get_percentile(data$stat, obs_stat)
+              ),
+              xmax = Inf,
+              ymin = 0,
+              ymax = Inf,
+              ...
+            )
           )
       }
     }
 
     if (direction == "between") {
       gg_plot <- gg_plot +
-        geom_rect(fill = ci_fill, alpha = 0.01,
-                  aes(xmin = endpoints[1],
-                      xmax = endpoints[2], ymin = 0, ymax = Inf),
-                  ...)
+        geom_rect(
+          fill = ci_fill, alpha = 0.01,
+          aes(xmin = endpoints[1], xmax = endpoints[2], ymin = 0, ymax = Inf),
+          ...
+        )
     }
   }
   gg_plot
@@ -439,7 +508,8 @@ visualize_simulation <- function(data, bins,
                                  direction,
                                  pvalue_fill,
                                  endpoints,
-                                 ci_fill, ...) {
+                                 ci_fill,
+                                 ...) {
   if (is.null(direction)) {
     if (length(unique(data$stat)) >= 10) {
       infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
@@ -450,14 +520,16 @@ visualize_simulation <- function(data, bins,
         xlab("stat")
     }
   } else {
-    infer_plot <- shade_density_check(data = data,
-                                      obs_stat = obs_stat,
-                                      direction = direction,
-                                      bins = bins,
-                                      density = FALSE,
-                                      pvalue_fill = pvalue_fill,
-                                      endpoints = endpoints,
-                                      ci_fill = ci_fill)
+    infer_plot <- shade_density_check(
+      data = data,
+      obs_stat = obs_stat,
+      direction = direction,
+      bins = bins,
+      density = FALSE,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill
+    )
   }
   infer_plot
 }
@@ -476,21 +548,27 @@ visualize_theoretical <- function(data,
     "method. {{infer}} currently does not check these for you."
   )
 
-  if (!is.null(attr(data, "stat")) &&
-      !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))) {
+  if (
+    !is.null(attr(data, "stat")) &&
+    !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))
+  ) {
     warning_glue(
       "Your `calculate`d statistic and the theoretical distribution are on ",
       "different scales. Displaying only the theoretical distribution."
     )
   }
 
-  if (attr(data, "theory_type") %in%
-      c("Two sample t", "Slope with t", "One sample t")) {
-    infer_plot <- theory_t_plot(deg_freedom = attr(data, "distr_param"),
-                                statistic_text = "t",
-                                dens_color = dens_color)
+  if (
+    attr(data, "theory_type") %in% c(
+      "Two sample t", "Slope with t", "One sample t"
+    )
+  ) {
+    infer_plot <- theory_t_plot(
+      deg_freedom = attr(data, "distr_param"),
+      statistic_text = "t",
+      dens_color = dens_color
+    )
   } else if (attr(data, "theory_type") == "ANOVA") {
-    
     if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
       warning_glue(
         "F usually corresponds to right-tailed tests. Proceed with caution."
@@ -501,23 +579,29 @@ visualize_theoretical <- function(data,
       deg_freedom_top = attr(data, "distr_param"),
       deg_freedom_bottom = attr(data, "distr_param2"),
       statistic_text = "F",
-      dens_color = dens_color)
-  } else if (attr(data, "theory_type") %in%
-             c("One sample prop z", "Two sample props z")) {
-    
-    infer_plot <- theory_z_plot(statistic_text = "z",
-                                dens_color = dens_color)
-  } else if (attr(data, "theory_type") %in%
-             c("Chi-square test of indep", "Chi-square Goodness of Fit")) {
-
+      dens_color = dens_color
+    )
+  } else if (
+    attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
+  ) {
+    infer_plot <- theory_z_plot(statistic_text = "z", dens_color = dens_color)
+  } else if (
+    attr(data, "theory_type") %in% c(
+      "Chi-square test of indep", "Chi-square Goodness of Fit"
+    )
+  ) {
     if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-      warning_glue("Chi-square usually corresponds to right-tailed tests. ",
-                   "Proceed with caution.")
+      warning_glue(
+        "Chi-square usually corresponds to right-tailed tests. ",
+        "Proceed with caution."
+      )
     }
 
-    infer_plot <- theory_chisq_plot(deg_freedom = attr(data, "distr_param"),
-                                    statistic_text = "Chi-Square",
-                                    dens_color = dens_color)
+    infer_plot <- theory_chisq_plot(
+      deg_freedom = attr(data, "distr_param"),
+      statistic_text = "Chi-Square",
+      dens_color = dens_color
+    )
   } # else {
     # stop_glue(
     #   '"{attr(data, "theory_type")}" is not implemented (possibly yet).'
@@ -529,44 +613,55 @@ visualize_theoretical <- function(data,
     if (!is.null(direction)) {
       if (direction %in% c("less", "left")) {
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
+            ...
+          )
       }
       if (direction %in% c("greater", "right")) {
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = obs_stat,
-                        xmax = Inf, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
+            ...
+          )
       }
 
       # Assuming two-tailed shading will only happen with theoretical
       # distributions centered at 0
       if (direction %in% c("two_sided", "both") && obs_stat >= 0) {
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-                    ...) +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = -Inf, xmax = -obs_stat, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
+            ...
+          ) +
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = -Inf, xmax = -obs_stat, ymin = 0, ymax = Inf),
+            ...
+          )
       }
 
       if (direction %in% c("two_sided", "both") && obs_stat < 0) {
         infer_plot <- infer_plot +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-                    ...) +
-          geom_rect(data = data.frame(obs_stat), fill = pvalue_fill,
-                    alpha = 0.6,
-                    aes(xmin = -obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-                    ...)
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
+            ...
+          ) +
+          geom_rect(
+            data = data.frame(obs_stat),
+            fill = pvalue_fill, alpha = 0.6,
+            aes(xmin = -obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
+            ...
+          )
       }
     }
   }
@@ -583,28 +678,33 @@ visualize_both <- function(data, bins,
                            direction,
                            pvalue_fill,
                            endpoints,
-                           ci_fill, ...) {
+                           ci_fill,
+                           ...) {
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. `infer` currently does not check these for you."
   )
 
   if (!(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))) {
-    stop_glue("Your `calculate`d statistic and the theoretical distribution ",
-              "are on different scales. Use a standardized `stat` instead.")
+    stop_glue(
+      "Your `calculate`d statistic and the theoretical distribution are on ",
+      "different scales. Use a standardized `stat` instead."
+    )
   }
 
   if (attr(data, "theory_type") %in% c("Two sample t", "Slope with t")) {
-    infer_plot <- both_t_plot(data = data,
-                              deg_freedom = attr(data, "distr_param"),
-                              statistic_text = "t",
-                              dens_color = dens_color,
-                              bins = bins,
-                              direction = direction,
-                              obs_stat = obs_stat,
-                              pvalue_fill = pvalue_fill,
-                              endpoints = endpoints,
-                              ci_fill = ci_fill)
+    infer_plot <- both_t_plot(
+      data = data,
+      deg_freedom = attr(data, "distr_param"),
+      statistic_text = "t",
+      dens_color = dens_color,
+      bins = bins,
+      direction = direction,
+      obs_stat = obs_stat,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill
+    )
   } else if (attr(data, "theory_type") == "ANOVA") {
     infer_plot <- both_anova_plot(
       data = data,
@@ -617,32 +717,39 @@ visualize_both <- function(data, bins,
       obs_stat = obs_stat,
       pvalue_fill = pvalue_fill,
       endpoints = endpoints,
-      ci_fill = ci_fill)
-  } else if (attr(data, "theory_type") %in%
-             c("One sample prop z", "Two sample props z")) {
-    infer_plot <- both_z_plot(data = data,
-                              statistic_text = "z",
-                              dens_color = dens_color,
-                              bins = bins,
-                              direction = direction,
-                              obs_stat = obs_stat,
-                              pvalue_fill = pvalue_fill,
-                              endpoints = endpoints,
-                              ci_fill = ci_fill)
+      ci_fill = ci_fill
+    )
   } else if (
-    attr(data, "theory_type") %in%
-    c("Chi-square test of indep", "Chi-square Goodness of Fit")
+    attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
   ) {
-    infer_plot <- both_chisq_plot(data = data,
-                                  deg_freedom = attr(data, "distr_param"),
-                                  statistic_text = "Chi-Square",
-                                  dens_color = dens_color,
-                                  bins = bins,
-                                  direction = direction,
-                                  obs_stat = obs_stat,
-                                  pvalue_fill = pvalue_fill,
-                                  endpoints = endpoints,
-                                  ci_fill = ci_fill)
+    infer_plot <- both_z_plot(
+      data = data,
+      statistic_text = "z",
+      dens_color = dens_color,
+      bins = bins,
+      direction = direction,
+      obs_stat = obs_stat,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill
+    )
+  } else if (
+    attr(data, "theory_type") %in% c(
+      "Chi-square test of indep", "Chi-square Goodness of Fit"
+    )
+  ) {
+    infer_plot <- both_chisq_plot(
+      data = data,
+      deg_freedom = attr(data, "distr_param"),
+      statistic_text = "Chi-Square",
+      dens_color = dens_color,
+      bins = bins,
+      direction = direction,
+      obs_stat = obs_stat,
+      pvalue_fill = pvalue_fill,
+      endpoints = endpoints,
+      ci_fill = ci_fill
+    )
   } # else {
     # stop_glue('"{attr(data, "theory_type")}" is not implemented yet.')
   # }
diff --git a/R/wrappers.R b/R/wrappers.R
index 17c54eee..713876ee 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -47,59 +47,68 @@ t_test <- function(data, formula, # response = NULL, explanatory = NULL,
   ### Only currently working with formula interface
 #  if (hasArg(formula)) {
   if (!is.null(f_rhs(formula))) {
-    data[[as.character(f_rhs(formula))]] <-
-      factor(data[[as.character(f_rhs(formula))]],
-             levels = c(order[1], order[2]))
+    data[[as.character(f_rhs(formula))]] <- factor(
+      data[[as.character(f_rhs(formula))]], levels = c(order[1], order[2])
+    )
 
     # Two sample case
     prelim <- data %>%
-      stats::t.test(formula = formula, data = .,
-                    alternative = alternative,
-                    mu = mu,
-                    conf.level = conf_level,
-                    ...) %>%
+      stats::t.test(
+        formula = formula, data = .,
+        alternative = alternative,
+        mu = mu,
+        conf.level = conf_level,
+        ...
+      ) %>%
       broom::glance()
   } else {
     # One sample case
     # To fix weird indexing error convert back to data.frame
     # (Error: Can't use matrix or array for column indexing)
     data <- as.data.frame(data)
-    prelim <- stats::t.test(x = data[[as.character(f_lhs(formula))]],
-                  alternative = alternative,
-                  mu = mu,
-                  conf.level = conf_level,
-                  ...) %>%
+    prelim <- stats::t.test(
+      x = data[[as.character(f_lhs(formula))]],
+      alternative = alternative,
+      mu = mu,
+      conf.level = conf_level,
+      ...
+    ) %>%
       broom::glance()
   }
 
   if (conf_int) {
     results <- prelim %>%
-      dplyr::select(statistic, t_df = parameter, p_value = p.value,
-                    alternative,
-                    lower_ci = conf.low,
-                    upper_ci = conf.high)
+      dplyr::select(
+        statistic, t_df = parameter, p_value = p.value, alternative,
+        lower_ci = conf.low, upper_ci = conf.high
+      )
   } else {
     results <- prelim %>%
-      dplyr::select(statistic, t_df = parameter, p_value = p.value,
-                    alternative)
+      dplyr::select(
+        statistic, t_df = parameter, p_value = p.value, alternative
+      )
   }
 
     return(results)
-#  } else {
-    # data %>%
-    #   stats::t.test(formula = substitute(response) ~ substitute(explanatory),
-    #                 data = .,
-    #                 alternative = alternative) %>%
-    #   broom::glance() %>%
-    #   dplyr::select(statistic, t_df = parameter, p_value = p.value,
-    #                 alternative)
-
-#     t.test(y = data[[as.character(substitute(response))]],
-#            x = data[[as.character(substitute(explanatory))]],
-#            alternative = alternative) %>%
-#     broom::glance() %>%
-#     select(statistic, t_df = parameter, p_value = p.value, alternative)
-# }
+#   } else {
+#     data %>%
+#       stats::t.test(
+#        formula = substitute(response) ~ substitute(explanatory), data = .,
+#         alternative = alternative
+#       ) %>%
+#       broom::glance() %>%
+#       dplyr::select(
+#         statistic, t_df = parameter, p_value = p.value, alternative
+#       )
+#
+#     t.test(
+#       y = data[[as.character(substitute(response))]],
+#       x = data[[as.character(substitute(explanatory))]],
+#       alternative = alternative
+#     ) %>%
+#       broom::glance() %>%
+#       select(statistic, t_df = parameter, p_value = p.value, alternative)
+#   }
 }
 
 #' Tidy t-test statistic
@@ -181,9 +190,9 @@ chisq_stat <- function(data, formula, ...) {
 }
 
 check_conf_level <- function(conf_level) {
-  if (class(conf_level) != "numeric" |
-      conf_level < 0 |
-      conf_level > 1) {
+  if (
+    class(conf_level) != "numeric" | conf_level < 0 | conf_level > 1
+  ) {
     stop_glue("The `conf_level` argument must be a number between 0 and 1.")
   }
 }
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index f682ff99..e34bc222 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -3,10 +3,10 @@ context("calculate")
 iris_df <- tibble::as_tibble(iris)
 
 iris_tbl <- iris_df %>%
-  dplyr::mutate(Sepal.Length.Group =
-                  dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-                Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
+    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+  )
 
 # calculate arguments
 test_that("x is a tibble", {
@@ -29,19 +29,19 @@ test_that("stat argument is appropriate", {
 })
 
 test_that("response attribute has been set", {
-  expect_error(tibble::as.tibble(iris) %>%
-                 calculate(stat = "median")
-               )
+  expect_error(
+    tibble::as.tibble(iris) %>% calculate(stat = "median")
+  )
 })
 
 test_that("variable chosen is of appropriate class (one var problems)", {
   # One sample chisq example
   gen_iris1 <- iris %>%
     specify(Species ~ NULL) %>%
-    hypothesize(null = "point",
-                p = c("setosa" = .5,
-                      "versicolor" = .25,
-                      "virginica" = .25)) %>%
+    hypothesize(
+      null = "point",
+      p = c("setosa" = .5, "versicolor" = .25, "virginica" = .25)
+    ) %>%
     generate(reps = 10, type = "simulate")
   expect_error(calculate(gen_iris1, stat = "mean"))
 
@@ -106,8 +106,9 @@ test_that("response variable is a factor (two var problems)", {
 
   # Species has more than 2 levels
   gen_iris4 <- iris %>%
-    dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Sepal.Length.Group ~ Species, success = ">5") %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
@@ -117,17 +118,21 @@ test_that("response variable is a factor (two var problems)", {
 
   # Check successful diff in props
   gen_iris4a <- iris %>%
-    dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
-    dplyr::mutate(Sepal.Width.Group =
-                    dplyr::if_else(Sepal.Width > 3, "large", "small")) %>%
+    dplyr::mutate(
+      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
+    dplyr::mutate(
+      Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+    ) %>%
     specify(Sepal.Length.Group ~ Sepal.Width.Group, success = ">5") %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
-  expect_silent(calculate(gen_iris4a, stat = "diff in props",
-                          order = c("large", "small")))
-  expect_silent(calculate(gen_iris4a, stat = "z",
-                          order = c("large", "small")))
+  expect_silent(
+    calculate(gen_iris4a, stat = "diff in props", order = c("large", "small"))
+  )
+  expect_silent(
+    calculate(gen_iris4a, stat = "z", order = c("large", "small"))
+  )
   expect_error(calculate(gen_iris4a, stat = "z"))
 })
 
@@ -141,17 +146,18 @@ test_that("response variable is numeric (two var problems)", {
 
 test_that("two sample mean-type problems are working", {
   gen_iris5a <- iris %>%
-    dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Sepal.Width ~ Sepal.Length.Group) %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
   expect_error(calculate(gen_iris5a, stat = "diff in means"))
-  expect_silent(calculate(gen_iris5a, stat = "diff in means",
-                          order = c(">5", "<=5")))
+  expect_silent(
+    calculate(gen_iris5a, stat = "diff in means", order = c(">5", "<=5"))
+  )
   expect_error(calculate(gen_iris5a, stat = "t"))
-  expect_silent(calculate(gen_iris5a, stat = "t",
-                          order = c(">5", "<=5")))
+  expect_silent(calculate(gen_iris5a, stat = "t", order = c(">5", "<=5")))
 })
 
 test_that("properties of tibble passed-in are correct", {
@@ -167,21 +173,27 @@ test_that("properties of tibble passed-in are correct", {
 
 test_that("order is working for diff in means", {
   gen_iris7 <- iris %>%
-    dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Sepal.Width ~ Sepal.Length.Group) %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
-  expect_equal(nrow(calculate(gen_iris7, stat = "diff in means",
-                              order = c(">5", "<=5"))), 10)
-  expect_equal(ncol(calculate(gen_iris7, stat = "diff in means",
-                              order = c(">5", "<=5"))), 2)
+  expect_equal(
+    nrow(calculate(gen_iris7, stat = "diff in means", order = c(">5", "<=5"))),
+    10
+  )
+  expect_equal(
+    ncol(calculate(gen_iris7, stat = "diff in means", order = c(">5", "<=5"))),
+    2
+  )
 })
 
 test_that("chi-square matches chisq.test value", {
   gen_iris8 <- iris %>%
-    dplyr::mutate(Petal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Petal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Petal.Length.Group ~ Species, success = ">5") %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
@@ -189,8 +201,9 @@ test_that("chi-square matches chisq.test value", {
   # chisq.test way
   trad_way <- gen_iris8 %>%
     dplyr::group_by(replicate) %>%
-    dplyr::do(broom::tidy(stats::chisq.test(table(.$Petal.Length.Group,
-                                                  .$Species)))) %>%
+    dplyr::do(broom::tidy(
+      stats::chisq.test(table(.$Petal.Length.Group, .$Species))
+    )) %>%
     dplyr::ungroup() %>%
     dplyr::select(replicate, stat = statistic)
   # Equal not including attributes
@@ -198,85 +211,93 @@ test_that("chi-square matches chisq.test value", {
 
   gen_iris9 <- iris %>%
     specify(Species ~ NULL) %>%
-    hypothesize(null = "point",
-                p = c("setosa" = 1/3,
-                      "versicolor" = 1/3,
-                      "virginica" = 1/3)) %>%
+    hypothesize(
+      null = "point",
+      p = c("setosa" = 1/3, "versicolor" = 1/3, "virginica" = 1/3)
+    ) %>%
     generate(reps = 10, type = "simulate")
   infer_way <- calculate(gen_iris9, stat = "Chisq")
   # chisq.test way
   trad_way <- gen_iris9 %>%
     dplyr::group_by(replicate) %>%
-    dplyr::do(broom::tidy(stats::chisq.test(table(.$Species)))) %>%
+    dplyr::do(broom::tidy(
+      stats::chisq.test(table(.$Species))
+    )) %>%
     dplyr::select(replicate, stat = statistic)
   expect_equal(infer_way, trad_way)
 
   gen_iris9a <- iris %>%
     specify(Species ~ NULL) %>%
-    hypothesize(null = "point",
-                p = c("setosa" = 0.8,
-                      "versicolor" = 0.1,
-                      "virginica" = 0.1)) %>%
+    hypothesize(
+      null = "point",
+      p = c("setosa" = 0.8, "versicolor" = 0.1, "virginica" = 0.1)
+    ) %>%
     generate(reps = 10, type = "simulate")
   infer_way <- calculate(gen_iris9a, stat = "Chisq")
   # chisq.test way
   trad_way <- gen_iris9a %>%
     dplyr::group_by(replicate) %>%
-    dplyr::do(broom::tidy(stats::chisq.test(table(.$Species),
-                                            p = c(0.8, 0.1, 0.1)))) %>%
+    dplyr::do(broom::tidy(
+      stats::chisq.test(table(.$Species), p = c(0.8, 0.1, 0.1))
+    )) %>%
     dplyr::select(replicate, stat = statistic)
   expect_equal(infer_way, trad_way)
 })
 
 test_that("`order` is working", {
   gen_iris10 <- iris %>%
-    dplyr::mutate(Petal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Petal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Petal.Width ~ Petal.Length.Group) %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
-  expect_error(calculate(gen_iris10, stat = "diff in means",
-                         order = c(TRUE, FALSE)))
+  expect_error(
+    calculate(gen_iris10, stat = "diff in means", order = c(TRUE, FALSE))
+  )
 
   gen_iris11 <- iris %>%
-    dplyr::mutate(Petal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Petal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Petal.Width ~ Petal.Length.Group) %>%
     generate(reps = 10, type = "bootstrap")
-  expect_error(calculate(gen_iris11, stat = "diff in medians",
-                         order = ">5"))
-  expect_error(calculate(gen_iris11, stat = "diff in medians",
-                         order = c(NA, ">5")))
-  expect_error(calculate(gen_iris11, stat = "diff in medians",
-                         order = c(">5", "<=4")))
-  expect_silent(calculate(gen_iris11, stat = "diff in medians",
-                         order = c(">5", "<=5")))
-  expect_error(calculate(gen_iris11, stat = "diff in means",
-                         order = c(">5", "<=4", ">4")))
+  expect_error(
+    calculate(gen_iris11, stat = "diff in medians", order = ">5")
+  )
+  expect_error(
+    calculate(gen_iris11, stat = "diff in medians", order = c(NA, ">5"))
+  )
+  expect_error(
+    calculate(gen_iris11, stat = "diff in medians", order = c(">5", "<=4"))
+  )
+  expect_silent(
+    calculate(gen_iris11, stat = "diff in medians", order = c(">5", "<=5"))
+  )
+  expect_error(
+    calculate(gen_iris11, stat = "diff in means", order = c(">5", "<=4", ">4"))
+  )
   # order not given
   expect_error(calculate(gen_iris11, stat = "diff in means"))
 })
 
 test_that('success is working for stat = "prop"', {
   gen_iris12 <- iris %>%
-    dplyr::mutate(Sepal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 5, ">5", "<=5")) %>%
+    dplyr::mutate(
+      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+    ) %>%
     specify(Sepal.Length.Group ~ NULL, success = ">5") %>%
     hypothesize(null = "point", p = 0.3) %>%
     generate(reps = 10, type = "simulate")
-  expect_silent(gen_iris12 %>%
-                  calculate(stat = "prop"))
-  expect_silent(gen_iris12 %>%
-                  calculate(stat = "z"))
+  expect_silent(gen_iris12 %>% calculate(stat = "prop"))
+  expect_silent(gen_iris12 %>% calculate(stat = "z"))
 })
 
 test_that("NULL response gives error", {
   iris_improp <- tibble::as_tibble(iris) %>%
     dplyr::select(Sepal.Width, Sepal.Length)
 
-  expect_error(
-    iris_improp %>% calculate(stat = "mean")
-  )
+  expect_error(iris_improp %>% calculate(stat = "mean"))
 })
 
 test_that("Permute F test works", {
@@ -298,43 +319,45 @@ test_that("Permute slope/correlation test works", {
 
 test_that("order being given when not needed gives warning", {
   gen_iris15 <- iris %>%
-    dplyr::mutate(Petal.Length.Group =
-                    dplyr::if_else(Sepal.Length > 4, ">4", "<=4")) %>%
+    dplyr::mutate(
+      Petal.Length.Group = dplyr::if_else(Sepal.Length > 4, ">4", "<=4")
+    ) %>%
     specify(Petal.Length.Group ~ Species, success = ">4") %>%
     hypothesize(null = "independence") %>%
     generate(reps = 10, type = "permute")
-  expect_warning(calculate(gen_iris15, stat = "Chisq",
-                           order = c("setosa", "virginica")))
+  expect_warning(
+    calculate(gen_iris15, stat = "Chisq", order = c("setosa", "virginica"))
+  )
 })
 
 ## Breaks oldrel build. Commented out for now.
 # test_that("warning given if calculate without generate", {
-#   expect_warning(iris %>%
-#                    specify(Species ~ NULL) %>%
-#                    hypothesize(null = "point",
-#                                p = c("setosa" = 0.4,
-#                                      "versicolor" = 0.4,
-#                                      "virginica" = 0.2)) %>%
-#                    #generate(reps = 10, type = "simulate") %>%
-#                    calculate(stat = "Chisq")
+#   expect_warning(
+#     iris %>%
+#       specify(Species ~ NULL) %>%
+#       hypothesize(
+#         null = "point",
+#         p = c("setosa" = 0.4, "versicolor" = 0.4, "virginica" = 0.2)
+#       ) %>%
+#       # generate(reps = 10, type = "simulate") %>%
+#       calculate(stat = "Chisq")
 #   )
-#
 # })
 
 test_that("specify() %>% calculate() works", {
-  expect_silent(iris_tbl %>%
-                  specify(Petal.Width ~ NULL) %>%
-                  calculate(stat = "mean")
+  expect_silent(
+    iris_tbl %>% specify(Petal.Width ~ NULL) %>% calculate(stat = "mean")
   )
-  expect_error(iris_tbl %>%
-                 specify(Petal.Width ~ NULL) %>%
-                 hypothesize(null = "point", mu = 4) %>%
-                 calculate(stat = "mean")
+  expect_error(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 4) %>%
+      calculate(stat = "mean")
   )
 
-  expect_error(iris_tbl %>%
-                  specify(Species ~ NULL) %>%
-                  calculate(stat = "Chisq"))
+  expect_error(
+    iris_tbl %>% specify(Species ~ NULL) %>% calculate(stat = "Chisq")
+  )
 })
 
 test_that("One sample t hypothesis test is working", {
@@ -352,23 +375,21 @@ test_that("specify done before calculate", {
     dplyr::select(stat = Sepal.Width)
   expect_error(calculate(iris_mean, stat = "mean"))
 
-  iris_prop <- iris_tbl %>%
-    dplyr::select(Sepal.Length.Group)
+  iris_prop <- iris_tbl %>% dplyr::select(Sepal.Length.Group)
   attr(iris_prop, "response") <- "Sepal.Length.Group"
   expect_error(calculate(iris_prop, stat = "prop"))
 })
 
 test_that("chisq GoF has params specified for observed stat", {
-  no_params <- iris_df %>%
-    specify(response = Species)
+  no_params <- iris_df %>% specify(response = Species)
   expect_error(calculate(no_params, stat = "Chisq"))
 
   params <- iris_df %>%
     specify(response = Species) %>%
-    hypothesize(null = "point",
-                p = c("setosa" = .5,
-                      "versicolor" = .25,
-                      "virginica" = .25))
+    hypothesize(
+      null = "point",
+      p = c("setosa" = .5, "versicolor" = .25, "virginica" = .25)
+    )
   expect_silent(calculate(params, stat = "Chisq"))
 })
 
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
index 6413db4a..60da5354 100644
--- a/tests/testthat/test-conf_int.R
+++ b/tests/testthat/test-conf_int.R
@@ -1,53 +1,32 @@
 context("conf_int")
 
 iris_tbl <- iris %>%
-  dplyr::mutate(Sepal.Length.Group =
-                  dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-                Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
+    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+  )
 
 iris_calc <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group,
-          success = "<=5") %>%
+  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000) %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
 obs_diff <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group,
-          success = "<=5") %>%
+  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
   calculate(stat = "diff in props", order = c("large", "small"))
 
 set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("basics work", {
-  expect_silent(
-    test_df %>%
-      conf_int()
-  )
-  expect_error(
-    test_df %>%
-      conf_int(type = "other")
-  )
-  expect_error(
-    test_df %>%
-      conf_int(level = 1.2)
-  )
-  expect_error(
-    test_df %>%
-      conf_int(point_estimate = "help")
-  )
-  expect_silent(
-    iris_calc %>% get_ci(type = "se", point_estimate = 4)
-  )
-  expect_silent(
-    iris_calc %>% get_ci(type = "se", point_estimate = obs_diff)
-  )
-  expect_error(
-    iris_calc %>% get_ci(type = "se", point_estimate = "error")
-  )
-  expect_error(
-    iris_calc %>% get_ci(type = "se")
-  )
+  expect_silent(test_df %>% conf_int())
+  expect_error(test_df  %>% conf_int(type = "other"))
+  expect_error(test_df  %>% conf_int(level = 1.2))
+  expect_error(test_df  %>% conf_int(point_estimate = "help"))
+  
+  expect_silent(iris_calc %>% get_ci(type = "se", point_estimate = 4))
+  expect_silent(iris_calc %>% get_ci(type = "se", point_estimate = obs_diff))
+  expect_error(iris_calc  %>% get_ci(type = "se", point_estimate = "error"))
+  expect_error(iris_calc  %>% get_ci(type = "se"))
 })
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index e92d5f77..c2de7d46 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -1,11 +1,10 @@
 context("generate")
 
 mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(cyl = factor(cyl),
-                vs = factor(vs),
-                am = factor(am),
-                gear = factor(gear),
-                carb = factor(carb))
+  dplyr::mutate(
+    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
+    carb = factor(carb)
+  )
 
 hyp_prop <- mtcars %>%
   specify(response = am, success = "1") %>%
@@ -72,19 +71,19 @@ test_that("cohesion with type argument", {
 })
 
 test_that("sensible output", {
-  expect_equal(nrow(mtcars) * 500,
-               nrow(generate(hyp_prop, reps = 500, type = "simulate")))
+  expect_equal(
+    nrow(mtcars) * 500, nrow(generate(hyp_prop, reps = 500, type = "simulate"))
+  )
   expect_silent(generate(hyp_mean, reps = 1, type = "bootstrap"))
   expect_error(generate(hyp_mean, reps = 1, type = "other"))
 })
 
 test_that("auto `type` works (generate)", {
   mtcars <- as.data.frame(mtcars) %>%
-    dplyr::mutate(cyl = factor(cyl),
-                  vs = factor(vs),
-                  am = factor(am),
-                  gear = factor(gear),
-                  carb = factor(carb))
+    dplyr::mutate(
+      cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
+      carb = factor(carb)
+    )
 
   one_mean <- mtcars %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
@@ -169,36 +168,43 @@ test_that("auto `type` works (generate)", {
   expect_error(mtcars %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", mu = 25) %>%
-    generate(reps = 100, type = "permute"))
+    generate(reps = 100, type = "permute")
+  )
 
   expect_error(mtcars %>%
     specify(response = mpg) %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 
   expect_error(mtcars %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", med = 26) %>%
-    generate(reps = 100, type = "permute"))
+    generate(reps = 100, type = "permute")
+  )
 
   expect_error(mtcars %>%
     specify(response = am, success = "1") %>% # formula alt: am ~ NULL
     hypothesize(null = "point", p = .25) %>%
-    generate(reps = 100, type = "bootstrap"))
+    generate(reps = 100, type = "bootstrap")
+  )
 
   expect_error(mtcars %>%
     specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
     hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "bootstrap"))
+    generate(reps = 100, type = "bootstrap")
+  )
 
   expect_error(mtcars %>%
     specify(cyl ~ NULL) %>% # alt: response = cyl
     hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-    generate(reps = 100, type = "bootstrap"))
+    generate(reps = 100, type = "bootstrap")
+  )
 
   expect_error(mtcars %>%
     specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
     hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 
   expect_error(mtcars %>%
     specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
@@ -208,43 +214,54 @@ test_that("auto `type` works (generate)", {
   expect_error(mtcars %>%
     specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 
   expect_error(mtcars %>%
     specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "bootstrap"))
+    generate(reps = 100, type = "bootstrap")
+  )
 
   expect_error(mtcars %>%
     specify(response = am, success = "1") %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 
   expect_error(mtcars %>%
     specify(mpg ~ am) %>%
-    generate(reps = 100, type = "permute"))
+    generate(reps = 100, type = "permute")
+  )
 
   expect_error(mtcars %>%
     specify(am ~ vs, success = "1") %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 
   expect_error(mtcars %>%
     specify(mpg ~ hp) %>%
-    generate(reps = 100, type = "simulate"))
+    generate(reps = 100, type = "simulate")
+  )
 })
 
 test_that("mismatches lead to error", {
   expect_error(mtcars %>% generate(reps = 10, type = "permute"))
-  expect_error(mtcars %>% specify(am ~ NULL, success = "1") %>%
-                 hypothesize(null = "independence", p = c("1" = 0.5)) %>%
-                 generate(reps = 100, type = "simulate"))
-  expect_error(mtcars %>%
-                 specify(cyl ~ NULL) %>% # alt: response = cyl
-                 hypothesize(null = "point", p = c("4" = .5, "6" = .25,
-                                                   "8" = .25)) %>%
-                 generate(reps = 100, type = "bootstrap"))
-  expect_error(mtcars %>%
-                 specify(mpg ~ hp) %>%
-                 generate(reps = 100, type = "other"))
+  expect_error(
+    mtcars %>%
+      specify(am ~ NULL, success = "1") %>%
+      hypothesize(null = "independence", p = c("1" = 0.5)) %>%
+      generate(reps = 100, type = "simulate")
+  )
+  expect_error(
+    mtcars %>%
+      specify(cyl ~ NULL) %>% # alt: response = cyl
+      hypothesize(
+        null = "point", p = c("4" = .5, "6" = .25, "8" = .25)
+      ) %>%
+      generate(reps = 100, type = "bootstrap"))
+  expect_error(
+    mtcars %>% specify(mpg ~ hp) %>% generate(reps = 100, type = "other")
+  )
 })
 
 test_that("generate() handles `NULL` value of `type`", {
diff --git a/tests/testthat/test-hypothesize.R b/tests/testthat/test-hypothesize.R
index 3ffbc004..22d80e8a 100644
--- a/tests/testthat/test-hypothesize.R
+++ b/tests/testthat/test-hypothesize.R
@@ -1,11 +1,10 @@
 context("hypothesize")
 
 mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(cyl = factor(cyl),
-                vs = factor(vs),
-                am = factor(am),
-                gear = factor(gear),
-                carb = factor(carb))
+  dplyr::mutate(
+    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
+    carb = factor(carb)
+  )
 
 one_mean <- mtcars %>%
   specify(response = mpg) %>% # formula alt: mpg ~ NULL
@@ -80,34 +79,47 @@ test_that("hypothesize arguments function", {
   expect_error(mtcars_s %>% hypothesize(null = "independence"))
   expect_error(mtcars_s %>% hypothesize(null = "point"))
   # Produces error on win-build
-#  expect_warning(mtcars_s %>%
-#                   hypothesize(null = c("point", "independence"), mu = 3))
-
-  expect_error(mtcars %>% dplyr::select(vs) %>%
-                 hypothesize(null = "point", mu = 1))
-
-  expect_error(mtcars %>% specify(response = vs) %>%
-                 hypothesize(null = "point", mu = 1))
-
-  expect_error(mtcars %>% specify(response = vs, success = "1") %>%
-                 hypothesize(null = "point", p = 1.1))
-  expect_error(mtcars %>% specify(response = vs, success = "1") %>%
-                 hypothesize(null = "point", p = -23))
-
-  expect_error(mtcars_s %>%
-                 hypothesize(null = "point",
-                             p = c("4" = .2, "6" = .25, "8" = .25)))
+#   expect_warning(
+#     mtcars_s %>% hypothesize(null = c("point", "independence"), mu = 3)
+#   )
+
+  expect_error(
+    mtcars %>% dplyr::select(vs) %>% hypothesize(null = "point", mu = 1)
+  )
+
+  expect_error(
+    mtcars %>% specify(response = vs) %>% hypothesize(null = "point", mu = 1)
+  )
+
+  expect_error(
+    mtcars %>%
+      specify(response = vs, success = "1") %>%
+      hypothesize(null = "point", p = 1.1)
+  )
+  expect_error(
+    mtcars %>%
+      specify(response = vs, success = "1") %>%
+      hypothesize(null = "point", p = -23)
+  )
+
+  expect_error(
+    mtcars_s %>%
+      hypothesize(
+        null = "point", p = c("4" = .2, "6" = .25, "8" = .25)
+      )
+  )
 
   expect_error(mtcars_s %>% hypothesize(null = "point", p = 0.2))
-  expect_warning(mtcars %>% specify(mpg ~ vs) %>%
-                   hypothesize(null = "independence", p = 0.5))
+  expect_warning(
+    mtcars %>%
+      specify(mpg ~ vs) %>%
+      hypothesize(null = "independence", p = 0.5)
+  )
 
   expect_error(mtcars_s %>% hypothesize())
 })
 
 test_that("params correct", {
-  expect_error(hypothesize(one_prop_specify,
-                           null = "point", mu = 2))
-  expect_error(hypothesize(one_mean_specify,
-                           null = "point", mean = 0.5))
+  expect_error(hypothesize(one_prop_specify, null = "point", mu = 2))
+  expect_error(hypothesize(one_mean_specify, null = "point", mean = 0.5))
 })
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index 7ea6d232..a24951a5 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -1,14 +1,13 @@
 context("p_value")
 
 iris_tbl <- iris %>%
-  dplyr::mutate(Sepal.Length.Group =
-                  dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-                Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
+    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+  )
 
 iris_calc <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group,
-          success = "<=5") %>%
+  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000) %>%
   calculate(stat = "diff in props", order = c("large", "small"))
@@ -17,10 +16,7 @@ set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("direction is appropriate", {
-  expect_error(
-    test_df %>%
-      p_value(obs_stat = 0.5, direction = "righ")
-  )
+  expect_error(test_df %>% p_value(obs_stat = 0.5, direction = "righ"))
 })
 
 test_that("p_value makes sense", {
@@ -38,8 +34,7 @@ test_that("p_value makes sense", {
   )
   expect_equal(
     iris_calc %>%
-      p_value(obs_stat = median(iris_calc$stat),
-              direction = "both") %>%
+      p_value(obs_stat = median(iris_calc$stat), direction = "both") %>%
       dplyr::pull(),
     expected = 1
   )
@@ -57,8 +52,7 @@ test_that("p_value makes sense", {
   )
   expect_equal(
     iris_calc %>%
-      get_pvalue(obs_stat = median(iris_calc$stat) + 1,
-              direction = "two_sided") %>%
+      get_pvalue(obs_stat = median(iris_calc$stat) + 1, direction = "two_sided") %>%
       dplyr::pull(),
     expected = 0
   )
diff --git a/tests/testthat/test-print.R b/tests/testthat/test-print.R
index 8933bde3..118287bc 100644
--- a/tests/testthat/test-print.R
+++ b/tests/testthat/test-print.R
@@ -1,8 +1,10 @@
 context("print")
 
 test_that("print works", {
-  expect_output(print(iris %>%
-    specify(Sepal.Length ~ Sepal.Width) %>%
-    hypothesize(null = "independence") %>%
-    generate(reps = 10, type = "permute")))
+  expect_output(print(
+    iris %>%
+      specify(Sepal.Length ~ Sepal.Width) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 10, type = "permute")
+  ))
 })
diff --git a/tests/testthat/test-rep_sample_n.R b/tests/testthat/test-rep_sample_n.R
index a6e0eca9..ef9fae6d 100644
--- a/tests/testthat/test-rep_sample_n.R
+++ b/tests/testthat/test-rep_sample_n.R
@@ -7,18 +7,20 @@ population <- tibble::data_frame(
 )
 
 test_that("rep_sample_n works", {
-  expect_silent(population %>%
-                  rep_sample_n(size = 2, reps = 10))
-  expect_error(population %>%
-                 rep_sample_n(size = 2, reps = 10,
-                              prob = rep(x = 1/5, times = 100)))
-  expect_error(population %>%
-                 rep_sample_n(size = 2, reps = 10,
-                              prob = c(1/2, 1/2)))
-  expect_error(population %>%
-                 rep_sample_n(size = 2, reps = 10,
-                              prob = c(0.25, 1/5, 1/5, 1/5, 0.15)))
-  test_rep <- population %>%
-    rep_sample_n(size = 2, reps = 10)
+  expect_silent(population %>% rep_sample_n(size = 2, reps = 10))
+  expect_error(
+    population %>%
+      rep_sample_n(size = 2, reps = 10, prob = rep(x = 1/5, times = 100))
+  )
+  expect_error(
+    population %>%
+      rep_sample_n(size = 2, reps = 10, prob = c(1/2, 1/2))
+  )
+  expect_error(
+    population %>%
+      rep_sample_n(size = 2, reps = 10, prob = c(0.25, 1/5, 1/5, 1/5, 0.15))
+  )
+  
+  test_rep <- population %>% rep_sample_n(size = 2, reps = 10)
   expect_equal(c("replicate", names(population)), names(test_rep))
 })
diff --git a/tests/testthat/test-specify.R b/tests/testthat/test-specify.R
index f858e3be..001f14ec 100644
--- a/tests/testthat/test-specify.R
+++ b/tests/testthat/test-specify.R
@@ -1,26 +1,20 @@
 context("specify")
 
 mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(cyl = factor(cyl),
-                vs = factor(vs),
-                am = factor(am),
-                gear = factor(gear),
-                carb = factor(carb))
+  dplyr::mutate(
+    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
+    carb = factor(carb)
+  )
 
-one_nonshift_mean <- mtcars %>%
-  specify(response = mpg)
+one_nonshift_mean <- mtcars %>% specify(response = mpg)
 
-one_nonshift_prop <- mtcars %>%
-  specify(response = am, success = "1")
+one_nonshift_prop <- mtcars %>% specify(response = am, success = "1")
 
-two_means_boot <- mtcars %>%
-  specify(mpg ~ am)
+two_means_boot <- mtcars %>% specify(mpg ~ am)
 
-two_props_boot <- mtcars %>%
-  specify(am ~ vs, success = "1")
+two_props_boot <- mtcars %>% specify(am ~ vs, success = "1")
 
-slope_boot <- mtcars %>%
-  specify(mpg ~ hp)
+slope_boot <- mtcars %>% specify(mpg ~ hp)
 
 test_that("auto `type` works (specify)", {
   expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 6973527d..b69a6b39 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -2,7 +2,6 @@ context("visualize")
 
 library(dplyr)
 
-
 Sepal.Width_resamp <- iris %>%
   specify(Sepal.Width ~ NULL) %>%
   hypothesize(null = "point", med = 3) %>%
@@ -10,13 +9,12 @@ Sepal.Width_resamp <- iris %>%
   calculate(stat = "median")
 
 iris_tbl <- tibble::as_tibble(iris) %>%
-  dplyr::mutate(Sepal.Length.Group =
-                  dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-                Sepal.Width.Group =
-                  dplyr::if_else(Sepal.Width > 3, "large", "small"))
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
+    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+  )
 
-obs_slope <- lm(Sepal.Length ~ Sepal.Width,
-                   data = iris_tbl) %>%
+obs_slope <- lm(Sepal.Length ~ Sepal.Width, data = iris_tbl) %>%
   broom::tidy() %>%
   dplyr::filter(term == "Sepal.Width") %>%
   dplyr::select(estimate) %>%
@@ -28,11 +26,14 @@ obs_diff <- iris_tbl %>%
   summarize(diff(prop)) %>%
   pull()
 
-obs_z <- sqrt(stats::prop.test(x = table(iris_tbl$Sepal.Length.Group,
-                                    iris_tbl$Sepal.Width.Group),
-                          n = nrow(iris_tbl),
-                          alternative = "two.sided",
-                          correct = FALSE)$statistic)
+obs_z <- sqrt(
+  stats::prop.test(
+    x = table(iris_tbl$Sepal.Length.Group, iris_tbl$Sepal.Width.Group),
+    n = nrow(iris_tbl),
+    alternative = "two.sided",
+    correct = FALSE
+  )$statistic
+)
 
 obs_diff_mean <- iris_tbl %>%
   group_by(Sepal.Length.Group) %>%
@@ -41,8 +42,7 @@ obs_diff_mean <- iris_tbl %>%
   pull()
 
 obs_t <- iris_tbl %>%
-  t_stat(Sepal.Width ~ Sepal.Length.Group,
-         order = c(">5", "<=5"))
+  t_stat(Sepal.Width ~ Sepal.Length.Group, order = c(">5", "<=5"))
 
 obs_F <- anova(
     aov(formula = Sepal.Width ~ Species, data = iris_tbl)
@@ -51,219 +51,238 @@ obs_F <- anova(
 
 test_that("visualize basic tests", {
   expect_silent(visualize(Sepal.Width_resamp))
-  expect_error(
-    Sepal.Width_resamp %>% visualize(bins = "yep")
+  expect_error(Sepal.Width_resamp %>% visualize(bins = "yep"))
+  expect_silent(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Sepal.Width) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "slope") %>%
+      visualize(obs_stat = obs_slope, direction = "right")
   )
-  expect_silent(iris_tbl %>%
-                  specify(Sepal.Length ~ Sepal.Width) %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "slope") %>%
-                  visualize(obs_stat = obs_slope, direction = "right"))
 
   # obs_stat not specified
-  expect_error(iris_tbl %>%
-                 specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                         success = "large") %>%
-                 hypothesize(null = "independence") %>%
-                 generate(reps = 100, type = "permute") %>%
-                 calculate(stat = "diff in props",
-                           order = c(">5", "<=5")) %>%
-                 visualize(direction = "both"))
-
-  expect_silent(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "diff in props",
-                            order = c(">5", "<=5")) %>%
-                  visualize(direction = "both", obs_stat = obs_diff))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-                   calculate(stat = "z", order = c(">5", "<=5")) %>%
-                   visualize(method = "theoretical"))
+  expect_error(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "diff in props", order = c(">5", "<=5")) %>%
+      visualize(direction = "both")
+  )
+
+  expect_silent(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "diff in props", order = c(">5", "<=5")) %>%
+      visualize(direction = "both", obs_stat = obs_diff)
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      calculate(stat = "z", order = c(">5", "<=5")) %>%
+      visualize(method = "theoretical")
+  )
 
   # diff in props and z on different scales
-  expect_error(expect_warning(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "diff in props",
-                            order = c(">5", "<=5")) %>%
-                  visualize(method = "both", direction = "both",
-                            obs_stat = obs_diff)
-  ))
-
-  expect_silent(iris_tbl %>%
-                  specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "diff in props",
-                            order = c(">5", "<=5")) %>%
-                  visualize())
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                          success = "large") %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 100, type = "permute") %>%
-                  calculate(stat = "z",
-                            order = c(">5", "<=5")) %>%
-                  visualize(method = "both", direction = "both",
-                            obs_stat = obs_z))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Sepal.Length.Group,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "z",
-                             order = c("<=5", ">5")) %>%
-                   visualize(method = "both", direction = "both",
-                             obs_stat = -obs_z))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "t", order = c("small", "large")) %>%
-                   visualize(method = "both", direction = "left",
-                             obs_stat = -obs_t))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Sepal.Width.Group) %>%
-                   hypothesize(null = "independence") %>%
-#                    generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "t", order = c("small", "large")) %>%
-                   visualize(method = "theoretical", direction = "left",
-                             obs_stat = -obs_t))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Sepal.Length.Group) %>%
-                   hypothesize(null = "independence") %>%
-                   visualize(method = "theoretical"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Species) %>%
-                   hypothesize(null = "independence") %>%
-                   visualize(method = "theoretical"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Species) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "F") %>%
-                   visualize(method = "both", obs_stat = obs_F,
-                             direction = "right"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Species) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "F") %>%
-                   visualize(method = "both", obs_stat = obs_F,
-                             direction = "left"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Species,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "Chisq") %>%
-                   visualize(method = "both", obs_stat = obs_F,
-                             direction = "right"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Species,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-                   # calculate(stat = "Chisq") %>%
-                   visualize(method = "theoretical", obs_stat = obs_F,
-                             direction = "right"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Species ~ NULL) %>%
-                   hypothesize(null = "point",
-                               p = c("setosa" = 0.4,
-                                     "versicolor" = 0.4,
-                                     "virginica" = 0.2)) %>%
-                   generate(reps = 100, type = "simulate") %>%
-                   calculate(stat = "Chisq") %>%
-                   visualize(method = "both"))
+  expect_error(
+    expect_warning(
+      iris_tbl %>%
+        specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+        hypothesize(null = "independence") %>%
+        generate(reps = 100, type = "permute") %>%
+        calculate(stat = "diff in props", order = c(">5", "<=5")) %>%
+        visualize(method = "both", direction = "both", obs_stat = obs_diff)
+    )
+  )
+
+  expect_silent(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "diff in props", order = c(">5", "<=5")) %>%
+      visualize()
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "z", order = c(">5", "<=5")) %>%
+      visualize(method = "both", direction = "both", obs_stat = obs_z)
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "z", order = c("<=5", ">5")) %>%
+      visualize(method = "both", direction = "both", obs_stat = -obs_z)
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Sepal.Width.Group) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "t", order = c("small", "large")) %>%
+      visualize(method = "both", direction = "left", obs_stat = -obs_t)
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Sepal.Width.Group) %>%
+      hypothesize(null = "independence") %>%
+#       generate(reps = 100, type = "permute") %>%
+      calculate(stat = "t", order = c("small", "large")) %>%
+      visualize(method = "theoretical", direction = "left", obs_stat = -obs_t)
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Sepal.Length.Group) %>%
+      hypothesize(null = "independence") %>%
+      visualize(method = "theoretical")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Species) %>%
+      hypothesize(null = "independence") %>%
+      visualize(method = "theoretical")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Species) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "F") %>%
+      visualize(method = "both", obs_stat = obs_F, direction = "right")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Species) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "F") %>%
+      visualize(method = "both", obs_stat = obs_F, direction = "left")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Species, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "Chisq") %>%
+      visualize(method = "both", obs_stat = obs_F, direction = "right")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Species, success = "large") %>%
+      hypothesize(null = "independence") %>%
+#       calculate(stat = "Chisq") %>%
+      visualize(method = "theoretical", obs_stat = obs_F, direction = "right")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Species ~ NULL) %>%
+      hypothesize(
+        null = "point",
+        p = c("setosa" = 0.4, "versicolor" = 0.4, "virginica" = 0.2)
+      ) %>%
+      generate(reps = 100, type = "simulate") %>%
+      calculate(stat = "Chisq") %>%
+      visualize(method = "both"))
 
   # traditional instead of theoretical
-  expect_error(iris_tbl %>%
-                 specify(Species ~ NULL) %>%
-                 hypothesize(null = "point",
-                             p = c("setosa" = 0.4,
-                                   "versicolor" = 0.4,
-                                   "virginica" = 0.2)) %>%
-#                 generate(reps = 100, type = "simulate") %>%
-#                 calculate(stat = "Chisq") %>%
-                 visualize(method = "traditional"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Species ~ NULL) %>%
-                   hypothesize(null = "point",
-                               p = c("setosa" = 0.4,
-                                     "versicolor" = 0.4,
-                                     "virginica" = 0.2)) %>%
-                   # generate(reps = 100, type = "simulate") %>%
-                   # calculate(stat = "Chisq") %>%
-                   visualize(method = "theoretical"))
-
-  expect_silent(iris_tbl %>%
-                  specify(Petal.Width ~ Sepal.Width.Group) %>%
-                  hypothesize(null = "independence") %>%
-                  generate(reps = 10, type = "permute") %>%
-                  calculate(stat = "diff in means",
-                            order = c("large", "small")) %>%
-                  visualize(direction = "both",
-                            obs_stat = obs_diff_mean)
+  expect_error(
+    iris_tbl %>%
+      specify(Species ~ NULL) %>%
+      hypothesize(
+        null = "point",
+        p = c("setosa" = 0.4, "versicolor" = 0.4, "virginica" = 0.2)
+      ) %>%
+#       generate(reps = 100, type = "simulate") %>%
+#       calculate(stat = "Chisq") %>%
+      visualize(method = "traditional")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Species ~ NULL) %>%
+      hypothesize(
+        null = "point",
+        p = c("setosa" = 0.4, "versicolor" = 0.4, "virginica" = 0.2)
+      ) %>%
+#       generate(reps = 100, type = "simulate") %>%
+#       calculate(stat = "Chisq") %>%
+      visualize(method = "theoretical")
+  )
+
+  expect_silent(
+    iris_tbl %>%
+      specify(Petal.Width ~ Sepal.Width.Group) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 10, type = "permute") %>%
+      calculate(stat = "diff in means", order = c("large", "small")) %>%
+      visualize(direction = "both",obs_stat = obs_diff_mean)
   )
 
   # Produces warning first for not checking conditions but would also error
-  expect_error(expect_warning(iris_tbl %>%
-                   specify(Petal.Width ~ Sepal.Width.Group) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "diff in means",
-                             order = c("large", "small")) %>%
-                   visualize(method = "both", direction = "both",
-                             obs_stat = obs_diff_mean)
-  ))
-
-  expect_warning(iris_tbl %>%
-                   specify(Petal.Width ~ Sepal.Width.Group) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "diff in means",
-                             order = c("large", "small")) %>%
-                   visualize(method = "theoretical", direction = "both",
-                             obs_stat = obs_diff_mean))
-
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ NULL, success = "small") %>%
-                   hypothesize(null = "point", p = 0.8) %>%
-#                    generate(reps = 100, type = "simulate") %>%
-#                    calculate(stat = "z") %>%
-                   visualize(method = "theoretical",
-                             obs_stat = 2, # Should probably update
-                             direction = "both"))
-
-  expect_silent(iris_tbl %>%
-                  specify(Petal.Width ~ NULL) %>%
-                  hypothesize(null = "point", mu = 1.3) %>%
-                  generate(reps = 100, type = "bootstrap") %>%
-                  calculate(stat = "mean") %>%
-                  visualize(direction = "left",
-                            obs_stat = mean(iris$Petal.Width)))
+  expect_error(
+    expect_warning(
+      iris_tbl %>%
+        specify(Petal.Width ~ Sepal.Width.Group) %>%
+        hypothesize(null = "independence") %>%
+        generate(reps = 100, type = "permute") %>%
+        calculate(stat = "diff in means", order = c("large", "small")) %>%
+        visualize(method = "both", direction = "both", obs_stat = obs_diff_mean)
+    )
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Petal.Width ~ Sepal.Width.Group) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "diff in means", order = c("large", "small")) %>%
+      visualize(
+        method = "theoretical", direction = "both", obs_stat = obs_diff_mean
+      )
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ NULL, success = "small") %>%
+      hypothesize(null = "point", p = 0.8) %>%
+#       generate(reps = 100, type = "simulate") %>%
+#       calculate(stat = "z") %>%
+      visualize(
+        method = "theoretical",
+        obs_stat = 2, # Should probably update
+        direction = "both"
+      )
+  )
+
+  expect_silent(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 1.3) %>%
+      generate(reps = 100, type = "bootstrap") %>%
+      calculate(stat = "mean") %>%
+      visualize(direction = "left", obs_stat = mean(iris$Petal.Width))
+  )
 })
 
 test_that("get_percentile works", {
@@ -272,101 +291,105 @@ test_that("get_percentile works", {
 
 test_that("obs_stat as a data.frame works", {
   mean_petal_width <- iris_tbl %>%
-                  specify(Petal.Width ~ NULL) %>%
-                  calculate(stat = "mean")
-  expect_silent(iris_tbl %>%
-                  specify(Petal.Width ~ NULL) %>%
-                  hypothesize(null = "point", mu = 4) %>%
-                  generate(reps = 100, type = "bootstrap") %>%
-                  calculate(stat = "mean") %>%
-                  visualize(obs_stat = mean_petal_width))
+    specify(Petal.Width ~ NULL) %>%
+    calculate(stat = "mean")
+  expect_silent(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 4) %>%
+      generate(reps = 100, type = "bootstrap") %>%
+      calculate(stat = "mean") %>%
+      visualize(obs_stat = mean_petal_width)
+  )
   mean_df_test <- data.frame(x = c(4.1, 1), y = c(1, 2))
-  expect_warning(iris_tbl %>%
-                   specify(Petal.Width ~ NULL) %>%
-                   hypothesize(null = "point", mu = 4) %>%
-                   generate(reps = 100, type = "bootstrap") %>%
-                   calculate(stat = "mean") %>%
-                   visualize(obs_stat = mean_df_test))
+  expect_warning(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 4) %>%
+      generate(reps = 100, type = "bootstrap") %>%
+      calculate(stat = "mean") %>%
+      visualize(obs_stat = mean_df_test)
+  )
 })
 
 
 test_that('method = "both" behaves nicely', {
-  # stop_glue('`generate()` and `calculate()` are both required ',
-  #           'to be done prior to `visualize(method = "both")`')
-  expect_error(iris_tbl %>%
-                 specify(Petal.Width ~ NULL) %>%
-                 hypothesize(null = "point", mu = 4) %>%
-                 generate(reps = 100, type = "bootstrap") %>%
-#                  calculate(stat = "mean") %>%
-                 visualize(method = "both"))
-
-  expect_warning(iris_tbl %>%
-                   specify(Petal.Width ~ Sepal.Length.Group) %>%
-                   hypothesize(null = "point", mu = 4) %>%
-                   generate(reps = 10, type = "bootstrap") %>%
-                   calculate(stat = "t", order = c(">5", "<=5")) %>%
-                   visualize(method = "both"))
+  # stop_glue(
+  #   '`generate()` and `calculate()` are both required to be done prior ',
+  #   'to `visualize(method = "both")`'
+  # )
+  expect_error(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 4) %>%
+      generate(reps = 100, type = "bootstrap") %>%
+#       calculate(stat = "mean") %>%
+      visualize(method = "both")
+  )
+
+  expect_warning(
+    iris_tbl %>%
+      specify(Petal.Width ~ Sepal.Length.Group) %>%
+      hypothesize(null = "point", mu = 4) %>%
+      generate(reps = 10, type = "bootstrap") %>%
+      calculate(stat = "t", order = c(">5", "<=5")) %>%
+      visualize(method = "both")
+  )
 })
 
 test_that("Traditional right-tailed tests have warning if not right-tailed", {
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Species,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "Chisq") %>%
-                   visualize(method = "both", obs_stat = 2, direction = "left"))
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Species) %>%
-                   hypothesize(null = "independence") %>%
-                   generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "F") %>%
-                   visualize(method = "both", obs_stat = 2,
-                             direction = "two_sided"))
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Width.Group ~ Species,
-                           success = "large") %>%
-                   hypothesize(null = "independence") %>%
-#                    generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "Chisq") %>%
-                   visualize(method = "theoretical", obs_stat = 2,
-                             direction = "left"))
-  expect_warning(iris_tbl %>%
-                   specify(Sepal.Length ~ Species) %>%
-                   hypothesize(null = "independence") %>%
-#                    generate(reps = 100, type = "permute") %>%
-                   calculate(stat = "F") %>%
-                   visualize(method = "theoretical", obs_stat = 2,
-                             direction = "two_sided"))
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Species, success = "large") %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "Chisq") %>%
+      visualize(method = "both", obs_stat = 2, direction = "left")
+  )
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Species) %>%
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "permute") %>%
+      calculate(stat = "F") %>%
+      visualize(method = "both", obs_stat = 2, direction = "two_sided")
+  )
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Width.Group ~ Species, success = "large") %>%
+      hypothesize(null = "independence") %>%
+#       generate(reps = 100, type = "permute") %>%
+      calculate(stat = "Chisq") %>%
+      visualize(method = "theoretical", obs_stat = 2, direction = "left")
+  )
+  expect_warning(
+    iris_tbl %>%
+      specify(Sepal.Length ~ Species) %>%
+      hypothesize(null = "independence") %>%
+#       generate(reps = 100, type = "permute") %>%
+      calculate(stat = "F") %>%
+      visualize(method = "theoretical", obs_stat = 2, direction = "two_sided")
+  )
 })
 
 test_that("confidence interval plots are working", {
   iris_boot <- iris_tbl %>%
-    specify(Sepal.Width.Group ~ Sepal.Length.Group,
-            success = "large") %>%
+    specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
     generate(reps = 100) %>%
-    calculate(stat = "diff in props",
-              order = c(">5", "<=5"))
+    calculate(stat = "diff in props", order = c(">5", "<=5"))
 
   df_error <- tibble::tibble(col1 = rnorm(5), col2 = rnorm(5))
   vec_error <- 1:10
 
   perc_ci <- iris_boot %>% get_ci()
 
-  expect_error(
-    iris_boot %>% visualize(endpoints = df_error)
-  )
+  expect_error(iris_boot %>% visualize(endpoints = df_error))
 
-  expect_warning(
-    iris_boot %>% visualize(endpoints = vec_error)
-  )
+  expect_warning(iris_boot %>% visualize(endpoints = vec_error))
 
   expect_silent(
-    iris_boot %>% visualize(endpoints = perc_ci,
-                            direction = "between")
+    iris_boot %>% visualize(endpoints = perc_ci, direction = "between")
   )
 
-  expect_warning(
-    iris_boot %>% visualize(obs_stat = 3, endpoints = perc_ci)
-  )
+  expect_warning(iris_boot %>% visualize(obs_stat = 3, endpoints = perc_ci))
 })
diff --git a/tests/testthat/test-wrappers.R b/tests/testthat/test-wrappers.R
index 028ebaa7..6f676e75 100644
--- a/tests/testthat/test-wrappers.R
+++ b/tests/testthat/test-wrappers.R
@@ -5,48 +5,47 @@ iris2 <- iris %>%
   droplevels(.$Species)
 
 iris3 <- iris %>%
-  dplyr::mutate(Sepal.Length.Group =
-                  dplyr::if_else(Sepal.Length > 5, ">5", "<=5"))
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+  )
 
 test_that("t_test works", {
   # order is missing
   expect_error(iris2 %>% t_test(Sepal.Width ~ Species))
 
-  expect_error(iris2 %>% t_test(response = "Sepal.Width",
-                                explanatory = "Species"))
+  expect_error(
+    iris2 %>% t_test(response = "Sepal.Width", explanatory = "Species")
+  )
 ## Not implemented
-#  expect_silent(iris2 %>% t_test(response = Sepal.Width,
-#                                 explanatory = Species))
+#   expect_silent(
+#     iris2 %>% t_test(response = Sepal.Width, explanatory = Species)
+#   )
 })
 
 test_that("chisq_test works", {
   expect_silent(iris3 %>% chisq_test(Sepal.Length.Group ~ Species))
   new_way <- iris3 %>% chisq_test(Sepal.Length.Group ~ Species)
-  old_way <- chisq.test(x = table(iris3$Species,
-                                  iris3$Sepal.Length.Group)) %>%
+  old_way <- chisq.test(x = table(iris3$Species, iris3$Sepal.Length.Group)) %>%
     broom::glance() %>%
     dplyr::select(statistic, chisq_df = parameter, p_value = p.value)
 
   expect_equal(new_way, old_way)
   ## Not implemented
-  #  expect_silent(iris3 %>% chisq_test(response = Sepal.Length.Group,
-  #                                 explanatory = Species))
+  # expect_silent(
+  #   iris3 %>% chisq_test(response = Sepal.Length.Group, explanatory = Species)
+  # )
 })
 
 test_that("_stat functions work", {
   # Test of independence
-  expect_silent(
-    iris3 %>% chisq_stat(Sepal.Length.Group ~ Species)
-    )
+  expect_silent(iris3 %>% chisq_stat(Sepal.Length.Group ~ Species))
   another_way <- iris3 %>%
     chisq_test(Sepal.Length.Group ~ Species) %>%
     dplyr::select(statistic) %>%
     dplyr::rename(stat = statistic)
-  obs_stat_way <- iris3 %>%
-    chisq_stat(Sepal.Length.Group ~ Species)
+  obs_stat_way <- iris3 %>% chisq_stat(Sepal.Length.Group ~ Species)
   one_more <- chisq.test(
-    table(iris3$Species,
-                iris3$Sepal.Length.Group)
+    table(iris3$Species, iris3$Sepal.Length.Group)
   )$statistic
 
   expect_equivalent(another_way, obs_stat_way)
@@ -64,8 +63,9 @@ test_that("_stat functions work", {
 
   # Two sample t
   expect_silent(
-    iris2 %>% t_stat(Sepal.Width ~ Species,
-                                 order = c("virginica", "versicolor"))
+    iris2 %>% t_stat(
+      Sepal.Width ~ Species, order = c("virginica", "versicolor")
+    )
   )
   another_way <- iris2 %>%
     t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor")) %>%
@@ -86,31 +86,43 @@ test_that("_stat functions work", {
 
 test_that("conf_int argument works", {
   expect_equal(
-    names(iris2 %>%
-      t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor"),
-             conf_int = FALSE)),
+    names(
+      iris2 %>%
+        t_test(
+          Sepal.Width ~ Species, order = c("virginica", "versicolor"),
+          conf_int = FALSE
+        )
+    ),
     c("statistic", "t_df", "p_value", "alternative")
   )
   expect_equal(
-    names(iris2 %>%
-            t_test(Sepal.Width ~ Species, order = c("virginica", "versicolor"),
-                   conf_int = TRUE)),
+    names(
+      iris2 %>%
+        t_test(
+          Sepal.Width ~ Species, order = c("virginica", "versicolor"),
+          conf_int = TRUE
+        )
+    ),
     c("statistic", "t_df", "p_value", "alternative", "lower_ci", "upper_ci")
   )
 
   ci_test <- iris2 %>%
-    t_test(Sepal.Width ~ Species, order = c("versicolor", "virginica"),
-           conf_int = TRUE, conf_level = 0.9)
-  old_way <- t.test(formula = Sepal.Width ~ Species,
-                    data = iris2,
-                    conf.level = 0.9)[["conf.int"]]
+    t_test(
+      Sepal.Width ~ Species, order = c("versicolor", "virginica"),
+      conf_int = TRUE, conf_level = 0.9
+    )
+  old_way <- t.test(
+    formula = Sepal.Width ~ Species, data = iris2, conf.level = 0.9
+  )[["conf.int"]]
   expect_equal(ci_test$lower_ci[1], old_way[1])
   expect_equal(ci_test$upper_ci[1], old_way[2])
 
   expect_error(
     iris2 %>%
-      t_test(Petal.Width ~ Species, order = c("versicolor", "virginica"),
-             conf_int = TRUE, conf_level = 1.1)
+      t_test(
+        Petal.Width ~ Species, order = c("versicolor", "virginica"),
+        conf_int = TRUE, conf_level = 1.1
+      )
   )
 
   # Check that var.equal produces different results
@@ -120,11 +132,11 @@ test_that("conf_int argument works", {
   no_var_equal <- iris_small %>%
     t_stat(Petal.Width ~ Species, order = c("versicolor", "virginica"))
   var_equal <- iris_small %>%
-    t_stat(Petal.Width ~ Species, order = c("versicolor", "virginica"),
-           var.equal = TRUE)
-  expect_false(
-    no_var_equal == var_equal
-  )
+    t_stat(
+      Petal.Width ~ Species, order = c("versicolor", "virginica"),
+      var.equal = TRUE
+    )
+  expect_false(no_var_equal == var_equal)
 
   shortcut_no_var_equal <- iris_small %>%
     specify(Petal.Width ~ Species) %>%
@@ -132,9 +144,9 @@ test_that("conf_int argument works", {
 
   shortcut_var_equal <- iris_small %>%
     specify(Petal.Width ~ Species) %>%
-    calculate(stat = "t", order = c("versicolor", "virginica"),
-              var.equal = TRUE)
-  expect_false(
-    shortcut_no_var_equal == shortcut_var_equal
-  )
+    calculate(
+      stat = "t", order = c("versicolor", "virginica"),
+      var.equal = TRUE
+    )
+  expect_false(shortcut_no_var_equal == shortcut_var_equal)
 })

From ce92b84bc130c1879f616f663d7872336024b395 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 17:06:30 +0300
Subject: [PATCH 06/78] Remove unnecessary `return()`s.

---
 R/calculate.R   |  2 +-
 R/conf_int.R    |  2 +-
 R/generate.R    | 12 ++++++------
 R/hypothesize.R |  2 +-
 R/p_value.R     |  6 +++---
 R/specify.R     |  2 +-
 R/utils.R       |  8 ++++----
 R/wrappers.R    |  2 +-
 8 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 488b3db0..c6b8a84e 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -117,7 +117,7 @@ calculate <- function(x,
     result <- select(result, stat)
   }
 
-  return(result)
+  result
 }
 
 calc_impl <- function(type, x, order, ...) {
diff --git a/R/conf_int.R b/R/conf_int.R
index 089fdff9..9e24b3f1 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -56,7 +56,7 @@ conf_int <- function(x, level = 0.95, type = "percentile",
     )
   }
 
-  return(ci)
+  ci
 }
 
 check_ci_args <- function(x, level, type, point_estimate) {
diff --git a/R/generate.R b/R/generate.R
index ed0c475d..20a265e5 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -70,11 +70,11 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 #  }
 
   if (type == "bootstrap") {
-    return(bootstrap(x, reps, ...))
+    bootstrap(x, reps, ...)
   } else if (type == "permute") {
-    return(permute(x, reps, ...))
+    permute(x, reps, ...)
   } else if (type == "simulate") {
-    return(simulate(x, reps, ...))
+    simulate(x, reps, ...)
   } # else if (!(type %in% c("bootstrap", "permute", "simulate"))) {
     # stop_glue(
     #   "Choose one of the available options for `type`: ",
@@ -130,7 +130,7 @@ bootstrap <- function(x, reps = 1, ...) {
 
   class(result) <- append("infer", class(result))
 
-  return(result)
+  result
 }
 
 #' @importFrom dplyr bind_rows group_by
@@ -144,7 +144,7 @@ permute <- function(x, reps = 1, ...) {
 
   class(df_out) <- append("infer", class(df_out))
 
-  return(df_out)
+  df_out
 }
 
 permute_once <- function(x, ...) {
@@ -180,5 +180,5 @@ simulate <- function(x, reps = 1, ...) {
 
   class(rep_tbl) <- append("infer", class(rep_tbl))
 
-  return(dplyr::group_by(rep_tbl, replicate))
+  dplyr::group_by(rep_tbl, replicate)
 }
diff --git a/R/hypothesize.R b/R/hypothesize.R
index 48cd20dc..4902d2a2 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -83,5 +83,5 @@ hypothesize <- function(x, null, ...) {
   #   }
   # }
 
-  return(tibble::as_tibble(x))
+  tibble::as_tibble(x)
 }
diff --git a/R/p_value.R b/R/p_value.R
index 09026d02..cd5f8af9 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -65,7 +65,7 @@ p_value <- function(x, obs_stat, direction) {
   #   )
   # }
 
-  return(pvalue)
+  pvalue
 }
 
 simulation_based_p_value <- function(x, obs_stat, direction) {
@@ -100,9 +100,9 @@ two_sided_p_value <- function(x, obs_stat) {
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
     # scenario instead of using >=
-    return(tibble::tibble(p_value = 1))
+    tibble::tibble(p_value = 1)
   } else {
-    return(tibble::tibble(p_value = basic_p_value))
+    tibble::tibble(p_value = basic_p_value)
   }
 }
 
diff --git a/R/specify.R b/R/specify.R
index 1293be5c..854559c8 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -157,5 +157,5 @@ specify <- function(x, formula, response = NULL,
   # add "infer" class
   class(x) <- append("infer", class(x))
 
-  return(x)
+  x
 }
diff --git a/R/utils.R b/R/utils.R
index 26c7d307..2dfbac5d 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,12 +1,12 @@
 format_params <- function(x) {
   par_levels <- get_par_levels(x)
   fct_levels <- as.character(unique(dplyr::pull(x, !!attr(x, "response"))))
-  return(attr(x, "params")[match(fct_levels, par_levels)])
+  attr(x, "params")[match(fct_levels, par_levels)]
 }
 
 get_par_levels <- function(x) {
   par_names <- names(attr(x, "params"))
-  return(gsub("^.\\.", "", par_names))
+  gsub("^.\\.", "", par_names)
 }
 
 set_attributes <- function(to, from = x) {
@@ -23,7 +23,7 @@ set_attributes <- function(to, from = x) {
   attr(to, "generate") <- attr(from, "generate")
   attr(to, "type") <- attr(from, "type")
 
-  return(to)
+  to
 }
 
 explanatory_variable <- function(x) {
@@ -275,7 +275,7 @@ parse_params <- function(dots, x) {
   #   warning_glue("Proportions do not sum to 1, normalizing automatically.")
   # }
 
-  return(unlist(dots))
+  unlist(dots)
 }
 
 hypothesize_checks <- function(x, null) {
diff --git a/R/wrappers.R b/R/wrappers.R
index 713876ee..b68b623e 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -89,7 +89,7 @@ t_test <- function(data, formula, # response = NULL, explanatory = NULL,
       )
   }
 
-    return(results)
+  results
 #   } else {
 #     data %>%
 #       stats::t.test(

From 5b6aeb635d8167d22e480877461e7cbb9a8f2825 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 5 Aug 2018 17:41:10 +0300
Subject: [PATCH 07/78] Update predicates inside `if`.

---
 R/calculate.R   | 15 +++++++++------
 R/conf_int.R    |  6 +++---
 R/generate.R    |  8 ++++----
 R/hypothesize.R |  2 +-
 R/set_params.R  | 24 +++++++++++-------------
 R/specify.R     | 12 +++++++-----
 R/utils.R       | 20 ++++++++++----------
 R/visualize.R   | 16 ++++++++--------
 R/wrappers.R    |  2 +-
 9 files changed, 54 insertions(+), 51 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index c6b8a84e..6c7b03a0 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -73,15 +73,17 @@ calculate <- function(x,
   }
   
   if (
-    stat %in% c("diff in means", "diff in medians", "diff in props") ||
-    (!is.null(attr(x, "theory_type")) &&
-    attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))
+    (stat %in% c("diff in means", "diff in medians", "diff in props")) ||
+    (
+      !is.null(attr(x, "theory_type")) &&
+      (attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))
+    )
   ) {
     check_order(x, explanatory_variable(x), order)
   }
 
   if (!(
-    stat %in% c("diff in means", "diff in medians", "diff in props") ||
+    (stat %in% c("diff in means", "diff in medians", "diff in props")) ||
     (
       !is.null(attr(x, "theory_type")) &&
       attr(x, "theory_type") %in% c("Two sample props z", "Two sample t")
@@ -327,7 +329,8 @@ calc_impl.t <- function(stat, x, order, ...) {
   # the standardization formulas are different.
   # # Standardized slope
   # else if (
-  #   (attr(x, "theory_type") == "Slope/correlation with t") && stat == "slope"
+  #   (attr(x, "theory_type") == "Slope/correlation with t") &&
+  #   (stat == "slope")
   # ) {
   #   explan_string <- as.character(attr(x, "explanatory"))
   # 
@@ -342,7 +345,7 @@ calc_impl.t <- function(stat, x, order, ...) {
   # # Standardized correlation
   # else if (
   #   (attr(x, "theory_type") == "Slope/correlation with t") &&
-  #   stat == "correlation"
+  #   (stat == "correlation")
   # ) {
   #   x %>%
   #     dplyr::summarize(
diff --git a/R/conf_int.R b/R/conf_int.R
index 9e24b3f1..2632bf51 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -69,7 +69,7 @@ check_ci_args <- function(x, level, type, point_estimate) {
   }
   check_type(x, is.data.frame)
   check_type(level, is.numeric)
-  if (level <= 0 || level >= 1) {
+  if ((level <= 0) || (level >= 1)) {
     stop_glue("The value of `level` must be between 0 and 1 non-inclusive.")
   }
 
@@ -77,13 +77,13 @@ check_ci_args <- function(x, level, type, point_estimate) {
     stop_glue('The options for `type` are "percentile" or "se".')
   }
 
-  if (type == "se" && is.null(point_estimate)) {
+  if ((type == "se") && is.null(point_estimate)) {
     stop_glue(
       'A numeric value needs to be given for `point_estimate` for `type = "se"'
     )
   }
 
-  if (type == "se" && is.vector(point_estimate)) {
+  if ((type == "se") && is.vector(point_estimate)) {
     check_type(point_estimate, is.numeric)
   }
 }
diff --git a/R/generate.R b/R/generate.R
index 20a265e5..16e54745 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -42,7 +42,7 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
   attr(x, "generate") <- TRUE
 
   if (
-    type == "permute" &&
+    (type == "permute") &&
     any(is.null(attr(x, "response")), is.null(attr(x, "explanatory")))
   ) {
     stop_glue(
@@ -52,14 +52,14 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
   }
 ## Can't get to these anymore with tests
 #   if (
-#     type == "simulate" &&
-#     attr(x, "null") != "point" &&
+#     (type == "simulate") &&
+#     (attr(x, "null") != "point") &&
 #     !(length(grep("p.", names(attr(x, "params")))) >= 1)
 #   ) {
 #     stop_glue("Simulation requires a `point` null hypothesis on proportions.")
 #   }
 #   if (
-#     type == "bootstrap" &&
+#     (type == "bootstrap") &&
 #     !(attr(attr(x, "params"), "names") %in% c("mu", "med", "sigma")) &&
 #     !is.null(attr(x, "null"))
 #   ) {
diff --git a/R/hypothesize.R b/R/hypothesize.R
index 4902d2a2..2603958d 100755
--- a/R/hypothesize.R
+++ b/R/hypothesize.R
@@ -52,7 +52,7 @@ hypothesize <- function(x, null, ...) {
 
   }
 
-  if (!is.null(null) && null == "independence") {
+  if (!is.null(null) && (null == "independence")) {
     attr(x, "type") <- "permute"
   }
 
diff --git a/R/set_params.R b/R/set_params.R
index 4a741843..0867a5f7 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -13,8 +13,7 @@ set_params <- function(x) {
   # One variable
   if (
     !is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
-    !is.null(attr(x, "response_type")) &&
-    is.null(attr(x, "explanatory_type"))
+    !is.null(attr(x, "response_type")) && is.null(attr(x, "explanatory_type"))
   ) {
 
     # One mean
@@ -29,7 +28,7 @@ set_params <- function(x) {
     } else if (
       
       # One prop
-      attr(x, "response_type") == "factor" && (num_response_levels == 2)
+      (attr(x, "response_type") == "factor") && (num_response_levels == 2)
     ) {
       # No parameters since standard normal
       attr(x, "theory_type") <- "One sample prop z"
@@ -45,15 +44,14 @@ set_params <- function(x) {
   # Two variables
   if (
     !is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
-    !is.null(attr(x, "response_type")) &&
-    !is.null(attr(x, "explanatory_type"))
+    !is.null(attr(x, "response_type")) && !is.null(attr(x, "explanatory_type"))
   ) {
     attr(x, "type") <- "bootstrap"
 
     # Response is numeric, explanatory is categorical
     if (
-      attr(x, "response_type") %in% c("integer", "numeric") &
-      attr(x, "explanatory_type") == "factor"
+      (attr(x, "response_type") %in% c("integer", "numeric")) &
+      (attr(x, "explanatory_type") == "factor")
     ) {
       
       # Two sample means (t distribution)
@@ -92,16 +90,16 @@ set_params <- function(x) {
 
     # Response is categorical, explanatory is categorical
     if (
-      attr(x, "response_type") == "factor" &
-      attr(x, "explanatory_type") == "factor"
+      (attr(x, "response_type") == "factor") &
+      (attr(x, "explanatory_type") == "factor")
     ) {
       attr(x, "type") <- "bootstrap"
 
       # Two sample proportions (z distribution)
       # Parameter(s) not needed since standard normal
       if (
-        length(levels(response_variable(x))) == 2 &
-        length(levels(explanatory_variable(x))) == 2
+        (length(levels(response_variable(x))) == 2) &
+        (length(levels(explanatory_variable(x))) == 2)
       ) {
         attr(x, "theory_type") <- "Two sample props z"
       } else {
@@ -122,8 +120,8 @@ set_params <- function(x) {
 
     # Response is numeric, explanatory is numeric
     if (
-      attr(x, "response_type") %in% c("integer", "numeric") &
-      attr(x, "explanatory_type") %in% c("integer", "numeric")
+      (attr(x, "response_type") %in% c("integer", "numeric")) &
+      (attr(x, "explanatory_type") %in% c("integer", "numeric"))
     ) {
       response_string <- as.character(attr(x, "response"))
       explanatory_string <- as.character(attr(x, "explanatory"))
diff --git a/R/specify.R b/R/specify.R
index 854559c8..937b3f09 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -39,7 +39,7 @@ specify <- function(x, formula, response = NULL,
     mutate_if(is.character, as.factor) %>%
     mutate_if(is.logical, as.factor)
 
-  if ((!methods::hasArg(formula) && !methods::hasArg(response))) {
+  if (!methods::hasArg(formula) && !methods::hasArg(response)) {
     stop_glue("Please give the `response` variable.")
   }
   if (methods::hasArg(formula)) {
@@ -137,12 +137,14 @@ specify <- function(x, formula, response = NULL,
   }
 
   if (
-    attr(x, "response_type") == "factor" && is.null(success) &&
-    length(levels(response_variable(x))) == 2 &&
+    (attr(x, "response_type") == "factor") && is.null(success) &&
+    (length(levels(response_variable(x))) == 2) &&
     (
       is.null(attr(x, "explanatory_type")) ||
-      (!is.null(attr(x, "explanatory_type")) &&
-       length(levels(explanatory_variable(x))) == 2)
+      (
+        !is.null(attr(x, "explanatory_type")) &&
+        (length(levels(explanatory_variable(x))) == 2)
+      )
     )
   ) {
     stop_glue(
diff --git a/R/utils.R b/R/utils.R
index 2dfbac5d..66af83d3 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -195,28 +195,28 @@ check_point_params <- function(x, stat) {
   hyp_text <- 'to be set in `hypothesize()`.'
   if (!is.null(attr(x, "null"))) {
     if (stat %in% c("mean", "median", "sd", "prop")) {
-      if ((stat == "mean" && !("mu" %in% param_names))) {
+      if ((stat == "mean") && !("mu" %in% param_names)) {
         stop_glue('`stat == "mean"` requires `"mu"` {hyp_text}')
       }
-      if ((!(stat == "mean") && ("mu" %in% param_names))) {
+      if (!(stat == "mean") && ("mu" %in% param_names)) {
         stop_glue('`"mu"` does not correspond to `stat = "{stat}"`.')
       }
-      if ((stat == "median" && !("med" %in% param_names))) {
+      if ((stat == "median") && !("med" %in% param_names)) {
         stop_glue('`stat == "median"` requires `"med"` {hyp_text}')
       }
-      if ((!(stat == "median") && ("med" %in% param_names))) {
+      if (!(stat == "median") && ("med" %in% param_names)) {
         stop_glue('`"med"` does not correspond to `stat = "{stat}"`.')
       }
       ## Tests unable to get to
-      # if ((stat == "sigma" && !("sd" %in% param_names))) {
+      # if ((stat == "sigma") && !("sd" %in% param_names)) {
       #   stop_glue('`stat == "sd"` requires `"sigma"` {hyp_text}')
       # }
-      if ((!(stat == "sd") && ("sigma" %in% param_names))) {
+      if (!(stat == "sd") && ("sigma" %in% param_names)) {
         stop_glue('`"sigma"` does not correspond to `stat = "{stat}"`.')
       }
 
       ## Tests unable to get to
-      # if (stat == "prop" && !(any(grepl("p.", param_names)))) {
+      # if ((stat == "prop") && !any(grepl("p.", param_names))) {
       #   stop_glue('`stat == "prop"` requires `"p"` {hyp_text}')
       # }
     }
@@ -243,13 +243,13 @@ parse_params <- function(dots, x) {
   # 0 index of dots
   if (length(p_ind)) {
     if (length(dots[[p_ind]]) == 1) {
-      if (attr(x, "null") == "point" && is.null(attr(x, "success"))) {
+      if ((attr(x, "null") == "point") && is.null(attr(x, "success"))) {
         stop_glue(
           "A point null regarding a proportion requires that `success` ",
           "be indicated in `specify()`."
         )
       }
-      if (dots$p < 0 || dots$p > 1) {
+      if ((dots$p < 0) || (dots$p > 1)) {
         stop_glue(
           "The value suggested for `p` is not between 0 and 1, inclusive."
         )
@@ -304,7 +304,7 @@ hypothesize_checks <- function(x, null) {
     )
   }
 
-  if (null == "independence" && !has_explanatory(x)) {
+  if ((null == "independence") && !has_explanatory(x)) {
     stop_glue(
       'Please `specify()` an explanatory and a response variable when ',
       'testing\n',
diff --git a/R/visualize.R b/R/visualize.R
index 100cd5a3..bda7887c 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -109,7 +109,7 @@ visualize <- function(data, bins = 15, method = "simulation",
   obs_stat <- check_obs_stat(obs_stat)
   if (
     !is.null(direction) &&
-    (is.null(obs_stat) + is.null(endpoints)) != 1
+    (is.null(obs_stat) + is.null(endpoints) != 1)
   ) {
     stop_glue(
       "Shading requires either `endpoints` values for a confidence interval ",
@@ -151,7 +151,7 @@ visualize <- function(data, bins = 15, method = "simulation",
     }
 
     if (
-      ("replicate" %in% names(data)) && length(unique(data$replicate)) < 100
+      ("replicate" %in% names(data)) && (length(unique(data$replicate)) < 100)
     ) {
       warning_glue(
         "With only {length(unique(data$stat))} replicates, it may be ",
@@ -440,8 +440,8 @@ shade_density_check <- function(data,
       }
 
       if (
-        direction %in% c("two_sided", "both") &&
-        obs_stat >= stats::median(data$stat)
+        (direction %in% c("two_sided", "both")) &&
+        (obs_stat >= stats::median(data$stat))
       ) {
         gg_plot <- gg_plot +
           geom_rect(
@@ -464,8 +464,8 @@ shade_density_check <- function(data,
       }
 
       if (
-        direction %in% c("two_sided", "both") &&
-        obs_stat < stats::median(data$stat)
+        (direction %in% c("two_sided", "both")) &&
+        (obs_stat < stats::median(data$stat))
       ) {
         gg_plot <- gg_plot +
           geom_rect(
@@ -632,7 +632,7 @@ visualize_theoretical <- function(data,
 
       # Assuming two-tailed shading will only happen with theoretical
       # distributions centered at 0
-      if (direction %in% c("two_sided", "both") && obs_stat >= 0) {
+      if ((direction %in% c("two_sided", "both")) && (obs_stat >= 0)) {
         infer_plot <- infer_plot +
           geom_rect(
             data = data.frame(obs_stat),
@@ -648,7 +648,7 @@ visualize_theoretical <- function(data,
           )
       }
 
-      if (direction %in% c("two_sided", "both") && obs_stat < 0) {
+      if ((direction %in% c("two_sided", "both")) && (obs_stat < 0)) {
         infer_plot <- infer_plot +
           geom_rect(
             data = data.frame(obs_stat),
diff --git a/R/wrappers.R b/R/wrappers.R
index b68b623e..a6097acc 100755
--- a/R/wrappers.R
+++ b/R/wrappers.R
@@ -191,7 +191,7 @@ chisq_stat <- function(data, formula, ...) {
 
 check_conf_level <- function(conf_level) {
   if (
-    class(conf_level) != "numeric" | conf_level < 0 | conf_level > 1
+    (class(conf_level) != "numeric") | (conf_level < 0) | (conf_level > 1)
   ) {
     stop_glue("The `conf_level` argument must be a number between 0 and 1.")
   }

From c0a892b36243471d74b314854f9fb415934f5eb0 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Sun, 5 Aug 2018 15:30:35 -0700
Subject: [PATCH 08/78] Fix long line

---
 tests/testthat/test-p_value.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index a24951a5..a6458bbc 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -52,7 +52,9 @@ test_that("p_value makes sense", {
   )
   expect_equal(
     iris_calc %>%
-      get_pvalue(obs_stat = median(iris_calc$stat) + 1, direction = "two_sided") %>%
+      get_pvalue(obs_stat = median(iris_calc$stat) + 1, 
+        direction = "two_sided"
+        ) %>%
       dplyr::pull(),
     expected = 0
   )

From 43a1119bc942be344638a203b535861cb9060179 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Sun, 5 Aug 2018 16:26:46 -0700
Subject: [PATCH 09/78] Get spacing under 80 chars per line

---
 tests/testthat/test-p_value.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index a6458bbc..e239ec50 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -53,8 +53,7 @@ test_that("p_value makes sense", {
   expect_equal(
     iris_calc %>%
       get_pvalue(obs_stat = median(iris_calc$stat) + 1, 
-        direction = "two_sided"
-        ) %>%
+        direction = "two_sided") %>%
       dplyr::pull(),
     expected = 0
   )

From a84a101c85f97cdfc9cba011dcd6c9283d1e9ba0 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 6 Aug 2018 08:12:15 -0700
Subject: [PATCH 10/78] Fix indentation to match common style guide

---
 tests/testthat/test-p_value.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index e239ec50..f572764f 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -52,8 +52,9 @@ test_that("p_value makes sense", {
   )
   expect_equal(
     iris_calc %>%
-      get_pvalue(obs_stat = median(iris_calc$stat) + 1, 
-        direction = "two_sided") %>%
+      get_pvalue(
+        obs_stat = median(iris_calc$stat) + 1, direction = "two_sided"
+      ) %>%
       dplyr::pull(),
     expected = 0
   )

From 5e1ff74b4fa4798ddb20099c74d39f7fed82ded4 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 6 Aug 2018 08:12:42 -0700
Subject: [PATCH 11/78] Update vignettes and documentation

---
 inst/doc/chisq_test.R      | 2 +-
 inst/doc/chisq_test.Rmd    | 2 +-
 inst/doc/chisq_test.html   | 8 ++++----
 inst/doc/two_sample_t.html | 9 ++++-----
 man/get_pvalue.Rd          | 4 ++--
 man/visualize.Rd           | 4 ++--
 6 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
index eaa45c86..69d754ed 100644
--- a/inst/doc/chisq_test.R
+++ b/inst/doc/chisq_test.R
@@ -58,7 +58,7 @@ fli_small %>%
 
 ## ----eval=FALSE----------------------------------------------------------
 #  fli_small %>%
-#    specify(origin ~ season) %>%  %>% # alt: response = origin, explanatory = season
+#    specify(origin ~ season) %>% # alt: response = origin, explanatory = season
 #    hypothesize(null = "independence") %>%
 #    generate(reps = 1000, type = "permute") %>%
 #    calculate(stat = "Chisq") %>%
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
index 54c3d53b..f2dd959a 100644
--- a/inst/doc/chisq_test.Rmd
+++ b/inst/doc/chisq_test.Rmd
@@ -118,7 +118,7 @@ fli_small %>%
 
 ```{r eval=FALSE}
 fli_small %>%
-  specify(origin ~ season) %>%  %>% # alt: response = origin, explanatory = season
+  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq") %>% 
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
index 56f2b356..285dae6a 100644
--- a/inst/doc/chisq_test.html
+++ b/inst/doc/chisq_test.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-07-06" />
+<meta name="date" content="2018-08-06" />
 
 <title>Chi-squared test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Chi-squared test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-07-06</em></h4>
+<h4 class="date"><em>2018-08-06</em></h4>
 
 
 
@@ -210,13 +210,13 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>, <span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. `infer` currently does not check these for you.</code></pre>
+## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-chi2-distribution-on-top-of-permuted-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution on top of permuted statistics</h2>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st">  </span><span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
+<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
index 6e7cd5f0..605cdffa 100644
--- a/inst/doc/two_sample_t.html
+++ b/inst/doc/two_sample_t.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-07-06" />
+<meta name="date" content="2018-08-06" />
 
 <title>Two sample t test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Two sample <span class="math inline">\(t\)</span> test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-07-06</em></h4>
+<h4 class="date"><em>2018-08-06</em></h4>
 
 
 
@@ -141,13 +141,12 @@ <h2>Calculate observed statistic</h2>
 <p>Or using another shortcut function in <code>infer</code>:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">t_stat</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
-<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
 The observed <span class="math inline">\(t\)</span> statistic is
 <div class="kable-table">
 <table>
 <thead>
 <tr class="header">
-<th align="right">stat</th>
+<th align="right">statistic</th>
 </tr>
 </thead>
 <tbody>
@@ -201,7 +200,7 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>, <span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. `infer` currently does not check these for you.</code></pre>
+## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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6ZBv2zLjz76aKcEaNb7t7/9bdYGckr6pXCUMqOSDXp8kSIWvxqRiG/8dZV9LQ6eU6v169fPrcokqk995A/8tYrywgsvmEyaPGfncqMpxfd9ovp1TNF8YhOw+W33lRJ/taGyjFL97JYmL8chAAEIQAACEAgngfLtF8LZrgpLLRMhhUz0Iv4Uq0NmQcovINtvFTnEamAtO/DYosGZzETKM/tQXXLGlRNubJGpjGbH582b5w5rtlU2/PEhIf1rdK78GmLNffSewnx6EYVMKxkKT6rQjbLj1my1HI9VNOhV0WpFssUfhMqROtWia9U+/ZfjcnxRaEx/dtp/zzdT8l+n+7/6Sw7SMmfxoiwlrF4ZjeVD4Tt2H3LIIc6XQ4N/2dPrWpXy+iph5TEHJ0+eHPPK7Omnn3arEv7KgPioxOa+UD940aSKXVeeHJX97Ba7GS8gAAEIQAACEAgdAVYA4rpMTpRyDFYEoK9//etucKiBl6LsyG5fzrUqMv3xQm9GHYUVUWfz5s3O+df/H1d1sZdyjH3ooYfMC/3oVgIU7UUDdTmMyhm4b9++7nwN7jX4l9mMbMI1ex9bpIRIXjlq6r9s7OX4q1l2zWirDYrBr1noRx55xL72ta+51QmZtEgh0GBRikiyRTPhUiD+9a9/OSdqye0rFMnUIQdlmQKp3fFl6NChzu9CqwNnnnmm4639WJ+N+GtSea3VD91bRQqdchrI2Vr/ZR6lVZJExcsk7Ry8xViKlJQ3KSYaoHvhQ53CqOvi+ypRXWUd04qCzG3Uz8oFoOhJSlAmfw4VOaaLtRei1imfdevWdY7o8UpTvBzxn5nKfnbLagPvQQACEIAABCAQAgKeHW9elfLyACh2e3zxZvwjXsjM6GFvwB3xwnVGVJfXhS7uuzcoj3iz19FztKO49l5m2Yiu98/zBocRL3lY9Dw/Frxi6McXzx4/ovjz3uDcXe/5F0Q806OIN6CLnqo48N4sv3tfMftV4mO6S15PQYjKIVm8FYiI50gcrccb/Ltjek+bZxoU8VYJIp5Dq6tfJ5YWZz5ayX93brjhhog3EHX1eA6q8W+7134egERveisOEW8lwl0fmwdAMfW9KDjFeHhmTJHDDjssLXkA/Lbrv2Lte6YwES8Ua8RbxSkhZjxjbxXAMfPrUPvVH17irui18X1VWt+Xlgfg8ccfj3h2/46L5LvgggtcronoDbwdxeb3HG+j53iRgyLqW8mlPAAq8XLoWHx7kvns+nkAPDM1VREt3uqNu5/nmxA9xg4EIAABCEAAAuEhUEWieoMHSikEFPddqwJlRTvRDL3s+eX865tplFJdwsOaVZbfgDfQdTP1iU5SyFDVrSyypRVfDs3Sl+Z4qtlizaj7Dqal1VXecUU4ylQUGK2gKE+CzG6CVuQvINnUV6WtfCTTV6W1S30oh2T5iJS28qGvrM5RH2sVoLSSjBz+Z6ain93S7s1xCEAAAhCAAASCSwAFILh9g2QQgAAEIAABCEAAAhBIOwGcgNOOlAohAAEIQAACEIAABCAQXAIoAMHtGySDAAQgAAEIQAACEIBA2gmgAKQdKRVCAAIQgAAEIAABCEAguARQAILbN0gGAQhAAAIQgAAEIACBtBNAAUg7UiqEAAQgAAEIQAACEIBAcAmgAAS3b5AMAhCAAAQgAAEIQAACaSeAApB2pFQIAQhAAAIQgAAEIACB4BJAAQhu3yAZBCAAAQhAAAIQgAAE0k6getprpEIIQAACEIAABNJKIOJlCd+3cFGJOqs2bmTVvMzhFAhAAAKpEAh9JuDts99Npb05PbdGjRq2Z8+enMrAzUsnUKdOHdu9e7ft37+/9JN4J2cEqlevbge8QZC2VMuWDi1SvYTzUyRQr14903dozZo1KV7J6UkR2LLFrEuPEqcWnTTSat90c4njiQ7wDEpEJTjHateubXv37rV9+/YFRygkiRKoWrWqaQtL/0Qa1LV63TtF5Y/fwQQongivIQABCEAAAhCAAAQgkMcEUADyuHNpGgQgAAEIQAACEIAABOIJoADEE+E1BCAAAQhAAAIQgAAE8phA1hQA2VbPnDnTZs2a5WzcymM6bdo027p1a3mn8T4EIAABCEAAAhCAAAQgkAKBrCgAO3futDFjxpgG9RMnTrRrrrnGIpFIqWJOnz7dbrrpJtuwYUOp5/AGBCAAAQhAAAIQgAAEIJA6gawoAJMmTbJ+/frZjTfeaOPHj7cdO3bY3LlzE0q7bt06+9Of/mSNGjVK+D4HIQABCEAAAhCAAAQgAIGKE8iKArB06VLr3bt3VErtL1pUMp6xVgXuuusuu+yyy6xWrVrR82N3FAJQYRq1VSQcYGxd7EMAAhCAAAQgAAEIQKDQCGQlEdjq1autQYMGUbbaX7lyZfS1v/P0009b27Zt7eijj/YPlfiv97Zv3+6ODx482B6+bFyJc4J8QLHMKcElULNmzeAKh2QVJlC3VasKX8uFxQlo4mWLF5N+8+bN0f/+vvKc6Pe9YcOGJTbFoKdUnMABL8fCVwkur1a9msu/kOCthId4BiXEEpiD+p7wXQlMdyQUJCz9s6ec8WZWRqPVqlUrllxJSRSU8CK2LF++3CZPnmwPPvhg7OES+z/96U+jTsStW7cOVWKteA4lGseBnBLQl1pJWMryT8mpgAV+cyVgUd9UpH92eYNVSuoEPvvsM/vwww/dplVbbUuWLKnQ7+7BBx9sXbt2te7du7v/3bp1s86dOxtKd3L9EtnsJQJLUKSQJZtgUoP/sCQxStDUvD9UVFSEdUOAe7lKlSqmLSzWJ/uLqpVJMysKQNOmTYs59Mq5VzP9sWXq1Km2YsUK+/rXv+4Oy3H4/PPPt5/97GfWt2/f6Knf/va3o/vaCVMm4DBlkCsGuUBeSAHQF5tMwMHs8MpkApbfEaV8Ahrw67f41VdfdX5a27ZtcxfJJPPwww93A/fTTz/dmjdv7mb669evb9o069+iRQtr3LixLVu2zK0MaJVAm6K5aYXgk08+scWLF9sTTzwRzRas30QpAcOGDbPjjjvO+vTpYxoEURIQ2Jn4Mxw5EEl6UM8zKAHXAB3SZ1/PIJS0AHVKjCj6/oTpOxSJHIiRvuRuVhSAQYMGudl9/dcDRaFAZeuvIqdfFQ32tfnlzDPPtF/+8pfWvn17/xD/IQABCEAgjQQUnnnOnDluwK9BvwbpesDJT+vCCy80zdJ36dLFOnTo4I6Xdet69eo5UxQpAtrKKpoEkjKg7a233rK//vWvLkCE6pBpp5QBbS1btiyrGt6DAAQgAIEKEsiKAjB8+HCXA2D06NHuITJq1Cjr2LGjE/nRRx91Mz5jx46tYBO4DAIQgAAEUiGwcOFCF5L573//u5uhb9KkiZuFv/rqq23o0KEZj8J20EEH2YABA9x23nnnuVW3t99+O6qI/OQnP3HNOeaYY+x73/uejRw5ElOhVDqYcyEAAQiUQ6CKZ09bekD+ci5O9W0tBcv2P51OSGEyAZKJSbK2mqmy5fzKE6jjOdlpRhQToMqzzEQNlTEB2tKh7BnpTMgbtDoVPOEf//iHG/i/9957pkG4VlpPO+0069Gjh7NtrYzM/grAmjVrKlONu1Z1vPzyy25l4J133nGmRZL17LPPtk6dOlW6/lBW4JlTWZceJUQvOmmk1b7p5hLHEx3gGZSISnCOaXwkPzRMgILTJ7GShM4EqEFdq9e99N/LrKwA+ABlK0qBAAQgAIHsEfj444/tkUcesWeffdZFUBs4cKALtnDyyScHNtqIfAw02NcmJ2QlkPzb3/5mDz/8sMspc+6559o3vvGNcs2SskeZO0EAAhAIF4Gs5AEIFxKkhQAEIBB+Asq/cumllzqTHkVYUzZ2+V89+eST9s1vfjOwg/948oocpGAQWgm477773ArdJZdc4tolpSYsETni28VrCEAAArkkgAKQS/rcGwIQgECaCciRV8kUhwwZYjNmzLCbb77Z5s2bZzfccINz5k3z7bJWncwjzjrrLHv++efdaoYcjaXgKILQc889hyKQtZ7gRhCAQD4QQAHIh16kDRCAQMETUPjNyy+/3A38p0+fbjfddJOL8KNoPvF5V8IOq1+/fvbUU0+ZnJibNWtmP/rRj1zUoH/+858VyhMRdh7IDwEIQCBVAigAqRLjfAhAAAIBIqAcBzKRUfSe1157zW688UY38L/ooovybuAfj71///6mDPLPPPOMc2pWm+UbsGDBgvhTeQ0BCEAAAjEEUABiYLALAQhAIEwEXnzxRRc3X86xmgWfO3euXXzxxS4ef5jaUVlZFS5UqwGPP/64Szp50kknOUVIicgoEIAABCBQkgAKQEkmHIEABCAQaAKy81delQsuuMAOO+wwFz//+uuvL7iBf3wnyR9g2rRpNm7cOBdCVBGPtEJAgQAEIACB4gRQAIrz4BUEIACBwBLYuXOny6KuLLlLlixxYTEVHvPQQw8NrMzZFkyx7q+88kqTH8RRRx1lV1xxhZ1++uku63C2ZeF+EIAABIJKAAUgqD2DXBCAAARiCLz//vt24okn2vjx493M/xtvvGGnnnpqzBnsxhJo06aNKdP8hAkTbNWqVY7d7373O6IFxUJiHwIQKFgCKAAF2/U0HAIQCAMBZaZW/HsN9qtVq2aK6S9HX2WuppRP4Pjjj3dmQd///veds/QZZ5xhn3/+efkXcgYEIACBPCaAApDHnUvTIACBcBP49NNP7bTTTrO7777bzjvvPJsyZYp179493I3KgfQKg3rHHXfYE088YStWrDApBUqIRoEABCBQqARQAAq152k3BCAQaAITJ050A9XVq1e7mPe33HKL1axZM9AyB104JUd79dVXXfKwsWPH2g9/+ENbv3590MVGPghAAAJpJ4ACkHakVAgBCECg4gQ2b95sY8aMsWuuucZOPvlkmzp1qh177LEVr5ArixFo1KiRPfTQQ/bb3/7WZUqWQ7UyJlMgAAEIFBIBFIBC6m3aCgEIBJrAwoULTTHsZ82aZb///e/tgQcesAYNGgRa5rAKJ18ArQYccsghNmrUKJODMAUCEIBAoRBAASiUnqadEIBAoAkoXv3Xv/51Z+YjR19ltKVklsDBBx/szKtkCqRsyvq/bdu2zN6U2iEAAQgEgAAKQAA6AREgAIHCJbB371776U9/6uLVjxgxwv71r38R1z+LH4fq1avbrbfeag8++KC99tprzuxKORYoEIAABPKZAApAPvcubYMABAJNQPHpv/Wtb7lY9XLyldkP4T1z02Xf/OY3nfIViURs5MiR9vzzz+dGEO4KAQhAIAsEUACyAJlbQAACEIgnMHfuXNOM/2effebMUC688ML4U3idZQKdO3d2eRYGDx5sF198sd12220kDstyH3A7CEAgOwSqZ+c2mbtL1arh0WGqVKliYZI3c70W3Jrpo/zsm6KiokA17JlnnnEmP0ceeaQ99thj1rJly0DJVxFhlKRM35+gsU61LQcddJD95S9/sfvvv9/5BShvQBBWZiLeZ3hfosZUsaSfK/y+JQIYrGMaIzBOCFaf+NKoX8L0HTrgC17KfxSAUsBk4nCYPjiZaH8Y6uSHN7i9pO+PSkX6qEaNGoFp2C9+8Qu788477fTTT3cDy1q1agVGtsoIIgVAJUisK9MehWHVioBWZpSMbdKkSdaiRYvKVFmpayNFNRIqAKk8V1I5t1LCcnGFCdBHFUaX8QvVN6Hqn3ImyEOvAOzbl3BOJOMfhIrcQAOXMMlbkTaG+RoNXPbv3++2MLcjX2WXs+aBAwcqZJKxffv2nGPZs2ePjRs3zpn7XH755Xbddde5z1oQZEsHHD0Y1Uf50h4xGT58uCk607nnnuuSh02YMMG6dOmSDlyp17Ej8Wc4ciCS9HOFZ1Dq2LN5hVbP9AxinJBN6snfS9+fMH2HIt7zsqwSHvuZslrBexCAAAQCTGDTpk02evRoe/bZZ+2Xv/ylXX/99W4mKcAiI9p/CfTu3ds5B8s5W6FZFSmIAgEIQCDsBFAAwt6DyA8BCASagGzIFd9/wYIF9vjjj9t3v/vdQMuLcCUJtG3b1v75z39az5497fvf/75NnDix5EkcgQAEIBAiAigAIeosRIUABMJF4IMPPrBTTz3Vdu/e7QaQgwYNClcDkDZKQBmZpcCdddZZJv+Au+++O/oeOxCAAATCRiD0PgBhA468EIBAYRCYM2eOnXPOOdauXTt74oknrFmzZoXR8DxupWy0f/WrX1mrVq3s3nvvNZl2KYOw/B8oEIAABMJEAAUgTL2FrBCAQCgIvPzyy3bRRReZwnwqpGTDhg1DITdCJkfgJz/5iTVq1Mhuuukm27x5s913332hD3+aXMs5CwIQyBcCKAD50pO0AwIQCAQBxfgfO3asKZnUH/7wB6tdu3Yg5EKI9BI4//zzrXHjxq6vpQQ88sgj9HV6EVMbBCCQQQL4AGQQLlVDAAKFReCPf/yjKcSnnH4f8xJ8MfjP7/4/44wzTH0+c+ZMF+Vpy5Yt+d1gWgcBCOQNARSAvOlKGgIBCOSSgGzD/+///s/FjH/ggQcwCcllZ2Tx3iNGjHA+HosWLbJvfetbtnbt2izenVtBAAIQqBgBFICKceMqCEAAAlECt956q3MOvfLKK+3nP/95hbIVRytjJ3QE+vfvbzL9+uqrr1yG51WrVoWuDQgMAQgUFgEUgMLqb1oLAQikmYAcQR966CG74YYb7Nprr01z7VQXFgJy+P773/9u27ZtM5kGffnll2ERHTkhAIECJIACUICdTpMhAIH0ENCgX46+N998s1166aXpqZRaQkugU6dOTgnYtWuXMwdauXJlaNuC4BCAQH4TQAHI7/6ldRCAQAYIRCIRN9v/6KOPmsx/FPKTAgEROOSQQ5wSsG/fPqcEfP7554CBAAQgEDgCKACB6xIEggAEgkxAg39lgp0wYYJLAnXBBRcEWVxkywGBDh06OCVAnxU5Bq9YsSIHUnBLCEAAAqUTQAEonQ3vQAACEChG4MCBA3b11Vfb448/bnfddZf94Ac/KPY+LyDgE1AGaPkEKEuwlIDly5f7b/EfAhCAQM4JoADkvAsQAAIQCAMBzeb++Mc/tr/97W92zz332DnnnBMGsZExhwTatm3rlICioiLnGIwSkMPO4NYQgEAxAigAxXDwAgIQgEBJAr7N/1NPPeXCfZ599tklT+IIBBIQaNOmjVMCatasaWeeeabhE5AAEocgAIGsE0AByDpybggBCISNgEJ9Tpw40Zn9jB49OmziI2+OCbRu3dqkPMocSEoAIUJz3CHcHgIQMBQAPgQQgAAEyiBwxx132B//+Ee75ZZbMPspgxNvlU1AKwFSAnbv3m1nnXWWrVmzpuwLeBcCEIBABglkTQHQj97MmTNt1qxZtnfv3lKbpB/FKVOm4DBVKiHegAAEskVAtv7jx4+36667zi688MJs3Zb75CkBRQd68sknbfPmzU4JWL9+fZ62lGZBAAJBJ5AVBWDnzp02ZswYmzZtmltGVwg92dTGlxdeeMHGjRtnSp5y44032osvvhh/Cq8hAAEIZIXAb3/7W/v1r3/tHH+vuOKKrNyTm+Q/ASULkxKwdu1aGzVqlG3atCn/G00LIQCBwBHIigIwadIk69evnxvUazZtx44dNnfu3GIwpBC89NJLLqnO+eefb2PHjjVdR4EABCCQbQIPP/yw/fznP7dLLrnETUpk+/7cL78JdO3a1Z544gnnEPzd737Xtm7dmt8NpnUQgEDgCFTPhkRLly61ESNGRG/Vu3dvW7RokfXv3z96TM5Rv/nNb9xrmQjJXEjLpfFl2bJlpljcKnXr1rWG3nVhKmonJXgE/H7Rf38/eFIWtkR+31Skf6pXT/6n7i9/+Yuz99dExC2e3T8lOQJVq1Z1351UWCdXc36epeegQsrKKVghZbUqULt27VIbG6le3fYnetd7pCT7nfC/Q4mq4VgwCNBHweiHRFL43zP/f6JzwnQs+adiJVq1evVqa9CgQbQG7cvMJ1HZsmWLKcSezIYeeeSREqd8+9vftu3bt7vjgwcPtocvG1finCAf4OEY5N4xq1GjRrAFRLoKEajTrFlS12kQJhPFH/7wh6ZVgHz5oU+q8Wk6qVmSrNN0u1BXc/LJJztT15NOOskuvvhie+6550w5AxKVA95v01cJ3qhWrVqZikP8JWUpGfHn8jr7BNT/pX0Gsi8NdwwzgT3epEFZpex3y7oyhff0A7V////mLvbt21fqD5aUg+eff96mT59uF110kftBjP3Beuihh0zXqzRu3Nh2bd6VgiS5PVVf6rIcoHMrHXevVauW7dmzJ7rCBJFgEdDviEwF/RXAVKTbkYSz5dSpU93kw8iRI+3OO++0DRs2pHKLgj9Xv9P6Dm3cuLHgWaQC4IgjjnBRprQK8J3vfMf0jNNqSnyJeJNjiYqerbt2Jfcc5BmUiGBwjilXhPrTH+MERzIkEQF/lTN2PBtkMpGikr8jsfJmRQFo2rRpsYepHqzKkBhbNPB6++23nVmQZt2GDBliv/vd72zhwoV29NFHR0/t06dPdF8722e/W+x1kF9UdPAS5Dblm2z0UXB7VD++GvxXRAHQ70tZZd68eTbGC1QwYMAAe+CBB9xDOCw/8mW1K5vvafVM35/yWGdTprDca9iwYXbffffZ5Zdf7kxb77777pKil/YZ9uJpJPud4PetJNagHanob1zQ2pGv8vjPoTC0T9/3skrZ6kFZV6bw3qBBg2zy5MlulmLdunUuFGivXr1cDXqtTTMTv//9700PYpWPPvrIFCKtffv27jV/IAABCGSCgCYZNPvarVs3+9Of/oQZWCYgU2e5BM444wy38qSEc1qBokAAAhDIJIGsrAAMHz7cOfUqg6a0J4U+69ixo2vXo48+6gb/ivqjTUqAbG+1FHbrrbca9qSZ7H7qhkBhE1BQAf0uKVPrhAkTrE6dOoUNhNbnlIBWoZQj4Be/+IU1atTIRaHKqUDcHAIQyFsCWVEA5Ph6++23u1BnshONdYRV3H+/9OzZ0ykA27Zts3r16vmH+Q8BCEAg7QS+/PJLZ3OtaGKKxqIBFwUCuSZw5ZVXOj8KZaCWT9z3vve9XIvE/SEAgTwkkBUFwOdWv359f7fM/wz+y8TDmxCAQCUJyFFVK5FytnvmmWesefPmlayRyyGQPgI333yzKSLetdde64JdnHLKKemrnJogAAEIeASyqgBAHAIQgECuCSgR4fe//32XifUf//iHtWvXLtcicX8IFCOgQBj33HOPyxJ86aWXOiVggBctiAIBCEAgXQSy4gScLmGpBwIQgEBlCGjG/8ILL3SJCP/85z9b586dK1Md10IgYwQU9nb8+PGmgBnyDVj04YcZuxcVQwAChUcABaDw+pwWQ6AgCSgk2lVXXWWvvfaa8zXq27dvQXKg0eEhoLwKUlTbtGljF5x/QXgER1IIQCDwBFAAAt9FCAgBCKSDgJwqn376aWdaMWLEiHRUSR0QyDgBOQI/8cQTVqMmWcozDpsbQKCACKAAFFBn01QIFCoBZVd98MEH7frrr3dhPwuVA+0OJ4EWLVrYn/74x3AKj9QQgEAgCaAABLJbEAoCEEgXAcX6V06R888/32VaTVe91AOBbBLwc+dk857cCwIQyF8CKAD527e0DAIFT0Cx/mfPnm2nnXaaUwIKHggAIAABCEAAAh4BFAA+BhCAQF4SWL9+vU2fPt1kPnH//febQitSIAABCEAAAhBAAeAzAAEI5CGBrVu32rRp00zJB4cMGWJFRUV52EqaBAGzTZs3gQECEIBAygRYAUgZGRdAAAJBJrBr1y579dVXTXHUjzvuOAb/Qe4sZKs0gQ0bNtjy5csrXQ8VQAAChUUABaCw+pvWQiCvCSjRl2b+d+/e7Qb/tWvXzuv20jgI1K1b1/m5rF69GhgQgAAEkiaAApA0Kk6EAASCTODAgQPO5n/Tpk02bNgwa9iwYZDFRTYIpIVA82bNrGnTpi7BnVYDKBCAAASSIYACkAwlzoEABAJPYO7cuaaoPwMHDrRm3qCIAoFCIFClSlUbOnSoaSVAq1/btm0rhGbTRghAoJIEUAAqCZDLIQCB3BN477337JNPPrF+/fpZ27Ztcy8QEkAgiwRq1KjhTN4U6Ur+LzKBo0AAAhAoiwAKQFl0eA8CEAg8gY8//tg++OADO+KII6xTp06BlxcBIZAJAloBkNP7zp07nTmQ/GEoEIAABEojgAJQGhmOQwACgSfwxRdf2Lx58+yQQw6xnj17Bl5eBIRAJgk0atTImQMpB8bMmTMtEolk8nbUDQEIhJgACkCIOw/RIVDIBPxEXy1btrT+/fsXMgraDoEoASW+GzBggH3++ec2f/786HF2IAABCMQSqB77Ioz7ivUdliL7zDDJGxau6ZDTzxJbtSo6cTp4ZqIO9Y36SZuf6EuRfhTxp7xEX4QDzUSPFK9TfaC+gXVxLul6Fdmz13YlqKxK1ZLPlUMPPdSUD+Ott96yevXqWffu3d2V+g7xDEoAMUCH6KMAdUacKP4zKCzfoQNegICySugVgLIax3sQSJYAS+XJksrteeonDWxeeeUVN5AZPnx4uYN/SUz/Zr7ffMb+/8zfsbDuELHUzHk06N++fbtTAurUqWMdO3YsLGC0FgJpJqDfNn+yMM1VZ6i6sn8zQq8A7N+/P0Pg0l+ttMYwyZt+AsGvUbHk6aNg9pN+ePfs2WNTp051SsCJJ55oin6STH9JaaBklkD16tVdf8A6Q5xL+QxHDkRK/Q707t3bKQEzZsywmjVrughZyXxfMtQCqk2CAM+gJCDl6BStAKiE5TtU3mRM2esDOYLMbSEAAQjEE9CP2RtvvGFKdjR06FASfcUD4jUE4ghIaZY/QJMmTVxkICXJo0AAAhAQARQAPgcQgEAoCCjRlxwbleirefPmoZAZISGQawJamZHCLN+Mf//737Zjx45ci8T9IQCBABBAAQhAJyACBCBQNoEFCxbY4sWLrU+fPtauXbuyT+ZdCECgGAGZ/yhHgMxLlChMpnQUCECgsAmgABR2/9N6CASewLJly+zdd991kUy6du0aeHkREAJBJKBoQCNGjLBt27bZ9OnTnTIQRDmRCQIQyA4BFIDscOYuEIBABQisXr3a5syZY+3bt3ez/xWogksgAIH/EpAvwODBg+2rr76yWbNmwQUCEChgAigABdz5NB0CQSawceNGe/31161p06bOkTHIsiIbBMJCoHXr1i5x3qeffmrvvPNOWMRGTghAIM0EQh8GNM08qA4CEAgAAcUvl62y4pfLgTEsiVcCgA4RIFAuASUK03fs/ffft7p169rhhx9e7jWcAAEI5BcBFID86k9aA4HQE5CDogb/KnJcVKx/CgQgkF4CPXr0cBGB5s2b5yIEtW3bNr03oDYIQCDQBDABCnT3IBwECouAEqy89tprbnZSg3/NTlIgAIHMEOjbt6/JJEiJwtauXZuZm1ArBCAQSAIoAIHsFoSCQOERUKIvOSauW7fOhgwZYo0bNy48CLQYAlkkoMymcgpu2LChU7y3bNmSxbtzKwhAIJcEUABySZ97QwACUQLz58+3FStW2DHHHGOtWrWKHmcHAhDIHAElCtNqW1FRkU2dOtV27tyZuZtRMwQgEBgCKACB6QoEgUDhEli0aJFL9NWrVy/r2LFj4YKg5RDIAYFatWrZ8ccfb/v27bNp06bZ3r17cyAFt4QABLJJAAUgm7S5FwQgUIKAwhG+/fbb1rlzZ5fsq8QJHIAABDJOoH79+jZs2DDbvHkzicIyTpsbQCD3BFAAct8HSACBgiWgRF+y+2/Xrp0dffTRBcuBhkMgCASUc0M+AX4CviDIhAwQgEBmCKAAZIYrtUIAAuUQiE30deyxx1qVKlXKuYK3IQCBTBM4+OCDrV+/frZs2TJ79913M3076ocABHJEgDwAOQLPbSFQyARI9FXIvU/bg07gsMMOczkClCisdu3azjwv6DIjHwQgkBoBFIDUeHE2BCBQSQK7d+920UY040+ir0rC5HIIZIiAnyjszTffdEqAzPQoEIBA/hDABCh/+pKWQCDwBPwoI7t27XKDfxJ9Bb7LELCACShRWJs2bVyisK+++qqASdB0COQfgawpAJr1mzlzpnP4KyvEmOyCX3nlFVu1alX+0aZFEChgAgcOHLA33njDNmzYYEOHDrVGjRoVMA2aDoHgE1CisIEDB5qcg5WhW89nCgQgkB8EsqIAKLHImDFjXHzhiRMn2jXXXGPK+hlfnnvuObviiits+fLlduutt9p9990XfwqvIQCBkBKYM2eOffHFFzZo0CBr3rx5SFuB2BAoLAJKFCaFvU6dOs50b9u2bYUFgNZCIE8JZEUBmDRpkosqcOONN9r48eOdc9HcuXOLId2/f79NmDDBbrvtNrvgggvs3nvvdSsBzDgUw8QLCISSwDvvvOOiiii6SNu2bUPZBoSGQKESqFGjhksUphUBZQuWCR8FAhAIN4GsOAEvXbrURowYESXVu3dvU+bP/v37R49Vq1bN/vznP5tvE6wfGM00SDGILVqG9E2ItCzZpWq12LcDvS+nR/2AUoJHwA9BSR+lv28WL15sCxcutJ49e1Yqmkhlvjs1a9ZMf8OosRgBzRTr+wPrYljS9iLifYb3JKrNi56b7HejMr9v9erVsxNOOMEmT57szIH0TFefU9JLQH2ZbH+m987UVh4B9UtlvkPl1Z/u9yPlhNbOyrdXSUUaNGgQbZv2V65cGX3t7/iDf9kK33///XbSSSc520P/ff2/6qqrTCEEVZSw5OHLxrn9sPyRokMJLgHNdFHSR+CTTz6xefPmWbdu3UwOhbkqtQ86KFe3Lrj7HgTrjPT5Ae/ZkcgNV8+UWrVqJX3PyjyDdJ+TTz7ZXnzxRZctWM9oBqtJo0/qRClVKFZJocrZSUVFRTm7dyo33lPOeDMrCoB+cGJn8hUJRLGFExU5C8sMSD4CN9xwQ4lTpkyZEvUf0GBtx+IVJc4J6gHJu2dPwjmcoIpcMHJJq9dnUp+/2M9qwQDIQEPlyD9t2jSX5Verfjt27KjUXfRQ1O9CRfpnGxFMKsU+mYs1gSM78bVr1yZzOuekSCCyZUvCK/bv25/0dysdz6D69es7P57XvNV4BeyQkzAlPQT0DNL4yLdySE+t1JIuAhrLaqygPgpDiVSva2WtfWdFAZCpjiJ/+EX7ieyANUC49tprTZkIx40bZ4lmKuKdB7dbeBQAv/38Dx6BRE7pwZMyPBKtW7fONEDQ91VZftNVKtpPWlWkZJaA+kYbrDPEOUCfYT2jjznmGBfVT0pFLlf3MkQ7Z9VW9DcuZwIX0I3VN1IA8qVkxSBdUT9kNyi7fg0MZs2aZb169XIM9Vqbys033+xshK+77rqEg393En8gAIFAE9i0aZO9+uqr1rBhQxsyZAjf5UD3FsJBoGIEDjnkEDvqqKNsyZIlpozBFAhAIFwEsrICMHz4cJcDYPTo0c5ecNSoUdaxY0dH6tFHHzXZU5144ommMIHannzyySjFBx54wJSRkAIBCASfgBz3FSVEtsLK8hsWW8ngk0VCCASPQNeuXZ3ZpBQArQR06dIleEIiEQQgkJBAVhQA2e7efvvttnXrVmdnHevgIlMfvyhJEAUCEAgnAa3wafCvJdLjjz8+JcfEcLYYqSEAAUX3ku/UW2+95SJA+ZN7kIEABIJNICsKgI9AzkMUCEAg/wjIuV2Df/1XeEA/olf+tZQWQQAC8QTkA6Dvvsx7tRIgHwEKBCAQbAJZ8QEINgKkgwAEKkNAERFe8xx+tcInsx/Z/lMgAIHCIaBVvwEDBljLli1deNA1a9YUTuNpKQRCSgAFIKQdh9gQCAIBRXyZPn26c+QfOnSoNWnSJAhiIQMEIJBlAoraJ6f/xo0bu/C/sZH/siwKt4MABJIggAKQBCROgQAEShJQSLQZM2aY4v0rKZ9m/ygQgEDhEpB/n1YBZQIok8DNmzcXLgxaDoGAE0ABCHgHIR4EgkhAg//Zs2fbZ5995pb+27RpE0QxkQkCEMgyAfkAKPJfzZo1XaIwRQajQAACwSOAAhC8PkEiCASegCJ+LFu2zPr16xcN6Rt4oREQAhDICgGFAVYksKpVq9rLL7+cdKbirAjHTSAAAUcABYAPAgQgkBKBd9991z766COXBKhTp04pXcvJEIBAYRCQGZBWAvbv3+9WAhQmmAIBCASHAApAcPoCSSAQeAILFiwwbUrOpyRAFAhAAAKlEVDobykByhPghwku7VyOQwAC2SWAApBd3twNAqEloFl/zf5r4E927tB2I4JDIKsEGjVq5ByD5Qvw6quv2t69e7N6f24GAQgkJoACkJgLRyEAgRgCH3/8sb355psmk5+jjjoq5h12IQABCJRNQOGBhw0bZhs3bnQhQpU7hAIBCOSWAApAbvlzdwgEnsDSpUtt7ty5duihh5oyflIgAAEIpEqgefPmTglYv349SkCq8DgfAhkggAKQAahUCYF8IaBIP3PmzLFDDjnE+vfvb8r4SYEABCBQEQLKFaKEgWvXrnXZw1kJqAhFroFAegigAKSHI7VAIO8ILF++3GbNmmUdOnSwY445hsF/3vUwDYJA9gm0atXKKQFr1qxxWcQVJYgCAQhknwAKQPaZc0cIBJ7AihUrbObMmdauXTuX6IuZ/8B3GQJCIDQEWrdu7bKHr1692ikBBw4cCI3sCAqBfCGAApAvPUk7IJAmAsruO2PGDGvbtq0NHDjQJfNJU9VUAwEIQMARUPbwQYMG2ZdffmlvvPGGoQTwwYBAdgmgAGSXN3eDQKAJaPCvh/HBBx/sHs7K5EmBAAQgkAkCmmSQErBy5UqUgEwApk4IlEGAp3sZcHgLAoVEQGY/DP4LqcdpKwRyT0BmhlpplBIwffp0lzk491IhAQTynwAKQP73MS2EQLkE5PCrwb+W5QcPHmzVqlUr9xpOgAAEIJAOAu3bt3crAV988YW9/vrrKAHpgEodECiHQPVy3g/820VFRYGX0RdQ5hRhkteXu5D+a+BbaGYvivMvh19F+xkyZEhg269+0RaJRFL+SNavXz/la7ggNQI1atRwkaJgnRq3ZM+OHIjYtgQnV61aJennSpCfQcozoufjtGnTnBJw/PHHW/XqoR+iJOixsg/pGUTQhbIZ5epd9Yu/5UqGVO67vxwT3tB/u8IUR1g/vmGSN5UPWtjP1ZdaDx85ohVSWDoN/mfPnu0G/1qGV/uD6oynwUBF5du5c2fYP6KBl1/fIfURrDPTVZFdiT/DBzyFONnnStCfQYoONHToUJcj4OWXX3aJwwpJCfB/45Ltz8x80qi1NAL6/gT9OxQre6Sc6FqhVwAqMhsYCyib+5I1TPJmk02u7+X3SyH10ccff+wy/CrJl+L8q/gcct0fie7v901FZOSBmohoeo9JOVPfwDq9XKO17dsX3S224y2IJfud8L9Dxa4P2AtfCZAp0NSpU+24444rqJWAMPRRwD4yWRPH75tkv29ZE6yUG5W3Vo4PQCngOAyBfCawePFiN/jXsjtJvvK5p2kbBMJHQErAsGHDbP369U4J2LNnT/gagcQQCDgBFICAdxDiQSDdBD744AN766237PDDD7f+/ftjb5puwNQHAQhUmkDLli3d7P/GjRtN5kC7du2qdJ1UAAEI/I8ACsD/WLAHgbwnMH/+fHvvvfese/fu1rdvXwb/ed/jNBAC4SXQokULO+GEE2zHjh320ksv2fbt28PbGCSHQMAIoAAErEMQBwKZICCbxTlz5tiHH35ovXr1clsm7kOdEIAABNJJoEmTJjZixAjbu3ev/fvf/7YtW7aks3rqgkDBEkABKNiup+GFQkDOmYrxr4g/mvXX7D8FAhCAQFgINGzY0E488UQXgUUrATILokAAApUjgAJQOX5cDYFAE1BEFsXV/vzzz122Tdn9UyAAAQiEjUC9evWcElCzZk1nDrR27dqwNQF5IRAoAigAgeoOhIFA+ggocobC6K1Zs8Yl+OrgJfqiQAACEAgrgdq1aztzoAYNGtgrr7xiX375ZVibgtwQyDkBFICcdwECQCD9BOQsJ3vZTZs2uUgabdq0Sf9NqBECEIBAlgloBWD48OHWrFkzt7q5bNmyLEvA7SCQHwRQAPKjH2kFBKIEZB87ZcoU0wqAnOcUSYMCAQhAIF8IKGu7EoS1b9/eZs2aZQsWLMiXptEOCGSNQOgzAWeNFDeCQAgIrF692pRBU0vlxx9/vNWtWzcEUiMiBCAAgdQIVK1a1Y499lirU6eOvfvuuy5UaJ8+fQhtnBpGzi5gAigABdz5ND2/CHz66aduNqxp06Y2dOhQq1GjRn41kNZAAAIQiCFQpUoV6927t1MClNxQ+QIGDhxo1asztInBxC4EEhLABCghFg5CIFwEFi1aZDNmzDDZ+mvmn8F/uPoPaSEAgYoT6NKliw0ePNg5Bcs5ePfu3RWvjCshUCAEUAAKpKNpZn4SUIz/N998095++23r3LmzDRo0yKpVq5afjaVVEIAABEoh0K5dOzf5oURhCoCwdevWUs7kMAQgIAIoAHwOIBBSAnLyfe211+yjjz5yy+DYv4a0IxEbAhBICwEFPFDgg/3799vkyZPtq6++Sku9VAKBfCSAApCPvUqb8p7Atm3b3CyXYvzL3r9bt25532YaCAEIQKA8Ao0aNbKTTz7ZlD1Y5kDKgE6BAARKEkABKMmEIxAINAEN+jW7tXfvXpcZkxj/ge4uhIMABLJMoFatWi5XgJIfzpkzx5lIRiKRLEvB7SAQbAK4yge7f5AOAsUIKOmNHmiNGzd2M/8K90mBAAQgAIHiBOQLpTChWglQmFD5BhAhqDgjXhU2AVYACrv/aX1ICGj2Sg8xJb1p27atnXDCCS7Wf0jER0wIQAACOSFwxBFHuAhBq1atcgkSZT5JgQAEsugErLBcM2fOdAMYmS6UVfQF1bkUCEDAXEbfadOmuWyXPXr0cJF+iHPNJwMCEIBAcgQUIUjOwRqHyHxSCRMpECh0AllZAdi5c6eNGTPGNIiZOHGiXXPNNVaaPd6uXbvslltuseeee67Q+4b2Q8A2btxo//rXv0x2/0OGDDEpABQIQAACEEiNQJMmTWzkyJFR52DlTqFAoJAJZEUBmDRpkvXr189uvPFGGz9+vMvWN3fu3BLcZd8sRYH4vSXQcKAACSiz75QpU0wp7xXVQqY/FAhAAAIQqBgB+UwNHz7c5UxR7pTp06fbvn37KlYZV0Eg5ASy4gSsMFxafvOLUndL++7fv79/yP1XGu8bbrjB1q9f72Y9i7353xePPfaYM4nQSy3rDWvSJtFpgTymgRymG4HsmqhQchxTevlcFiX3Ulp7fUfat2/vHNeKiopyKVIg7q3vj/pG/1MtdevWTfUSzk+RgLJPq39gnSK4JE+P7NtvOxKcW6VqlaSfKzyD/gPwmGOOsebNmzuTZE2yHHfccdagQYMEdLN/iESO2Wee7B31+6YtLOO4/VXKflZmRQGQvV3sl0v7K1euLMFczjoqSm5UWvnDH/5gvhOPPPxHXHBFaacG8jgDuUB2S1Qo/fjm8gdY5nJTp041OawpsVfPnj2jsrFTcQJF9epV/GKuTIqAHowq9WCdFK9UTzrgJbdKpABoUJ/KcyWVc1OVMUznd+nSxZo1a2YvvfSS/fOf/7Rhw4a5CZdct0H9WZFJjlzLXUj3z+UYISXO1QKgAAiWMvP5RUtuFQ1fOGPGDL8a93/77HeLvQ7yC82QKXsrJZgE6tSp4/on9rOaTUmlKOvzrRUAzUi1bt3apBBQ/kNAsy5ioy3VsoWMoKkiS/l8Dfz1HZK/CiUDBLwwlonKfm9lINnfCZ5BxQnq8yrzSv3uShHo2rWr9erVK2cDcI2LFCQFs6Ti/RSUV75yFpb+iRRVtZplwMvKCkDTpk1tw4YNUTG0jz1zFAc7BU5AA9r333/fRfnRjJRiVWNGUeAfCpoPAQhkhUDNmjXdhMuCBQvsvffecwrsoEGDWMnKCn1ukksCZa8PpEkyfZkUeksRftatW+fs7qRlq+i1NgoECpGA/F6Url4PH5nAKb4/g/9C/CTQZghAIFcEZL525JFHOl9Fraa8+OKLtmLFilyJw30hkBUCWVkBkNe94vqPHj3aLa2NGjXKOnbs6Br46KOPOvvFsWPHZqXB3AQCQSEgPxgl9tKy4vHHH2+tWrUKimjIAQEIQKDgCMgx+JRTTnG/y2+88YbLF3DUUUeFxumz4DqMBleKQBUvHn+kUjWkcLHCe8rGLZ0e1PgApNABnFomAdmDKlFMpn0AVP8777xjixcvtpYtW7p09RX1iSmzQXn2ZqV8ADq0yDMawWsOPgAZ7hOdGTrJAAAXpklEQVT5AHQpmQek6KSRVvumm5O6OT4ASWFyJ3344Yfud1pBS2SW2ahRo+QvruCZ+ABUEFyWLgudD0CDulave6dS6WRlBcC/e/369f1d/kOgIAnI/0WrYVu8h7ki/HTv3j3nYUcLsiNoNAQgAIEyCMghWCsCWglQMkb9XuuYH+2qjEt5CwKhIJBVBSAURBASAhkgIEffhQsXOmdfKcInnXSSKTMlBQIQgAAEgklAv9GnnnqqzZ8/35Q4TGabAwYMwEE4mN2FVCkSQAFIERinQyBVAprtl62/nN0Ve1ozSek0g0tVHs6HAAQgAIHkCOi3ul+/fi5y4ezZs+2FF16wo48+2g477LDkKuAsCASUAApAQDsGscJPQO41S5YscTNHCjUnZ3jZ/FMgAAEIQCBcBJSXRasB8+bNszlz5tjnn39u/fv3r3BOo3C1HmnzkQAKQD72Km3KOQHN+s+dO9e+8hJQHXLIIW7GSA54FAhAAAIQCCcBTeQorLnyGEkRUAZhrQboN54CgbARQAEIW48hb6AJyNZ/0aJFzta/Vq1aNnToUGvTpk2gZUY4CEAAAhBInkCHDh2cg/Cbb77pzDuXLVvmzIQIdJI8Q87MPQEUgNz3ARLkCQHZ+GtpeNOmTda5c2dn619UVJQnraMZEIAABCDgE1DY6CFDhthnn31mUgTkG9CjRw8XKUjhIikQCDoBFICg9xDyBZ7A3r177d1337WPPvrIGjZs6CL8NG3aNPByIyAEIAABCFSOQLt27Zxvl3K7aPv000+dbwBR3irHlaszTwAFIPOMuUMeE9CPvcLD7dq1y772ta+5uP7M/uRxh9M0CEAAAnEE5N+lSEEdO3Z0q8CTJ0+2ww8/3D0T5DdAgUAQCaAABLFXkCnwBDZu3OiWfdesWWOtWrVyEX6UMZICAQhAAAKFSUCJw0455RTnB7ZgwQK3GqCwz506dSKBWGF+JALdahSAQHcPwgWNwO7du+29995z4T3r1avnbEAVEYICAQhAAAIQqFatmh155JEuMpBWhxUt6OOPP7Y+ffo4x2EIQSAoBFAAgtITyBFoAorus3TpUmfrv3//fre0261bN9OPPQUCEIAABCAQS6Bu3bouZKhMgeQk/NJLL5miB/Xu3dvkQEyBQK4JoADkuge4f+AJKOGLnLsU218/4L169TL9uFMgAAEIQAACZRFo0aKFjRw50q0CaPVYzxNlhO/evbuRG6YscryXaQIoAJkmTP2hJSD7fi3hKrxns2bNbMSIESzhhrY3ERwCEIBAbggoMIRCQ2sC6YMPPrAPP/zQKQRHHHGEcxauXp2hWG56prDvyqeusPuf1icgoDj+mvH/4osvXFhPxXrGzj8BKA5BAAIQgEDSBBQRSJmDtQLw/vvvuwmmxYsXO5NSsgknjZET00QABSBNIKkm/AQ2b97sbDWV1VE2mv3797dDDz2U6A3h71paAAEIQCAwBBRAYsCAASY/Mk02zZ4920UOknLAZFNguinvBUEByPsupoHlEdDAX8uyK1ascDaZctKS4xbLsuWR430IQAACEKgogUaNGtmwYcNM5qZKJjl16lS36izTIJkLValSpaJVcx0EyiWAAlAuIk7IVwKK5a+Bv1K516pVyyVy0TIsibzytcdpFwQgAIHgEVD+APmY6Zk0f/58mzlzpjMRkiKg5GI8k4LXZ/kgUegVgDB50etLHCZ58+EDnqgNa9eudbH8NfD3TX3koCX7TIX4jEQiiS7jWI4J6PujvqlI/zRu3DjH0uf/7bVipj6CdWb6OuKx3ZSg6qrVkn+u8AxKADBAh1q3bu0SS65atcqtCMg0SJNUyjJ/2GGHEXY6x32lFRlt+h6FoeyrWnaY8tArAHv27AlDPzgZNfgPk7yhAZuEoIrjr/Brir6gqD4a+Pft29fZ+CuWvwb+Kvv27YvuJ1Etp2SRgAaY6kdtqZYt3swaJbMEZNes75VmMSkZIOCFIU5UDuw/kPRzhWdQIoLBOVa7dm33DJISLdMgPaukAGhF4K233nIZhTVZpfMo2Seggb82jRPCUCK1isoUM/QKQJmt482CJ7B3716XwEuRFrZv325NmjSxgQMHWrt27UKjxRd8JwIAAhCAQAESaNq0qVME5KemyatFixa5Tf4BXbt2ZbWtAD8T6WwyCkA6aVJXYAjoB1Pp15W9V9q6Iisce+yxxPEPTA8hCAQgAAEIJEOgYcOGLipdz549bcmSJW5TtLqWLVu6gBVt2rRhQisZkJxTjAAKQDEcvAgzAZnxyK5fA39FVSgqKnJ2k4q5LPMECgQgAAEIQCCsBBSsokePHi6L8PLly00r29OnT3cmQQpZLT8BnnVh7d3sy40CkH3m3DHNBLZ4trEa9GtGZPfu3c7MRzH8tUxKKM80w6Y6CEAAAhDIKQH5rWmwr01BLfT8k4nQggULTI7EnTp1soMPPphVgZz2UvBvjgIQ/D5CwgQENNBX3H7NgugHUAN9hUvTD99BBx2U4AoOQQACEIAABPKLQLNmzUybkohpEkzKwOuvv+5CW+uZqI1nYn71ebpagwKQLpLUk3ECMvH54osv3KBf/xUNRvGTme3POHpuAAEIQAACASagCE8yd9UmE1gpA/KB08qAfAh8ZaBu3boBbgWiZZMACkA2aXOvlAlokL969Wpn2y/7foVR1Y+Z7CD1g8aPWcpIuQACEIAABPKYgCbGtPXp08dNmkkZeP/9911ugRYtWlj79u1dYAzCiebxhyCJpqEAJAGJU7JLQFF7lAhFA/6VK1eaQnkqvriy9GpjOTO7/cHdIAABCEAgfATkK6CQ19p8s9lPP/3U5s2b5zaZDuk9RcnDeTh8/VtZiVEAKkuQ69NCYNeuXfbll1+6Ab/+SwnQD5Js+vUDpfj9ysBHgQAEIAABCEAgNQLKdH/44Ye7befOnS4xpibZ3n77bZs/f76bWJMiIOdhJSLjeZsa3zCejQIQxl7LA5kjkYitX7/eDfplz699FZn3yIZRg35m+vOgo2kCBCAAAQgEioBMf3xlQCsDWmmXMqAoQu+9955zIJYioE25BuRfQMk/AigA+dengW3Rtm3bnD2/bPpl4qMfHi1RtmrVyvr27evCl7EMGdjuQzAIQAACEMgzAloZUA4BbVp5/+qrr5zfgFbiP/nkE7cSIFMhKQLalJ24atWqeUahMJuDAlCY/Z6VVu/YsSM64NePyvbt2919/YgEilcshyQpARQIQAACEIAABHJHQOG0/Zl/SbF582a3Si9lYOHChc6RWM9rORjr2S2FQCv1KAS567PK3BkFoDL0uDZKQNF6Nm7c6GLyr1u3zv33B/z169d3s/z+DIKyGVIgAAEIQAACEAguAU3WaevatasLu61nu1bwtflRhaQ0yEfPz0egFQKtKlCCTwAFIPh9FEgJZc6zYcMGZ7uvHwVtitOvIgcizSLoh0CDfkXwoUAAAhCAAAQgEE4CmuX3w4sqDLfMhfTcV84BJeP86KOPnA+BWtegQQOnEEgx0AqBxgSs9Aev31EAgtcngZJIM/tbt251s/sa8Pub4vGryDlIA/0jjjjC/dd+UVFRoNqAMBCAAAQgAAEIpI+AZv79VX3VqsAeMhmSMuBv8iFQUUShRo0aOWXAVwj0Gudihydnf1AAcoY+WDfWl1cmO/oCb9q0KbrptZQAFS3r6cur6AH6rw2n3WD1I9JAAAIQgAAEsk3AH+RrYK/w3SrK4eNPGirSnxQDXynQ+7IO0PlaIZCpkfa1eiDlgpJ5AlDOPONA3UHx9mW+s2XLlmKbZvl9Ex4t9flfxg4dOrgvpb6YZN0NVFciDAQgAAEIQCCwBGQNIGdhbX6R6ZAmGeUzqAlG/V+6dKmLCuifI8VAikD8puM4HPuUKv8fBaDyDANVg0JrKvqOZvO1abCvTQN8/deXzy9aftMXTDP5GuhrXwN/Oe3yJfMp8R8CEIAABCAAgXQQ0Oy+TIW1xRZNTkoxiJ2cVPShJUuWRE/TKoMmImV5ELtJMdBx5Tdg7BLFVe4OCkC5iIJxgmbnlb0v0eYP+PU/doAvyfXF0BdFg3wl19K+BvjaiMYTjL5FCghAAAIQgEAhE9B4JNanwGehsY8mMP1JTH9SU+ZEy5cvj1ou+OdrzBOrEGhfdeu/FARt+B78h1bWFADNTL/11lvOGaRPnz6lOorKq1xpqTUj3blzZ79P8+q/7O3lRKtNXPz/2pcWrC1+33e6jQUhm3x9mPXB1hKb/6GP/Y/nfSwx9iEAAQhAAAIQCAsBjWFkgqwtUdGkaOwkqG/9oP9SEjSe8v0Y/eu1SiClwN80lordl4KgY9r8/XwcS2VFAVAHnXfeeda9e3eXVOKpp56ye++91ykDfofo/zvvvGM33XSTjRgxwsaPH29jxoyx008/PfaUwO0rBJYG63J20ey7/sdu/kDfP+a/Lq0h/gdR/zW4l3OM/8H0tVf/P0tdpVHkOAQgAAEIQAAC+U7AHw8p5GhpRWO0eOuJ2MlW+SIoWWkiZcGvUwqAlAFt8m3w93V/mSbpmDaZOPn7srzQ+C2oJSsKwKRJk6xfv342duxYx+Giiy6yuXPnWv/+/Ytxue++++yOO+6wr33ta3bWWWfZ+eefb6eccooDXezEAL2YMWOG0z59kfQh8T8A/gdEg3nf5Cb2Q6Tj2nSe/18fJAoEIAABCEAAAhCAQOUJ+GOt0lYRYu+giVwpDNp86wz/v97z9zWpK3MkvZbvgj/JG1vXcccdZ61bt449FKj9rCgA8vDWrL5fevfubYsWLSqmAAjsypUrTQkmVHyTli+++MI6duzoX2q33Xab09J0QOEozz6yb/S9XOyMHDnSOZ34Gl9Zs/J6L34pKhcyc8/SCUh5y8elvtJbHJ53pByX9f0qqyVybqdkloB+A9U/sM4M54hX7ZYEVVetVjXpSTL1jyacKMEloGdQRX/ngtuq8Eim74fMqEsreg7JjLu04isC+q969LuYq7KvarUyb50VBUBpoxVhxi/a12A/tsiURl7csTPgepAohmysArB48eLojLtbWjn77Nhqsr7/v1aVf+sq3uqA57FS/omckRMCVfRF9RRR79udk/tz07IJVPEGL+qbsn58S6shdz/BpUmUf8f9QUsuH3j5R/V/LTpQ2kCicX2zHv+Ju/6/sxPv8QxKzCUoR6t4g/+I8u78N/dOUORCjv8Q0PjUKQBl9I+eNUF53uz3VjHKKllRADSj6seYlzCa7ZfdVGyJP8c/L95+6q9//WvsZbZq1apir4P8QstPWiqiBI+ABi9adVKkAdkBUoJHQBMEmlXRkisleAQUYUwzXgrkQMkAAS93S6Kye5dnrpAkc/mUKe46JZgE9AySQ6tMSyjBI6DVAa3QqI/CUGT6VFbxptQyXxTvVTP5ftF+vF2UHDjktS27K78kOs9/j/8QgAAEIAABCEAAAhCAQOoEsqIADBo0yCZPnuxmVjU7NGvWLOvVq5eTVq+1SauSo/Dzzz/vjk+fPt1FwNGMBQUCEIAABCAAAQhAAAIQSA+BrJgADR8+3GbOnGmjR492zi2jRo2K2vU/+uijzklCEYIuueQSu+aaa+zZZ5915ykkKAUCEIAABCAAAQhAAAIQSB+BrCgAmt2//fbbnX21bP/12i/jxo3zd619+/amkKGyk08mXFP0QnYgAAEIQAACEIAABCAAgaQI/G8kntTplTtJsfCTKQz+k6HEORCAAAQgAAEIQAACEEidQFZ8AFIXiysgAAEIQAACEIAABCAAgUwQQAHIBFXqhAAEIAABCEAAAhCAQEAJZNUEKBMMYv0JMlF/OutULoQwyZvOtge9LmVoVnZqJakrL3Zu0NuSr/IpAZiSsPAdCmYPK2yzMrcrpDMl/QQitWrbgRNPKFFxlZ49rGqMX12JE2IO8AyKgRHA3SVLlrgcSWVlog2g2AUjkp+oNizPIOXXKqtU8R6qpD0tixDvFQSBtWvX2sCBA+03v/mNnXjiiQXRZhoJgXQSuP/+++1vf/ubzZ49O53VUhcECoaAQqF/73vfs8svv7xg2kxDc0cAE6DcsefOEIAABCAAAQhAAAIQyDoBFICsI+eGEIAABCAAAQhAAAIQyB2B0PsA5A4dd84nAnXr1rUrr7zSDjvssHxqFm2BQNYIHHPMMc6HJms35EYQyDMCF110kR155JF51iqaE1QC+AAEtWeQCwIQgAAEIAABCEAAAhkggAlQBqBSJQQgAAEIQAACEIAABIJKAAUgqD2DXDkhsHTpUps2bZrt3LkzJ/fnphAII4Hdu3fbzJkzbdasWbZ3794wNgGZIZBTAhs3brRXXnnFVq1alVM5uHnhEMAEqHD6mpaWQ+Cqq64yxc099NBDnRJw9dVXW9++fcu5irchUNgEpCyfd9551r17d/vyyy9dHo17773X5WwobDK0HgLJEXjuuefs6aeftsGDB9v8+fOtS5cuNnbs2OQu5iwIVJAATsAVBMdl+UVgwYIFplwAEyZMcA3r3Lmzi2mOApBf/Uxr0k9g0qRJpvjl/oBFjoxz5861/v37p/9m1AiBPCOg5Gx67txzzz3WsWNHO/vss+2ss86yc8891xo3bpxnraU5QSKACVCQegNZckagW7du9vDDD0fvv3nzZtu1a1f0NTsQgEBiAjKb6927d/RN7SurNgUCECifgFad//znP7vBv87Wc2fbtm0mxYACgUwSQAHIJF3qDg2BqlWruhTsEnjNmjVuRuacc84JjfwICoFcEVi9enWx8J8NGjSw9evX50oc7guB0BFQGGqVAwcOmDJqn3TSSda0adPQtQOBw0UAE6Bw9RfSponA8uXL7YUXXnC1acCi5VYVHb/22mvtBz/4ASYMjgh/IFA2Ac1gxs5W7tu3L6pMl30l70IAAj4BOdLfdtttFolE7IYbbvAP8x8CGSOAApAxtFQcZAJFRUXWsGFDJ2K9evXc/w8//NCuv/56+/GPf2xDhgwJsvjIBoHAENBM5YYNG6LyaL9t27bR1+xAAAJlE9ixY4ebeDr44INt3LhxLhhF2VfwLgQqT4AoQJVnSA15QGDdunVu1l8zML169cqDFtEECGSHwJQpU+yll16yO++809kuX3rppXbXXXdFbZqzIwV3gUB4CWjQ3759e7vsssvC2wgkDx0BFIDQdRkCZ4LA+PHj7YknnigWuvCggw6yf/zjH5m4HXVCIG8IyOTn1ltvNUXSki/NqFGj7Mwzz8yb9tEQCGSSgFaeL7zwQneLKlWqRG/1wAMPWI8ePaKv2YFAugmgAKSbKPVBAAIQKEACW7dudbb/1atjWVqA3U+TIQCBkBFAAQhZhyEuBCAAAQhAAAIQgAAEKkOAMKCVoce1EIAABCAAAQhAAAIQCBkBFICQdRjiQgACEIAABCAAAQhAoDIEUAAqQ49rIQABCEAAAhCAAAQgEDICKAAh6zDEhQAEIAABCEAAAhCAQGUIoABUhh7XQgACEIAABCAAAQhAIGQEUABC1mGICwEIQAACEIAABCAAgcoQQAGoDD2uhQAEIAABCEAAAhCAQMgIoACErMMQFwIQgAAEIAABCEAAApUhgAJQGXpcCwEIQAACEIAABCAAgZARQAEIWYchLgQgAAEIQAACEIAABCpDAAWgMvS4FgIQgAAEIAABCEAAAiEjgAIQsg5DXAhAAAIQgAAEIAABCFSGAApAZehxLQQgAAEIQAACEIAABEJGAAUgZB2GuBCAAAQgAAEIQAACEKgMARSAytDjWghAAAIQgAAEIAABCISMAApAyDoMcSEAAQhAAAIQgAAEIFAZAigAlaHHtRCAAAQgAAEIQAACEAgZARSAkHUY4kIAAhCAAAQgAAEIQKAyBFAAKkOPayEAAQhAAAIQgAAEIBAyAigAIeswxIUABCAAAQhAAAIQgEBlCKAAVIYe10IAAhCAAAQgAAEIQCBkBFAAQtZhiAsBCEAAAhCAAAQgAIHKEEABqAw9roUABCAAAQhAAAIQgEDICKAAhKzDEBcCEIAABCAAAQhAAAKVIYACUBl6XAsBCEAAAhCAAAQgAIGQEUABCFmHIS4EIAABCEAAAhCAAAQqQ+D/AR2R4UWL5e0yAAAAAElFTkSuQmCC" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-t-distribution-on-top-of-permuted-t-statistics" class="section level2">
diff --git a/man/get_pvalue.Rd b/man/get_pvalue.Rd
index 2ba28733..da59b651 100644
--- a/man/get_pvalue.Rd
+++ b/man/get_pvalue.Rd
@@ -33,11 +33,11 @@ d_hat <- mtcars_df \%>\%
   specify(mpg ~ am) \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
 null_distn <- mtcars_df \%>\%
-  specify(mpg ~ am) \%>\% 
+  specify(mpg ~ am) \%>\%
   hypothesize(null = "independence") \%>\%
   generate(reps = 100) \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
-null_distn \%>\% 
+null_distn \%>\%
   p_value(obs_stat = d_hat, direction = "right")
 
 }
diff --git a/man/visualize.Rd b/man/visualize.Rd
index e7c2a8c4..e3799c12 100755
--- a/man/visualize.Rd
+++ b/man/visualize.Rd
@@ -57,7 +57,7 @@ Visualize the distribution of the simulation-based inferential statistics or
 the theoretical distribution (or both!).
 }
 \examples{
-# Permutations to create a simulation-based null distribution for 
+# Permutations to create a simulation-based null distribution for
 # one numerical response and one categorical predictor
 # using t statistic
 mtcars \%>\%
@@ -68,7 +68,7 @@ mtcars \%>\%
   calculate(stat = "t", order = c("1", "0")) \%>\%
   visualize(method = "simulation") #default method
 
-# Theoretical t distribution for 
+# Theoretical t distribution for
 # one numerical response and one categorical predictor
 # using t statistic
 mtcars \%>\%

From 3d761f9c14978ff6eda9b137c91ebd2155ca102f Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 6 Aug 2018 08:13:27 -0700
Subject: [PATCH 12/78] Ignore docs folder since netlify set up to push to
 gh-pages-dev branch now

---
 .Rbuildignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.Rbuildignore b/.Rbuildignore
index a4139f1e..df4589d6 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -18,3 +18,5 @@
 ^TO-DO\.md$
 ^\.httr-oauth$
 ^_pkgdown.yml
+^_pkgdown\.yml$
+^docs$

From 477f56c4e82a866f3047d0953a4a1450abf3df20 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Mon, 6 Aug 2018 22:49:16 +0300
Subject: [PATCH 13/78] Fix `calculate()` to not depend on order of `p` (fixes
 #122).

---
 NEWS.md                         |  1 +
 R/calculate.R                   |  5 ++++-
 tests/testthat/test-calculate.R | 17 +++++++++++++++++
 3 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 3e0c02a4..00d4c356 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,7 @@
 - Update documentation code to follow tidyverse style guide (#159).
 - Remove help page for internal `set_params()` (#165).
 - Fully use {tibble} (#166).
+- Fix `calculate()` to not depend on order of `p` for `type = "simulate"` (#122).
 
 # infer 0.3.1
 
diff --git a/R/calculate.R b/R/calculate.R
index 6c7b03a0..8fce9081 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -233,10 +233,13 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
     # Chi-Square Goodness of Fit
     if (!is.null(attr(x, "params"))) {
       # When `hypothesize()` has been called
+      p_levels <- get_par_levels(x)
       x %>%
         dplyr::summarize(
           stat = stats::chisq.test(
-            table(!!(attr(x, "response"))), p = attr(x, "params")
+            # Ensure correct ordering of parameters
+            table(!!(attr(x, "response")))[p_levels],
+            p = attr(x, "params")
           )$stat
         )
     } else {
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index e34bc222..6b28150e 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -409,3 +409,20 @@ test_that("One sample t bootstrap is working", {
       calculate(stat = "t")
   )
 })
+
+test_that("calculate doesn't depend on order of `p` (#122)", {
+  calc_chisq <- function(p) {
+    set.seed(111)
+    
+    iris %>%
+      specify(Species ~ NULL) %>%
+      hypothesize(null = "point", p = p) %>%
+      generate(reps = 10, type = "simulate") %>%
+      calculate("Chisq")
+  }
+  
+  expect_equal(
+    calc_chisq(c("versicolor" = 0.25, "setosa" = 0.5, "virginica" = 0.25)),
+    calc_chisq(c("virginica" = 0.25, "versicolor" = 0.25, "setosa" = 0.5))
+  )
+})

From 1083bb32974c7a7b0ebf926b53c6b904771b6fcb Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 10:36:50 +0300
Subject: [PATCH 14/78] Use `is_nuat(x, at)` wrapper for `is.null(attr(x,
 at))`.

---
 R/calculate.R  | 18 +++++++++---------
 R/generate.R   |  6 +++---
 R/p_value.R    |  6 +++---
 R/set_params.R | 12 ++++++------
 R/specify.R    |  8 ++++----
 R/utils.R      | 14 +++++++++-----
 R/visualize.R  |  2 +-
 7 files changed, 35 insertions(+), 31 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 8fce9081..814267ad 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -48,8 +48,8 @@ calculate <- function(x,
     )
   }
 
-  if (is.null(attr(x, "generate")) || !attr(x, "generate")) {
-    if (is.null(attr(x, "null"))) {
+  if (is_nuat(x, "generate") || !attr(x, "generate")) {
+    if (is_nuat(x, "null")) {
       x$replicate <- 1L
     } else if (
       stat %in% c(
@@ -75,7 +75,7 @@ calculate <- function(x,
   if (
     (stat %in% c("diff in means", "diff in medians", "diff in props")) ||
     (
-      !is.null(attr(x, "theory_type")) &&
+      !is_nuat(x, "theory_type") &&
       (attr(x, "theory_type") %in% c("Two sample props z", "Two sample t"))
     )
   ) {
@@ -85,7 +85,7 @@ calculate <- function(x,
   if (!(
     (stat %in% c("diff in means", "diff in medians", "diff in props")) ||
     (
-      !is.null(attr(x, "theory_type")) &&
+      !is_nuat(x, "theory_type") &&
       attr(x, "theory_type") %in% c("Two sample props z", "Two sample t")
     )
   )) {
@@ -161,7 +161,7 @@ calc_impl.prop <- function(stat, x, order, ...) {
   #   )
   # }
 
-  if (is.null(attr(x, "success"))) {
+  if (is_nuat(x, "success")) {
     stop_glue(
       'To calculate a proportion, the `"success"` argument must be provided ',
       'in `specify()`.'
@@ -229,9 +229,9 @@ calc_impl.diff_in_medians <- function(stat, x, order, ...) {
 calc_impl.Chisq <- function(stat, x, order, ...) {
   ## The following could stand to be cleaned up
 
-  if (is.null(attr(x, "explanatory"))) {
+  if (is_nuat(x, "explanatory")) {
     # Chi-Square Goodness of Fit
-    if (!is.null(attr(x, "params"))) {
+    if (!is_nuat(x, "params")) {
       # When `hypothesize()` has been called
       p_levels <- get_par_levels(x)
       x %>%
@@ -280,7 +280,7 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
       ) %>%
       dplyr::ungroup()
 
-    if (!is.null(attr(x, "generate"))) {
+    if (!is_nuat(x, "generate")) {
       result <- result %>% dplyr::select(replicate, stat = statistic)
     } else {
       result <- result %>% dplyr::select(stat = statistic)
@@ -360,7 +360,7 @@ calc_impl.t <- function(stat, x, order, ...) {
   # One sample mean
   else if (attr(x, "theory_type") == "One sample t") {
     # For bootstrap
-    if (is.null(attr(x, "null"))) {
+    if (is_nuat(x, "null")) {
       x %>%
         dplyr::summarize(
           stat = stats::t.test(!!attr(x, "response"), ...)[["statistic"]]
diff --git a/R/generate.R b/R/generate.R
index 16e54745..44a81ad0 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -43,7 +43,7 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
   if (
     (type == "permute") &&
-    any(is.null(attr(x, "response")), is.null(attr(x, "explanatory")))
+    any(is_nuat(x, "response"), is_nuat(x, "explanatory"))
   ) {
     stop_glue(
       "Please `specify()` an explanatory and a response variable when ",
@@ -61,7 +61,7 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 #   if (
 #     (type == "bootstrap") &&
 #     !(attr(attr(x, "params"), "names") %in% c("mu", "med", "sigma")) &&
-#     !is.null(attr(x, "null"))
+#     !is_nuat(x, "null")
 #   ) {
 #     stop_glue(
 #       "Bootstrapping is inappropriate in this setting. ",
@@ -85,7 +85,7 @@ generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
 
 bootstrap <- function(x, reps = 1, ...) {
   # Check if hypothesis test chosen
-  if (!is.null(attr(x, "null"))) {
+  if (!is_nuat(x, "null")) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
     if (attr(attr(x, "params"), "names") == "mu") {
diff --git a/R/p_value.R b/R/p_value.R
index cd5f8af9..963329a4 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -36,7 +36,7 @@ p_value <- function(x, obs_stat, direction) {
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
 
-  is_simulation_based <- !is.null(attr(x, "generate")) && attr(x, "generate")
+  is_simulation_based <- !is_nuat(x, "generate") && attr(x, "generate")
 
   if (is_simulation_based) {
     pvalue <- simulation_based_p_value(
@@ -47,7 +47,7 @@ p_value <- function(x, obs_stat, direction) {
   ## Theoretical-based p-value
   # Could be more specific
   # else if (
-  #   is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param"))
+  #   is_nuat(x, "theory_type") || is_nuat(x, "distr_param")
   # ) {
   #   stop_glue(
   #     "Attributes have not been set appropriately. ",
@@ -112,7 +112,7 @@ get_pvalue <- p_value
 
 # which_distribution <- function(x, theory_type, obs_stat, direction) {
 #   param <- attr(x, "distr_param")
-#   if (!is.null(attr(x, "distr_param2"))) {
+#   if (!is_nuat(x, "distr_param2")) {
 #     param2 <- attr(x, "distr_param2")
 #   }
 # 
diff --git a/R/set_params.R b/R/set_params.R
index 0867a5f7..d7513ba2 100755
--- a/R/set_params.R
+++ b/R/set_params.R
@@ -6,14 +6,14 @@
 set_params <- function(x) {
   attr(x, "theory_type") <- NULL
 
-  if (!is.null(attr(x, "response"))) {
+  if (!is_nuat(x, "response")) {
     num_response_levels <- length(levels(response_variable(x)))
   }
 
   # One variable
   if (
-    !is.null(attr(x, "response")) && is.null(attr(x, "explanatory")) &&
-    !is.null(attr(x, "response_type")) && is.null(attr(x, "explanatory_type"))
+    !is_nuat(x, "response") && is_nuat(x, "explanatory") &&
+    !is_nuat(x, "response_type") && is_nuat(x, "explanatory_type")
   ) {
 
     # One mean
@@ -43,8 +43,8 @@ set_params <- function(x) {
 
   # Two variables
   if (
-    !is.null(attr(x, "response")) && !is.null(attr(x, "explanatory")) &
-    !is.null(attr(x, "response_type")) && !is.null(attr(x, "explanatory_type"))
+    !is_nuat(x, "response") && !is_nuat(x, "explanatory") &
+    !is_nuat(x, "response_type") && !is_nuat(x, "explanatory_type")
   ) {
     attr(x, "type") <- "bootstrap"
 
@@ -130,7 +130,7 @@ set_params <- function(x) {
     }
   }
 
-#  if(is.null(attr(x, "theory_type")))
+#  if(is_nuat(x, "theory_type"))
 #     warning_glue("Theoretical type not yet implemented")
 
   x
diff --git a/R/specify.R b/R/specify.R
index 937b3f09..93a69306 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -56,7 +56,7 @@ specify <- function(x, formula, response = NULL,
     attr(x, "explanatory") <- f_rhs(formula)
   }
 
-  if (is.null(attr(x, "response"))) {
+  if (is_nuat(x, "response")) {
     stop_glue("Supply not `NULL` response variable.")
   }
 
@@ -130,7 +130,7 @@ specify <- function(x, formula, response = NULL,
   # To help determine theoretical distribution to plot
   attr(x, "response_type") <- class(response_variable(x))
 
-  if (is.null(attr(x, "explanatory"))) {
+  if (is_nuat(x, "explanatory")) {
     attr(x, "explanatory_type") <- NULL
   } else {
     attr(x, "explanatory_type") <- class(explanatory_variable(x))
@@ -140,9 +140,9 @@ specify <- function(x, formula, response = NULL,
     (attr(x, "response_type") == "factor") && is.null(success) &&
     (length(levels(response_variable(x))) == 2) &&
     (
-      is.null(attr(x, "explanatory_type")) ||
+      is_nuat(x, "explanatory_type") ||
       (
-        !is.null(attr(x, "explanatory_type")) &&
+        !is_nuat(x, "explanatory_type") &&
         (length(levels(explanatory_variable(x))) == 2)
       )
     )
diff --git a/R/utils.R b/R/utils.R
index 66af83d3..c0b5a90b 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -26,6 +26,10 @@ set_attributes <- function(to, from = x) {
   to
 }
 
+is_nuat <- function(x, at) {
+  is.null(attr(x, at))
+}
+
 explanatory_variable <- function(x) {
   x[[as.character(attr(x, "explanatory"))]]
 }
@@ -43,11 +47,11 @@ reorder_explanatory <- function(x, order) {
 }
 
 has_explanatory <- function(x) {
-  !is.null(attr(x, "explanatory"))
+  !is_nuat(x, "explanatory")
 }
 
 has_response <- function(x) {
-  !is.null(attr(x, "response"))
+  !is_nuat(x, "response")
 }
 
 stop_glue <- function(..., .sep = "", .envir = parent.frame(),
@@ -139,7 +143,7 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
     )
   }
 
-  if (!("replicate" %in% names(x)) && !is.null(attr(x, "generate"))) {
+  if (!("replicate" %in% names(x)) && !is_nuat(x, "generate")) {
     warning_glue(
       'A `generate()` step was not performed prior to `calculate()`. ',
       'Review carefully.'
@@ -193,7 +197,7 @@ check_for_factor_stat <- function(x, stat, explanatory_variable) {
 check_point_params <- function(x, stat) {
   param_names <- attr(attr(x, "params"), "names")
   hyp_text <- 'to be set in `hypothesize()`.'
-  if (!is.null(attr(x, "null"))) {
+  if (!is_nuat(x, "null")) {
     if (stat %in% c("mean", "median", "sd", "prop")) {
       if ((stat == "mean") && !("mu" %in% param_names)) {
         stop_glue('`stat == "mean"` requires `"mu"` {hyp_text}')
@@ -243,7 +247,7 @@ parse_params <- function(dots, x) {
   # 0 index of dots
   if (length(p_ind)) {
     if (length(dots[[p_ind]]) == 1) {
-      if ((attr(x, "null") == "point") && is.null(attr(x, "success"))) {
+      if ((attr(x, "null") == "point") && is_nuat(x, "success")) {
         stop_glue(
           "A point null regarding a proportion requires that `success` ",
           "be indicated in `specify()`."
diff --git a/R/visualize.R b/R/visualize.R
index bda7887c..3d71b4be 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -549,7 +549,7 @@ visualize_theoretical <- function(data,
   )
 
   if (
-    !is.null(attr(data, "stat")) &&
+    !is_nuat(data, "stat") &&
     !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))
   ) {
     warning_glue(

From e641688a7df5fdc5282f7403b5a0474cb9e15ef9 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 10:56:55 +0300
Subject: [PATCH 15/78] Refactor `set_attributes()` into more flexible
 `copy_attrs()`.

---
 R/calculate.R | 19 ++++++++-----------
 R/generate.R  |  6 +++---
 R/utils.R     | 24 ++++++++++--------------
 3 files changed, 21 insertions(+), 28 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 814267ad..d0c3a234 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -111,7 +111,7 @@ calculate <- function(x,
 #     class(result) <- append("infer", class(result))
 #   }
 
-  result <- set_attributes(to = result, from = x)
+  result <- copy_attrs(to = result, from = x)
   attr(result, "stat") <- stat
 
   # For returning a 1x1 observed statistic value
@@ -286,16 +286,13 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
       result <- result %>% dplyr::select(stat = statistic)
     }
 
-    attr(result, "response") <- attr(x, "response")
-    attr(result, "success") <- attr(x, "success")
-    attr(result, "explanatory") <- attr(x, "explanatory")
-    attr(result, "response_type") <- attr(x, "response_type")
-    attr(result, "explanatory_type") <- attr(x, "explanatory_type")
-    attr(result, "distr_param") <- attr(x, "distr_param")
-    attr(result, "distr_param2") <- attr(x, "distr_param2")
-    attr(result, "theory_type") <- attr(x, "theory_type")
-
-    result
+    copy_attrs(
+      to = result, from = x,
+      attrs = c(
+        "response", "success", "explanatory", "response_type",
+        "explanatory_type", "distr_param", "distr_param2", "theory_type"
+      )
+    )
   }
 }
 
diff --git a/R/generate.R b/R/generate.R
index 44a81ad0..61f3856a 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -126,7 +126,7 @@ bootstrap <- function(x, reps = 1, ...) {
 
   # Set variables for use in calculate()
   result <- rep_sample_n(x, size = nrow(x), replace = TRUE, reps = reps)
-  result <- set_attributes(to = result, from = x)
+  result <- copy_attrs(to = result, from = x)
 
   class(result) <- append("infer", class(result))
 
@@ -140,7 +140,7 @@ permute <- function(x, reps = 1, ...) {
     dplyr::mutate(replicate = rep(1:reps, each = nrow(x))) %>%
     dplyr::group_by(replicate)
 
-  df_out <- set_attributes(to = df_out, from = x)
+  df_out <- copy_attrs(to = df_out, from = x)
 
   class(df_out) <- append("infer", class(df_out))
 
@@ -176,7 +176,7 @@ simulate <- function(x, reps = 1, ...) {
     replicate = as.factor(rep(1:reps, rep(nrow(x), reps)))
   )
 
-  rep_tbl <- set_attributes(to = rep_tbl, from = x)
+  rep_tbl <- copy_attrs(to = rep_tbl, from = x)
 
   class(rep_tbl) <- append("infer", class(rep_tbl))
 
diff --git a/R/utils.R b/R/utils.R
index c0b5a90b..2bee15d5 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -9,20 +9,16 @@ get_par_levels <- function(x) {
   gsub("^.\\.", "", par_names)
 }
 
-set_attributes <- function(to, from = x) {
-  attr(to, "response") <- attr(from, "response")
-  attr(to, "success") <- attr(from, "success")
-  attr(to, "explanatory") <- attr(from, "explanatory")
-  attr(to, "response_type") <- attr(from, "response_type")
-  attr(to, "explanatory_type") <- attr(from, "explanatory_type")
-  attr(to, "distr_param") <- attr(from, "distr_param")
-  attr(to, "distr_param2") <- attr(from, "distr_param2")
-  attr(to, "null") <- attr(from, "null")
-  attr(to, "params") <- attr(from, "params")
-  attr(to, "theory_type") <- attr(from, "theory_type")
-  attr(to, "generate") <- attr(from, "generate")
-  attr(to, "type") <- attr(from, "type")
-
+copy_attrs <- function(to, from,
+                       attrs = c(
+                         "response", "success", "explanatory", "response_type",
+                         "explanatory_type", "distr_param", "distr_param2",
+                         "null", "params", "theory_type", "generate", "type"
+                       )) {
+  for (at in attrs) {
+    attr(to, at) <- attr(from, at)
+  }
+  
   to
 }
 

From d18be575690e5c3098e78635a55c38af27586f6e Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 12:00:50 +0300
Subject: [PATCH 16/78] Refactor `calc_impl()` methods.

---
 R/calculate.R                   | 79 ++++++++++++++-------------------
 R/infer.R                       |  2 +-
 tests/testthat/test-calculate.R |  8 ++++
 3 files changed, 42 insertions(+), 47 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index d0c3a234..328801c5 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -126,31 +126,23 @@ calc_impl <- function(type, x, order, ...) {
   UseMethod("calc_impl", type)
 }
 
-calc_impl.mean <- function(stat, x, order, ...) {
-  col <- base::setdiff(names(x), "replicate")
-
-  x %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = mean(!!(sym(col)), ...))
+calc_impl_one_f <- function(f) {
+  function(type, x, order, ...) {
+    col <- base::setdiff(names(x), "replicate")
+    
+    x %>%
+      dplyr::group_by(replicate) %>%
+      dplyr::summarize(stat = f(!!(sym(col)), ...))
+  }
 }
 
-calc_impl.median <- function(stat, x, order, ...) {
-  col <- base::setdiff(names(x), "replicate")
+calc_impl.mean <- calc_impl_one_f(mean)
 
-  x %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = stats::median(!!(sym(col)), ...))
-}
-
-calc_impl.sd <- function(stat, x, order, ...) {
-  col <- base::setdiff(names(x), "replicate")
+calc_impl.median <- calc_impl_one_f(stats::median)
 
-  x %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(stat = stats::sd(!!(sym(col)), ...))
-}
+calc_impl.sd <- calc_impl_one_f(stats::sd)
 
-calc_impl.prop <- function(stat, x, order, ...) {
+calc_impl.prop <- function(type, x, order, ...) {
   col <- base::setdiff(names(x), "replicate")
 
   ## No longer needed with implementation of `check_point_params()`
@@ -179,7 +171,7 @@ calc_impl.prop <- function(stat, x, order, ...) {
     )
 }
 
-calc_impl.F <- function(stat, x, order, ...) {
+calc_impl.F <- function(type, x, order, ...) {
   x %>%
     dplyr::summarize(
       stat = stats::anova(
@@ -188,7 +180,7 @@ calc_impl.F <- function(stat, x, order, ...) {
     )
 }
 
-calc_impl.slope <- function(stat, x, order, ...) {
+calc_impl.slope <- function(type, x, order, ...) {
   x %>%
     dplyr::summarize(
       stat = stats::coef(
@@ -197,36 +189,31 @@ calc_impl.slope <- function(stat, x, order, ...) {
     )
 }
 
-calc_impl.correlation <- function(stat, x, order, ...) {
+calc_impl.correlation <- function(type, x, order, ...) {
   x %>%
     dplyr::summarize(
       stat = stats::cor(!!attr(x, "explanatory"), !!attr(x, "response"))
     )
 }
 
-calc_impl.diff_in_means <- function(stat, x, order, ...) {
-  x %>%
-    dplyr::group_by(replicate, !!attr(x, "explanatory")) %>%
-    dplyr::summarize(xbar = mean(!!attr(x, "response"), ...)) %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(
-      stat = xbar[!!(attr(x, "explanatory")) == order[1]] -
-        xbar[!!(attr(x, "explanatory")) == order[2]]
-    )
+calc_impl_diff_f <- function(f) {
+  function(type, x, order, ...) {
+    x %>%
+      dplyr::group_by(replicate, !!attr(x, "explanatory")) %>%
+      dplyr::summarize(value = f(!!attr(x, "response"), ...)) %>%
+      dplyr::group_by(replicate) %>%
+      dplyr::summarize(
+        stat = value[!!(attr(x, "explanatory")) == order[1]] -
+          value[!!(attr(x, "explanatory")) == order[2]]
+      )
+  }
 }
 
-calc_impl.diff_in_medians <- function(stat, x, order, ...) {
-  x %>%
-    dplyr::group_by(replicate, !!(attr(x, "explanatory"))) %>%
-    dplyr::summarize(xtilde = stats::median(!!attr(x, "response"), ...)) %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(
-      stat = xtilde[!!(attr(x, "explanatory")) == order[1]] -
-        xtilde[!!(attr(x, "explanatory")) == order[2]]
-    )
-}
+calc_impl.diff_in_means <- calc_impl_diff_f(mean)
+
+calc_impl.diff_in_medians <- calc_impl_diff_f(stats::median)
 
-calc_impl.Chisq <- function(stat, x, order, ...) {
+calc_impl.Chisq <- function(type, x, order, ...) {
   ## The following could stand to be cleaned up
 
   if (is_nuat(x, "explanatory")) {
@@ -296,7 +283,7 @@ calc_impl.Chisq <- function(stat, x, order, ...) {
   }
 }
 
-calc_impl.diff_in_props <- function(stat, x, order, ...) {
+calc_impl.diff_in_props <- function(type, x, order, ...) {
   col <- attr(x, "response")
   success <- attr(x, "success")
 
@@ -309,7 +296,7 @@ calc_impl.diff_in_props <- function(stat, x, order, ...) {
     )
 }
 
-calc_impl.t <- function(stat, x, order, ...) {
+calc_impl.t <- function(type, x, order, ...) {
   # Two sample means
 
   if (attr(x, "theory_type") == "Two sample t") {
@@ -374,7 +361,7 @@ calc_impl.t <- function(stat, x, order, ...) {
   }
 }
 
-calc_impl.z <- function(stat, x, order, ...) {
+calc_impl.z <- function(type, x, order, ...) {
   # Two sample proportions
   if (attr(x, "theory_type") == "Two sample props z") {
     col <- attr(x, "response")
diff --git a/R/infer.R b/R/infer.R
index a6621b57..69614a05 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -17,7 +17,7 @@ NULL
 if (getRversion() >= "2.15.1") {
   utils::globalVariables(
     c(
-      "prop", "stat", "xbar", "xtilde", "x", "..density..", "statistic", ".",
+      "prop", "stat", "value", "x", "..density..", "statistic", ".",
       "parameter", "p.value", "xmin", "xmax", "density", "denom", "diff_prop",
       "group_num", "n1", "n2", "num_suc", "p_hat", "total_suc", "explan",
       "probs", "conf.low", "conf.high"
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 6b28150e..096cb750 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -426,3 +426,11 @@ test_that("calculate doesn't depend on order of `p` (#122)", {
     calc_chisq(c("virginica" = 0.25, "versicolor" = 0.25, "setosa" = 0.5))
   )
 })
+
+test_that("calc_impl_one_f works", {
+  expect_true(is.function(calc_impl_one_f(mean)))
+})
+
+test_that("calc_impl_diff_f works", {
+  expect_true(is.function(calc_impl_diff_f(mean)))
+})
\ No newline at end of file

From a88f0288280913808b223a1ba755af17eb7c91ea Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 12:41:03 +0300
Subject: [PATCH 17/78] Move common data in tests into separate helper file.

---
 tests/testthat/helper-data.R      | 18 ++++++
 tests/testthat/test-calculate.R   |  8 +--
 tests/testthat/test-conf_int.R    | 12 ----
 tests/testthat/test-generate.R    | 97 ++++++++++++++-----------------
 tests/testthat/test-hypothesize.R | 40 ++++++-------
 tests/testthat/test-p_value.R     | 12 ----
 tests/testthat/test-specify.R     | 56 ++++++++----------
 tests/testthat/test-visualize.R   |  6 --
 8 files changed, 104 insertions(+), 145 deletions(-)
 create mode 100644 tests/testthat/helper-data.R

diff --git a/tests/testthat/helper-data.R b/tests/testthat/helper-data.R
new file mode 100644
index 00000000..12701584
--- /dev/null
+++ b/tests/testthat/helper-data.R
@@ -0,0 +1,18 @@
+iris_tbl <- iris %>%
+  tibble::as_tibble() %>%
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
+    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
+  )
+
+iris_calc <- iris_tbl %>%
+  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
+  hypothesize(null = "independence") %>%
+  generate(reps = 1000) %>%
+  calculate(stat = "diff in props", order = c("large", "small"))
+
+mtcars_df <- mtcars %>%
+  dplyr::mutate(
+    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
+    carb = factor(carb)
+  )
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 096cb750..daefde6c 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -2,12 +2,6 @@ context("calculate")
 
 iris_df <- tibble::as_tibble(iris)
 
-iris_tbl <- iris_df %>%
-  dplyr::mutate(
-    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
-  )
-
 # calculate arguments
 test_that("x is a tibble", {
   vec <- 1:10
@@ -433,4 +427,4 @@ test_that("calc_impl_one_f works", {
 
 test_that("calc_impl_diff_f works", {
   expect_true(is.function(calc_impl_diff_f(mean)))
-})
\ No newline at end of file
+})
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
index 60da5354..5cfe0060 100644
--- a/tests/testthat/test-conf_int.R
+++ b/tests/testthat/test-conf_int.R
@@ -1,17 +1,5 @@
 context("conf_int")
 
-iris_tbl <- iris %>%
-  dplyr::mutate(
-    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
-  )
-
-iris_calc <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "diff in props", order = c("large", "small"))
-
 obs_diff <- iris_tbl %>%
   specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
   calculate(stat = "diff in props", order = c("large", "small"))
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index c2de7d46..70088b90 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -1,44 +1,38 @@
 context("generate")
 
-mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(
-    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
-    carb = factor(carb)
-  )
-
-hyp_prop <- mtcars %>%
+hyp_prop <- mtcars_df %>%
   specify(response = am, success = "1") %>%
   hypothesize(null = "point", p = .5)
 
-hyp_diff_in_props <- mtcars %>%
+hyp_diff_in_props <- mtcars_df %>%
   specify(am ~ vs, success = "1") %>%
   hypothesize(null = "independence")
 
-hyp_chisq_gof <- mtcars %>%
+hyp_chisq_gof <- mtcars_df %>%
   specify(response = cyl) %>%
   hypothesize(null = "point", p = c("4" = 1/3, "6" = 1/3, "8" = 1/3))
 
-hyp_chisq_ind <- mtcars %>%
+hyp_chisq_ind <- mtcars_df %>%
   specify(cyl ~ vs) %>%
   hypothesize(null = "independence")
 
-hyp_mean <- mtcars %>%
+hyp_mean <- mtcars_df %>%
   specify(response = mpg) %>%
   hypothesize(null = "point", mu = 3)
 
-hyp_median <- mtcars %>%
+hyp_median <- mtcars_df %>%
   specify(response = mpg) %>%
   hypothesize(null = "point", med = 3)
 
-hyp_sd <- mtcars %>%
+hyp_sd <- mtcars_df %>%
   specify(response = mpg) %>%
   hypothesize(null = "point", sigma = 7)
 
-hyp_diff_in_means <- mtcars %>%
+hyp_diff_in_means <- mtcars_df %>%
   specify(mpg ~ vs) %>%
   hypothesize(null = "independence")
 
-hyp_anova <- mtcars %>%
+hyp_anova <- mtcars_df %>%
   specify(mpg ~ cyl) %>%
   hypothesize(null = "independence")
 
@@ -72,81 +66,76 @@ test_that("cohesion with type argument", {
 
 test_that("sensible output", {
   expect_equal(
-    nrow(mtcars) * 500, nrow(generate(hyp_prop, reps = 500, type = "simulate"))
+    nrow(mtcars_df) * 500,
+    nrow(generate(hyp_prop, reps = 500, type = "simulate"))
   )
   expect_silent(generate(hyp_mean, reps = 1, type = "bootstrap"))
   expect_error(generate(hyp_mean, reps = 1, type = "other"))
 })
 
 test_that("auto `type` works (generate)", {
-  mtcars <- as.data.frame(mtcars) %>%
-    dplyr::mutate(
-      cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
-      carb = factor(carb)
-    )
-
-  one_mean <- mtcars %>%
+  one_mean <- mtcars_df %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", mu = 25) %>%
     generate(reps = 100)
 
-  one_nonshift_mean <- mtcars %>%
+  one_nonshift_mean <- mtcars_df %>%
     specify(response = mpg) %>%
     generate(reps = 100)
 
-  one_median <- mtcars %>%
+  one_median <- mtcars_df %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", med = 26) %>%
     generate(reps = 100)
 
-  one_prop <- mtcars %>%
+  one_prop <- mtcars_df %>%
     specify(response = am, success = "1") %>% # formula alt: am ~ NULL
     hypothesize(null = "point", p = .25) %>%
     generate(reps = 100)
 
-  two_props <- mtcars %>%
+  two_props <- mtcars_df %>%
     specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
     hypothesize(null = "independence") %>%
     generate(reps = 100)
 
-  gof_chisq <- mtcars %>%
+  gof_chisq <- mtcars_df %>%
     specify(cyl ~ NULL) %>% # alt: response = cyl
     hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
     generate(reps = 100)
 
-  indep_chisq <- mtcars %>%
+  indep_chisq <- mtcars_df %>%
     specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
     hypothesize(null = "independence") %>%
     generate(reps = 100)
 
-  two_means <- mtcars %>%
+  two_means <- mtcars_df %>%
     specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
     hypothesize(null = "independence") %>%
     generate(reps = 100)
 
-  anova_f <- mtcars %>%
+  anova_f <- mtcars_df %>%
     specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100)
 
-  slopes <- mtcars %>%
+  slopes <- mtcars_df %>%
     specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100)
 
-  one_nonshift_prop <- mtcars %>%
+  one_nonshift_prop <- mtcars_df %>%
     specify(response = am, success = "1") %>%
     generate(reps = 100)
 
-  two_means_boot <- mtcars %>%
+  two_means_boot <- mtcars_df %>%
     specify(mpg ~ am) %>%
     generate(reps = 100)
 
-  two_props_boot <- mtcars %>%
+  two_props_boot <- mtcars_df %>%
     specify(am ~ vs, success = "1") %>%
     generate(reps = 100)
 
-  slope_boot <- mtcars %>%
+  slope_boot <- mtcars_df %>%
     specify(mpg ~ hp) %>%
     generate(reps = 100)
 
@@ -165,102 +154,102 @@ test_that("auto `type` works (generate)", {
   expect_equal(attr(two_props_boot, "type"), "bootstrap")
   expect_equal(attr(slope_boot, "type"), "bootstrap")
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", mu = 25) %>%
     generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(response = mpg) %>%
     generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(response = mpg) %>% # formula alt: mpg ~ NULL
     hypothesize(null = "point", med = 26) %>%
     generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(response = am, success = "1") %>% # formula alt: am ~ NULL
     hypothesize(null = "point", p = .25) %>%
     generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
     hypothesize(null = "independence") %>%
     generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(cyl ~ NULL) %>% # alt: response = cyl
     hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
     generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
     hypothesize(null = "independence") %>%
     generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
     hypothesize(null = "independence") %>%
     generate(reps = 100, type = "bootstrap"))
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
     hypothesize(null = "independence") %>%
     generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(response = am, success = "1") %>%
     generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(mpg ~ am) %>%
     generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(am ~ vs, success = "1") %>%
     generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars %>%
+  expect_error(mtcars_df %>%
     specify(mpg ~ hp) %>%
     generate(reps = 100, type = "simulate")
   )
 })
 
 test_that("mismatches lead to error", {
-  expect_error(mtcars %>% generate(reps = 10, type = "permute"))
+  expect_error(mtcars_df %>% generate(reps = 10, type = "permute"))
   expect_error(
-    mtcars %>%
+    mtcars_df %>%
       specify(am ~ NULL, success = "1") %>%
       hypothesize(null = "independence", p = c("1" = 0.5)) %>%
       generate(reps = 100, type = "simulate")
   )
   expect_error(
-    mtcars %>%
+    mtcars_df %>%
       specify(cyl ~ NULL) %>% # alt: response = cyl
       hypothesize(
         null = "point", p = c("4" = .5, "6" = .25, "8" = .25)
       ) %>%
       generate(reps = 100, type = "bootstrap"))
   expect_error(
-    mtcars %>% specify(mpg ~ hp) %>% generate(reps = 100, type = "other")
+    mtcars_df %>% specify(mpg ~ hp) %>% generate(reps = 100, type = "other")
   )
 })
 
diff --git a/tests/testthat/test-hypothesize.R b/tests/testthat/test-hypothesize.R
index 22d80e8a..1641c8fa 100644
--- a/tests/testthat/test-hypothesize.R
+++ b/tests/testthat/test-hypothesize.R
@@ -1,54 +1,48 @@
 context("hypothesize")
 
-mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(
-    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
-    carb = factor(carb)
-  )
-
-one_mean <- mtcars %>%
+one_mean <- mtcars_df %>%
   specify(response = mpg) %>% # formula alt: mpg ~ NULL
   hypothesize(null = "point", mu = 25)
 
-one_mean_specify <- mtcars %>%
+one_mean_specify <- mtcars_df %>%
   specify(response = mpg)
 
-one_median <- mtcars %>%
+one_median <- mtcars_df %>%
   specify(response = mpg) %>% # formula alt: mpg ~ NULL
   hypothesize(null = "point", med = 26)
 
-one_prop <- mtcars %>%
+one_prop <- mtcars_df %>%
   specify(response = am, success = "1") %>% # formula alt: am ~ NULL
   hypothesize(null = "point", p = .25)
 
-one_prop_specify <- mtcars %>%
+one_prop_specify <- mtcars_df %>%
   specify(response = am, success = "1")
 
-two_props <- mtcars %>%
+two_props <- mtcars_df %>%
   specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
   hypothesize(null = "independence")
 
-gof_chisq <- mtcars %>%
+gof_chisq <- mtcars_df %>%
   specify(cyl ~ NULL) %>% # alt: response = cyl
   hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25))
 
-indep_chisq <- mtcars %>%
+indep_chisq <- mtcars_df %>%
   specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
   hypothesize(null = "independence")
 
-two_means <- mtcars %>%
+two_means <- mtcars_df %>%
   specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
   hypothesize(null = "independence")
 
-two_medians <- mtcars %>%
+two_medians <- mtcars_df %>%
   specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
   hypothesize(null = "independence")
 
-anova_f <- mtcars %>%
+anova_f <- mtcars_df %>%
   specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
   hypothesize(null = "independence")
 
-slopes <- mtcars %>%
+slopes <- mtcars_df %>%
   specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
   hypothesize(null = "independence")
 
@@ -84,20 +78,20 @@ test_that("hypothesize arguments function", {
 #   )
 
   expect_error(
-    mtcars %>% dplyr::select(vs) %>% hypothesize(null = "point", mu = 1)
+    mtcars_df %>% dplyr::select(vs) %>% hypothesize(null = "point", mu = 1)
   )
 
   expect_error(
-    mtcars %>% specify(response = vs) %>% hypothesize(null = "point", mu = 1)
+    mtcars_df %>% specify(response = vs) %>% hypothesize(null = "point", mu = 1)
   )
 
   expect_error(
-    mtcars %>%
+    mtcars_df %>%
       specify(response = vs, success = "1") %>%
       hypothesize(null = "point", p = 1.1)
   )
   expect_error(
-    mtcars %>%
+    mtcars_df %>%
       specify(response = vs, success = "1") %>%
       hypothesize(null = "point", p = -23)
   )
@@ -111,7 +105,7 @@ test_that("hypothesize arguments function", {
 
   expect_error(mtcars_s %>% hypothesize(null = "point", p = 0.2))
   expect_warning(
-    mtcars %>%
+    mtcars_df %>%
       specify(mpg ~ vs) %>%
       hypothesize(null = "independence", p = 0.5)
   )
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index f572764f..c3b758b3 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -1,17 +1,5 @@
 context("p_value")
 
-iris_tbl <- iris %>%
-  dplyr::mutate(
-    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
-  )
-
-iris_calc <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "diff in props", order = c("large", "small"))
-
 set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
 
diff --git a/tests/testthat/test-specify.R b/tests/testthat/test-specify.R
index 001f14ec..392d5694 100644
--- a/tests/testthat/test-specify.R
+++ b/tests/testthat/test-specify.R
@@ -1,20 +1,14 @@
 context("specify")
 
-mtcars <- as.data.frame(mtcars) %>%
-  dplyr::mutate(
-    cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
-    carb = factor(carb)
-  )
+one_nonshift_mean <- mtcars_df %>% specify(response = mpg)
 
-one_nonshift_mean <- mtcars %>% specify(response = mpg)
+one_nonshift_prop <- mtcars_df %>% specify(response = am, success = "1")
 
-one_nonshift_prop <- mtcars %>% specify(response = am, success = "1")
+two_means_boot <- mtcars_df %>% specify(mpg ~ am)
 
-two_means_boot <- mtcars %>% specify(mpg ~ am)
+two_props_boot <- mtcars_df %>% specify(am ~ vs, success = "1")
 
-two_props_boot <- mtcars %>% specify(am ~ vs, success = "1")
-
-slope_boot <- mtcars %>% specify(mpg ~ hp)
+slope_boot <- mtcars_df %>% specify(mpg ~ hp)
 
 test_that("auto `type` works (specify)", {
   expect_equal(attr(one_nonshift_mean, "type"), "bootstrap")
@@ -27,42 +21,42 @@ test_that("auto `type` works (specify)", {
 test_that("data argument", {
   expect_error(specify(blah ~ cyl))
   expect_error(specify(1:3))
-  expect_is(mtcars, "data.frame")
-  expect_error(specify(mtcars, mtcars$mpg))
+  expect_is(mtcars_df, "data.frame")
+  expect_error(specify(mtcars_df, mtcars_df$mpg))
 })
 
 test_that("response and explanatory arguments", {
-  expect_error(specify(mtcars, response = blah))
-  expect_error(specify(mtcars, response = "blah"))
-  expect_error(specify(mtcars, formula = mpg ~ blah))
+  expect_error(specify(mtcars_df, response = blah))
+  expect_error(specify(mtcars_df, response = "blah"))
+  expect_error(specify(mtcars_df, formula = mpg ~ blah))
   expect_error(specify(blah ~ cyl))
-  expect_error(specify(mtcars_f, blah2 ~ cyl))
-  expect_error(specify(mtcars))
-  expect_error(specify(mtcars, formula = mpg ~ mpg))
-  expect_error(specify(mtcars, formula = mpg ~ "cyl"))
-  expect_silent(specify(mtcars, formula = mpg ~ cyl))
+  expect_error(specify(mtcars_df, blah2 ~ cyl))
+  expect_error(specify(mtcars_df))
+  expect_error(specify(mtcars_df, formula = mpg ~ mpg))
+  expect_error(specify(mtcars_df, formula = mpg ~ "cyl"))
+  expect_silent(specify(mtcars_df, formula = mpg ~ cyl))
 
-  expect_error(specify(mtcars, formula = NULL ~ cyl), "NULL.*response")
+  expect_error(specify(mtcars_df, formula = NULL ~ cyl), "NULL.*response")
 })
 
 test_that("success argument", {
-  expect_error(specify(mtcars, response = vs, success = 1))
-  expect_error(specify(mtcars, response = vs, success = "bogus"))
-  expect_error(specify(mtcars, response = mpg, success = "1"))
-  expect_error(specify(mtcars, response = cyl, success = "4"))
+  expect_error(specify(mtcars_df, response = vs, success = 1))
+  expect_error(specify(mtcars_df, response = vs, success = "bogus"))
+  expect_error(specify(mtcars_df, response = mpg, success = "1"))
+  expect_error(specify(mtcars_df, response = cyl, success = "4"))
   # success not given
-  expect_error(specify(mtcars, response = am))
+  expect_error(specify(mtcars_df, response = am))
 
 })
 
 test_that("sensible output", {
-  expect_equal(ncol(specify(mtcars, formula = mpg ~ NULL)), 1)
-  expect_equal(ncol(specify(mtcars, formula = mpg ~ wt)), 2)
-  expect_equal(class(specify(mtcars, formula = mpg ~ wt))[1], "infer")
+  expect_equal(ncol(specify(mtcars_df, formula = mpg ~ NULL)), 1)
+  expect_equal(ncol(specify(mtcars_df, formula = mpg ~ wt)), 2)
+  expect_equal(class(specify(mtcars_df, formula = mpg ~ wt))[1], "infer")
 })
 
 test_that("formula argument is a formula", {
-  expect_error(specify(mtcars, formula = "vs", success = 1))
+  expect_error(specify(mtcars_df, formula = "vs", success = 1))
 })
 
 test_that("is_complete works", {
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index b69a6b39..222807ac 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -8,12 +8,6 @@ Sepal.Width_resamp <- iris %>%
   generate(reps = 10, type = "bootstrap") %>%
   calculate(stat = "median")
 
-iris_tbl <- tibble::as_tibble(iris) %>%
-  dplyr::mutate(
-    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5"),
-    Sepal.Width.Group = dplyr::if_else(Sepal.Width > 3, "large", "small")
-  )
-
 obs_slope <- lm(Sepal.Length ~ Sepal.Width, data = iris_tbl) %>%
   broom::tidy() %>%
   dplyr::filter(term == "Sepal.Width") %>%

From f684df4aa8cd3ef63e8b82e5d906e990698f1fb4 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 19:03:27 +0300
Subject: [PATCH 18/78] DRY visualization of theoretical distributions.

---
 R/visualize.R | 92 +++++++++++++++------------------------------------
 1 file changed, 27 insertions(+), 65 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 3d71b4be..e12d8154 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -199,20 +199,15 @@ visualize <- function(data, bins = 15, method = "simulation",
   infer_plot
 }
 
-theory_t_plot <- function(deg_freedom, statistic_text = "t",
-                          dens_color = dens_color,
-                          ...) {
-  ggplot(data.frame(x = c(qt(0.001, deg_freedom),
-                          qt(0.999, deg_freedom)))) +
+theory_plot <- function(d_fun, q_fun, args_list, stat_name, dens_color) {
+  x_range <- do.call(q_fun, c(p = list(c(0.001, 0.999)), args_list))
+  
+  ggplot(data.frame(x = x_range)) +
     stat_function(
-      mapping = aes(x),
-      fun = dt,
-      args = list(df = deg_freedom),
-      color = dens_color
+      mapping = aes(x), fun = d_fun, args = args_list, color = dens_color
     ) +
-    ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
-    xlab("") +
-    ylab("")
+    ggtitle(glue_null("Theoretical {stat_name} Null Distribution")) +
+    xlab("") + ylab("")
 }
 
 both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
@@ -245,23 +240,6 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
     ylab("")
 }
 
-theory_anova_plot <- function(deg_freedom_top, deg_freedom_bottom,
-                              statistic_text = "F",
-                              dens_color = dens_color,
-                              ...) {
-  ggplot(data.frame(x = c(qf(0.001, deg_freedom_top, deg_freedom_bottom),
-                          qf(0.999, deg_freedom_top, deg_freedom_bottom)))) +
-    stat_function(
-      mapping = aes(x),
-      fun = df,
-      args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
-      color = dens_color
-    ) +
-    ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
-    xlab("") +
-    ylab("")
-}
-
 both_anova_plot <- function(data, deg_freedom_top,
                             deg_freedom_bottom, statistic_text = "F",
                             dens_color,
@@ -301,14 +279,6 @@ both_anova_plot <- function(data, deg_freedom_top,
     ylab("")
 }
 
-theory_z_plot <- function(statistic_text = "z", dens_color = dens_color, ...) {
-  ggplot(data.frame(x = c(qnorm(0.001), qnorm(0.999)))) +
-    stat_function(mapping = aes(x), fun = dnorm, color = dens_color) +
-    ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
-    xlab("") +
-    ylab("")
-}
-
 both_z_plot <- function(data, statistic_text = "z",
                         dens_color,
                         obs_stat,
@@ -336,23 +306,6 @@ both_z_plot <- function(data, statistic_text = "z",
     ylab("")
 }
 
-theory_chisq_plot <- function(deg_freedom,
-                              statistic_text = "Chi-Square",
-                              dens_color = dens_color,
-                              ...) {
-  ggplot(data.frame(x = c(qchisq(0.001, deg_freedom),
-                          qchisq(0.999, deg_freedom)))) +
-    stat_function(
-      mapping = aes(x),
-      fun = dchisq,
-      args = list(df = deg_freedom),
-      color = dens_color
-    ) +
-    ggtitle(glue_null("Theoretical {statistic_text} Null Distribution")) +
-    xlab("") +
-    ylab("")
-}
-
 both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
                             dens_color,
                             obs_stat,
@@ -563,9 +516,10 @@ visualize_theoretical <- function(data,
       "Two sample t", "Slope with t", "One sample t"
     )
   ) {
-    infer_plot <- theory_t_plot(
-      deg_freedom = attr(data, "distr_param"),
-      statistic_text = "t",
+    infer_plot <- theory_plot(
+      d_fun = dt, q_fun = qt,
+      args_list = list(df = attr(data, "distr_param")),
+      stat_name = "t",
       dens_color = dens_color
     )
   } else if (attr(data, "theory_type") == "ANOVA") {
@@ -575,16 +529,23 @@ visualize_theoretical <- function(data,
       )
     }
 
-    infer_plot <- theory_anova_plot(
-      deg_freedom_top = attr(data, "distr_param"),
-      deg_freedom_bottom = attr(data, "distr_param2"),
-      statistic_text = "F",
+    infer_plot <- theory_plot(
+      d_fun = df, q_fun = qf,
+      args_list = list(
+        df1 = attr(data, "distr_param"), df2 = attr(data, "distr_param2")
+      ),
+      stat_name = "F",
       dens_color = dens_color
     )
   } else if (
     attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
   ) {
-    infer_plot <- theory_z_plot(statistic_text = "z", dens_color = dens_color)
+    infer_plot <- theory_plot(
+      d_fun = dnorm, q_fun = qnorm,
+      args_list = list(),
+      stat_name = "z",
+      dens_color = dens_color
+    )
   } else if (
     attr(data, "theory_type") %in% c(
       "Chi-square test of indep", "Chi-square Goodness of Fit"
@@ -597,9 +558,10 @@ visualize_theoretical <- function(data,
       )
     }
 
-    infer_plot <- theory_chisq_plot(
-      deg_freedom = attr(data, "distr_param"),
-      statistic_text = "Chi-Square",
+    infer_plot <- theory_plot(
+      d_fun = dchisq, q_fun = qchisq,
+      args_list = list(df = attr(data, "distr_param")),
+      stat_name = "Chi-Square",
       dens_color = dens_color
     )
   } # else {

From 738bd0c86007bddcd5b767e97f5bbb4d75d81d2b Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 19:49:33 +0300
Subject: [PATCH 19/78] DRY visualization of "both" distributions.

---
 R/visualize.R | 169 ++++++++++++--------------------------------------
 1 file changed, 40 insertions(+), 129 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index e12d8154..4b795b7e 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -210,16 +210,10 @@ theory_plot <- function(d_fun, q_fun, args_list, stat_name, dens_color) {
     xlab("") + ylab("")
 }
 
-both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
-                        dens_color,
-                        obs_stat,
-                        direction,
-                        bins,
-                        pvalue_fill,
-                        endpoints,
-                        ci_fill,
-                        ...) {
-  infer_t_plot <- shade_density_check(
+both_plot <- function(data, d_fun, args_list, stat_name, stat_label, dens_color,
+                      obs_stat, direction, bins, pvalue_fill, endpoints,
+                      ci_fill, ...) {
+  infer_plot <- shade_density_check(
     data = data,
     obs_stat = obs_stat,
     direction = direction,
@@ -228,117 +222,15 @@ both_t_plot <- function(data = data, deg_freedom, statistic_text = "t",
     pvalue_fill = pvalue_fill,
     ci_fill = ci_fill
   )
-
-  infer_t_plot +
-    stat_function(
-      fun = dt, args = list(df = deg_freedom), color = dens_color
-    ) +
-    ggtitle(glue_null(
-      "Simulation-Based and Theoretical {statistic_text} Null Distributions"
-    )) +
-    xlab("tstat") +
-    ylab("")
-}
-
-both_anova_plot <- function(data, deg_freedom_top,
-                            deg_freedom_bottom, statistic_text = "F",
-                            dens_color,
-                            obs_stat,
-                            direction,
-                            bins,
-                            endpoints,
-                            pvalue_fill,
-                            ci_fill,
-                            ...) {
-  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-    warning_glue(
-      "F usually corresponds to right-tailed tests. Proceed with caution."
-    )
-  }
-
-  infer_anova_plot <- shade_density_check(
-    data = data,
-    obs_stat = obs_stat,
-    direction = direction,
-    bins = bins,
-    endpoints = endpoints,
-    pvalue_fill = pvalue_fill,
-    ci_fill = ci_fill
-  )
-
-  infer_anova_plot <- infer_anova_plot +
-    stat_function(
-      fun = df,
-      args = list(df1 = deg_freedom_top, df2 = deg_freedom_bottom),
-      color = dens_color
-    ) +
-    ggtitle(glue_null(
-      "Simulation-Based and Theoretical {statistic_text} Null Distributions"
-    )) +
-    xlab("Fstat") +
-    ylab("")
-}
-
-both_z_plot <- function(data, statistic_text = "z",
-                        dens_color,
-                        obs_stat,
-                        direction,
-                        pvalue_fill,
-                        bins,
-                        endpoints,
-                        ci_fill,
-                        ...) {
-  infer_z_plot <- shade_density_check(
-    data = data,
-    obs_stat = obs_stat,
-    direction = direction,
-    bins = bins,
-    endpoints = endpoints,
-    pvalue_fill = pvalue_fill,
-    ci_fill = ci_fill
-  )
-  infer_z_plot +
-    stat_function(fun = dnorm, color = dens_color) +
-    ggtitle(glue_null(
-      "Simulation-Based and Theoretical {statistic_text} Null Distributions"
-    )) +
-    xlab("zstat") +
-    ylab("")
-}
-
-both_chisq_plot <- function(data, deg_freedom, statistic_text = "Chi-Square",
-                            dens_color,
-                            obs_stat,
-                            direction,
-                            bins,
-                            endpoints,
-                            pvalue_fill = pvalue_fill,
-                            ci_fill = ci_fill,
-                            ...) {
-  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-    warning_glue("Chi-square usually corresponds to right-tailed tests. ",
-                 "Proceed with caution.")
-  }
-
-  infer_chisq_plot <- shade_density_check(
-    data = data,
-    obs_stat = obs_stat,
-    direction = direction,
-    bins = bins,
-    endpoints = endpoints,
-    pvalue_fill = pvalue_fill,
-    ci_fill = ci_fill
-  )
-
-  infer_chisq_plot +
+  
+  infer_plot +
     stat_function(
-      fun = dchisq, args = list(df = deg_freedom), color = dens_color
+      fun = d_fun, args = args_list, color = dens_color
     ) +
     ggtitle(glue_null(
-      "Simulation-Based and Theoretical {statistic_text} Null Distributions"
+      "Simulation-Based and Theoretical {stat_name} Null Distributions"
     )) +
-    xlab("chisqstat") +
-    ylab("")
+    xlab(stat_label) + ylab("")
 }
 
 shade_density_check <- function(data,
@@ -655,10 +547,11 @@ visualize_both <- function(data, bins,
   }
 
   if (attr(data, "theory_type") %in% c("Two sample t", "Slope with t")) {
-    infer_plot <- both_t_plot(
+    infer_plot <- both_plot(
       data = data,
-      deg_freedom = attr(data, "distr_param"),
-      statistic_text = "t",
+      d_fun = dt,
+      args_list = list(df = attr(data, "distr_param")),
+      stat_name = "t", stat_label = "tstat",
       dens_color = dens_color,
       bins = bins,
       direction = direction,
@@ -668,11 +561,19 @@ visualize_both <- function(data, bins,
       ci_fill = ci_fill
     )
   } else if (attr(data, "theory_type") == "ANOVA") {
-    infer_plot <- both_anova_plot(
+    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
+      warning_glue(
+        "F usually corresponds to right-tailed tests. Proceed with caution."
+      )
+    }
+    
+    infer_plot <- both_plot(
       data = data,
-      deg_freedom_top = attr(data, "distr_param"),
-      deg_freedom_bottom = attr(data, "distr_param2"),
-      statistic_text = "F",
+      d_fun = df,
+      args_list = list(
+        df1 = attr(data, "distr_param"), df2 = attr(data, "distr_param2")
+      ),
+      stat_name = "F", stat_label = "Fstat",
       dens_color = dens_color,
       bins = bins,
       direction = direction,
@@ -684,9 +585,11 @@ visualize_both <- function(data, bins,
   } else if (
     attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
   ) {
-    infer_plot <- both_z_plot(
+    infer_plot <- both_plot(
       data = data,
-      statistic_text = "z",
+      d_fun = dnorm,
+      args_list = list(),
+      stat_name = "z", stat_label = "zstat",
       dens_color = dens_color,
       bins = bins,
       direction = direction,
@@ -700,10 +603,18 @@ visualize_both <- function(data, bins,
       "Chi-square test of indep", "Chi-square Goodness of Fit"
     )
   ) {
-    infer_plot <- both_chisq_plot(
+    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
+      warning_glue(
+        "Chi-square usually corresponds to right-tailed tests. ",
+        "Proceed with caution."
+      )
+    }
+    
+    infer_plot <- both_plot(
       data = data,
-      deg_freedom = attr(data, "distr_param"),
-      statistic_text = "Chi-Square",
+      d_fun = dchisq,
+      args_list = list(df = attr(data, "distr_param")),
+      stat_name = "Chi-Square", stat_label = "chisqstat",
       dens_color = dens_color,
       bins = bins,
       direction = direction,

From 80f016b161e65c6e9ab19a25d6248e9ad6d8375d Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 20:00:25 +0300
Subject: [PATCH 20/78] DRY warning in visualization about right-tailed tests.

---
 R/visualize.R | 37 +++++++++++++++----------------------
 1 file changed, 15 insertions(+), 22 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 4b795b7e..90976451 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -415,11 +415,7 @@ visualize_theoretical <- function(data,
       dens_color = dens_color
     )
   } else if (attr(data, "theory_type") == "ANOVA") {
-    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-      warning_glue(
-        "F usually corresponds to right-tailed tests. Proceed with caution."
-      )
-    }
+    warn_right_tail_test(direction, "F")
 
     infer_plot <- theory_plot(
       d_fun = df, q_fun = qf,
@@ -443,12 +439,7 @@ visualize_theoretical <- function(data,
       "Chi-square test of indep", "Chi-square Goodness of Fit"
     )
   ) {
-    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-      warning_glue(
-        "Chi-square usually corresponds to right-tailed tests. ",
-        "Proceed with caution."
-      )
-    }
+    warn_right_tail_test(direction, "Chi-square")
 
     infer_plot <- theory_plot(
       d_fun = dchisq, q_fun = qchisq,
@@ -561,11 +552,7 @@ visualize_both <- function(data, bins,
       ci_fill = ci_fill
     )
   } else if (attr(data, "theory_type") == "ANOVA") {
-    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-      warning_glue(
-        "F usually corresponds to right-tailed tests. Proceed with caution."
-      )
-    }
+    warn_right_tail_test(direction, "F")
     
     infer_plot <- both_plot(
       data = data,
@@ -603,12 +590,7 @@ visualize_both <- function(data, bins,
       "Chi-square test of indep", "Chi-square Goodness of Fit"
     )
   ) {
-    if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
-      warning_glue(
-        "Chi-square usually corresponds to right-tailed tests. ",
-        "Proceed with caution."
-      )
-    }
+    warn_right_tail_test(direction, "Chi-square")
     
     infer_plot <- both_plot(
       data = data,
@@ -633,3 +615,14 @@ visualize_both <- function(data, bins,
 get_percentile <- function(vector, observation) {
   stats::ecdf(vector)(observation)
 }
+
+warn_right_tail_test <- function(direction, stat_name) {
+  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
+    warning_glue(
+      "{stat_name} usually corresponds to right-tailed tests. ",
+      "Proceed with caution."
+    )
+  }
+  
+  TRUE
+}
\ No newline at end of file

From 2cc4fe78e38b2b2e4b83a4ed54c6c14202095bf0 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 21:47:30 +0300
Subject: [PATCH 21/78] DRY visualization of p-value shades.

This also implements fixed shade transparency for all types of plots. Earlier it was fixed for "theoretical" and varied for "simulation" and "both".
---
 R/visualize.R | 177 +++++++++++++++++---------------------------------
 1 file changed, 59 insertions(+), 118 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 90976451..1846a479 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -267,80 +267,31 @@ shade_density_check <- function(data,
           geom_histogram(bins = bins, color = "white", ...)
       }
 
-      if (direction %in% c("less", "left")) {
+      if (direction %in% c("less", "left", "greater", "right")) {
         gg_plot <- gg_plot +
-          geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-            ...
-          )
-      }
-      if (direction %in% c("greater", "right")) {
-        gg_plot <- gg_plot +
-          geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-            ...
-          )
+          geom_tail(direction, obs_stat, pvalue_fill)
       }
-
-      if (
-        (direction %in% c("two_sided", "both")) &&
-        (obs_stat >= stats::median(data$stat))
-      ) {
+      
+      if (direction %in% c("two_sided", "both")) {
         gg_plot <- gg_plot +
-          geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            mapping = aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-            ...
-          ) +
-          geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            mapping = aes(
-              xmin = -Inf,
-              xmax = stats::quantile(
-                data$stat, probs = 1 - get_percentile(data$stat, obs_stat)
-              ),
-              ymin = 0,
-              ymax = Inf,
-              ...
-            )
+          geom_both_tails(
+            border_1 = obs_stat,
+            border_2 = mirror_obs_stat(data$stat, obs_stat),
+            fill = pvalue_fill
           )
       }
 
-      if (
-        (direction %in% c("two_sided", "both")) &&
-        (obs_stat < stats::median(data$stat))
-      ) {
+      if (direction == "between") {
         gg_plot <- gg_plot +
           geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            mapping = aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
+            data = data.frame(endpoints[1]),
+            fill = ci_fill, alpha = 0.6,
+            aes(xmin = endpoints[1], xmax = endpoints[2], ymin = 0, ymax = Inf),
+            inherit.aes = FALSE,
             ...
-          ) +
-          geom_rect(
-            fill = pvalue_fill, alpha = 0.01,
-            mapping = aes(
-              xmin = stats::quantile(
-                data$stat, probs = 1 - get_percentile(data$stat, obs_stat)
-              ),
-              xmax = Inf,
-              ymin = 0,
-              ymax = Inf,
-              ...
-            )
           )
       }
     }
-
-    if (direction == "between") {
-      gg_plot <- gg_plot +
-        geom_rect(
-          fill = ci_fill, alpha = 0.01,
-          aes(xmin = endpoints[1], xmax = endpoints[2], ymin = 0, ymax = Inf),
-          ...
-        )
-    }
   }
   gg_plot
 }
@@ -453,61 +404,17 @@ visualize_theoretical <- function(data,
     # )
   # }
 
-  # Move into its own function
-  if (!is.null(obs_stat)) {
-    if (!is.null(direction)) {
-      if (direction %in% c("less", "left")) {
-        infer_plot <- infer_plot +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-            ...
-          )
-      }
-      if (direction %in% c("greater", "right")) {
-        infer_plot <- infer_plot +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-            ...
-          )
-      }
-
-      # Assuming two-tailed shading will only happen with theoretical
-      # distributions centered at 0
-      if ((direction %in% c("two_sided", "both")) && (obs_stat >= 0)) {
-        infer_plot <- infer_plot +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-            ...
-          ) +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = -Inf, xmax = -obs_stat, ymin = 0, ymax = Inf),
-            ...
-          )
-      }
-
-      if ((direction %in% c("two_sided", "both")) && (obs_stat < 0)) {
-        infer_plot <- infer_plot +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = -Inf, xmax = obs_stat, ymin = 0, ymax = Inf),
-            ...
-          ) +
-          geom_rect(
-            data = data.frame(obs_stat),
-            fill = pvalue_fill, alpha = 0.6,
-            aes(xmin = -obs_stat, xmax = Inf, ymin = 0, ymax = Inf),
-            ...
-          )
-      }
+  # Plot tails
+  if (!is.null(obs_stat) && !is.null(direction)) {
+    if (direction %in% c("less", "left", "greater", "right")) {
+      infer_plot <- infer_plot +
+        geom_tail(direction, obs_stat, pvalue_fill, ...)
+    }
+    # Assuming two-tailed shading will only happen with theoretical
+    # distributions centered at 0
+    if (direction %in% c("two_sided", "both")) {
+      infer_plot <- infer_plot +
+        geom_both_tails(obs_stat, -obs_stat, pvalue_fill, ...)
     }
   }
 
@@ -616,6 +523,12 @@ get_percentile <- function(vector, observation) {
   stats::ecdf(vector)(observation)
 }
 
+mirror_obs_stat <- function(vector, observation) {
+  obs_percentile <- get_percentile(vector, observation)
+  
+  stats::quantile(vector, probs = 1 - obs_percentile)
+}
+
 warn_right_tail_test <- function(direction, stat_name) {
   if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
     warning_glue(
@@ -625,4 +538,32 @@ warn_right_tail_test <- function(direction, stat_name) {
   }
   
   TRUE
-}
\ No newline at end of file
+}
+
+geom_tail <- function(dir, border, fill, ...) {
+  if (dir %in% c("less", "left")) {
+    x_range <- c(-Inf, border)
+  } else if (dir %in% c("greater", "right")) {
+    x_range <- c(border, Inf)
+  }
+  
+  list(
+    geom_rect(
+      data = data.frame(border),
+      aes(xmin = x_range[1], xmax = x_range[2], ymin = 0, ymax = Inf),
+      fill = fill, alpha = 0.6,
+      inherit.aes = FALSE,
+      ...
+    )
+  )
+}
+
+geom_both_tails <- function(border_1, border_2, fill, ...) {
+  left_border <- min(border_1, border_2)
+  right_border <- max(border_1, border_2)
+  
+  c(
+    geom_tail("left", left_border, fill, ...),
+    geom_tail("right", right_border, fill, ...)
+  )
+}

From f592b7f58c73c750624c21fd2f3fbef4cc516d19 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 22:14:19 +0300
Subject: [PATCH 22/78] DRY and standardize theory type names.

Also this renames "Chi-square" into "Chi-Square" in warnings about right-tailed tests.
---
 R/visualize.R                   | 54 +++++++++++++++++----------------
 tests/testthat/test-visualize.R |  9 ++++++
 2 files changed, 37 insertions(+), 26 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 1846a479..4beb1ea2 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -353,19 +353,17 @@ visualize_theoretical <- function(data,
       "different scales. Displaying only the theoretical distribution."
     )
   }
+  
+  theory_type <- short_theory_type(data)
 
-  if (
-    attr(data, "theory_type") %in% c(
-      "Two sample t", "Slope with t", "One sample t"
-    )
-  ) {
+  if (theory_type == "t") {
     infer_plot <- theory_plot(
       d_fun = dt, q_fun = qt,
       args_list = list(df = attr(data, "distr_param")),
       stat_name = "t",
       dens_color = dens_color
     )
-  } else if (attr(data, "theory_type") == "ANOVA") {
+  } else if (theory_type == "F") {
     warn_right_tail_test(direction, "F")
 
     infer_plot <- theory_plot(
@@ -376,21 +374,15 @@ visualize_theoretical <- function(data,
       stat_name = "F",
       dens_color = dens_color
     )
-  } else if (
-    attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
-  ) {
+  } else if (theory_type == "z") {
     infer_plot <- theory_plot(
       d_fun = dnorm, q_fun = qnorm,
       args_list = list(),
       stat_name = "z",
       dens_color = dens_color
     )
-  } else if (
-    attr(data, "theory_type") %in% c(
-      "Chi-square test of indep", "Chi-square Goodness of Fit"
-    )
-  ) {
-    warn_right_tail_test(direction, "Chi-square")
+  } else if (theory_type == "Chi-Square") {
+    warn_right_tail_test(direction, "Chi-Square")
 
     infer_plot <- theory_plot(
       d_fun = dchisq, q_fun = qchisq,
@@ -444,7 +436,9 @@ visualize_both <- function(data, bins,
     )
   }
 
-  if (attr(data, "theory_type") %in% c("Two sample t", "Slope with t")) {
+  theory_type <- short_theory_type(data)
+  
+  if (theory_type == "t") {
     infer_plot <- both_plot(
       data = data,
       d_fun = dt,
@@ -458,7 +452,7 @@ visualize_both <- function(data, bins,
       endpoints = endpoints,
       ci_fill = ci_fill
     )
-  } else if (attr(data, "theory_type") == "ANOVA") {
+  } else if (theory_type == "F") {
     warn_right_tail_test(direction, "F")
     
     infer_plot <- both_plot(
@@ -476,9 +470,7 @@ visualize_both <- function(data, bins,
       endpoints = endpoints,
       ci_fill = ci_fill
     )
-  } else if (
-    attr(data, "theory_type") %in% c("One sample prop z", "Two sample props z")
-  ) {
+  } else if (theory_type == "z") {
     infer_plot <- both_plot(
       data = data,
       d_fun = dnorm,
@@ -492,12 +484,8 @@ visualize_both <- function(data, bins,
       endpoints = endpoints,
       ci_fill = ci_fill
     )
-  } else if (
-    attr(data, "theory_type") %in% c(
-      "Chi-square test of indep", "Chi-square Goodness of Fit"
-    )
-  ) {
-    warn_right_tail_test(direction, "Chi-square")
+  } else if (theory_type == "Chi-Square") {
+    warn_right_tail_test(direction, "Chi-Square")
     
     infer_plot <- both_plot(
       data = data,
@@ -529,6 +517,20 @@ mirror_obs_stat <- function(vector, observation) {
   stats::quantile(vector, probs = 1 - obs_percentile)
 }
 
+short_theory_type <- function(x) {
+  theory_attr <- attr(x, "theory_type")
+  theory_types <- list(
+    t = c("Two sample t", "Slope with t", "One sample t"),
+    `F` = "ANOVA",
+    z = c("One sample prop z", "Two sample props z"),
+    `Chi-Square` = c("Chi-square test of indep", "Chi-square Goodness of Fit")
+  )
+  
+  is_type <- vapply(theory_types, function(x) {theory_attr %in% x}, logical(1))
+  
+  names(theory_types)[which(is_type)[1]]
+}
+
 warn_right_tail_test <- function(direction, stat_name) {
   if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
     warning_glue(
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 222807ac..20dfd454 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -138,6 +138,15 @@ test_that("visualize basic tests", {
       calculate(stat = "t", order = c("small", "large")) %>%
       visualize(method = "theoretical", direction = "left", obs_stat = -obs_t)
   )
+  
+  expect_warning(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      hypothesize(null = "point", mu = 1) %>%
+      generate(reps = 100) %>%
+      calculate(stat = "t") %>%
+      visualize(method = "both")
+  )
 
   expect_warning(
     iris_tbl %>%

From 421e91f594b0fc323e36b18825cd8af3212ea098 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 22:14:43 +0300
Subject: [PATCH 23/78] Fix style in 'test-visualize.R'.

---
 tests/testthat/test-visualize.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 20dfd454..69f49586 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -206,7 +206,8 @@ test_that("visualize basic tests", {
       ) %>%
       generate(reps = 100, type = "simulate") %>%
       calculate(stat = "Chisq") %>%
-      visualize(method = "both"))
+      visualize(method = "both")
+  )
 
   # traditional instead of theoretical
   expect_error(

From a675cf5dc75c0042a447863906b686a144073f02 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 12 Aug 2018 22:18:54 +0300
Subject: [PATCH 24/78] Update 'NEWS.md'.

---
 NEWS.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 00d4c356..b4caca49 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -5,6 +5,9 @@
 - Remove help page for internal `set_params()` (#165).
 - Fully use {tibble} (#166).
 - Fix `calculate()` to not depend on order of `p` for `type = "simulate"` (#122).
+- Reduce code duplication (#173).
+- Make transparancy in `visualize()` to not depend on method and data volume.
+- Make `visualize()` work for "One sample t" theoretical type with `method = "both"`.
 
 # infer 0.3.1
 

From 8ea69e60ec30681afcb41c0d4223a9af6e708cf0 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sat, 18 Aug 2018 11:24:02 +0300
Subject: [PATCH 25/78] Add `stat = "sum"` option to `calculate()`.

---
 R/calculate.R                   | 14 ++++++++------
 R/utils.R                       |  4 ++--
 man/calculate.Rd                | 11 +++++------
 tests/testthat/test-calculate.R | 19 +++++++++++++++++++
 4 files changed, 34 insertions(+), 14 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 328801c5..2343ce15 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -3,9 +3,9 @@
 #' @param x The output from [generate()] for computation-based inference or the
 #'   output from [hypothesize()] piped in to here for theory-based inference.
 #' @param stat A string giving the type of the statistic to calculate. Current
-#'   options include `"mean"`, `"median"`, `"sd"`, `"prop"`, `"diff in means"`,
-#'   `"diff in medians"`, `"diff in props"`, `"Chisq"`, `"F"`, `"t"`, `"z"`,
-#'   `"slope"`, and `"correlation"`.
+#'   options include `"mean"`, `"median"`, `"sum"`, `"sd"`, `"prop"`, `"diff in
+#'   means"`, `"diff in medians"`, `"diff in props"`, `"Chisq"`, `"F"`, `"t"`,
+#'   `"z"`, `"slope"`, and `"correlation"`.
 #' @param order A string vector of specifying the order in which the levels of
 #'   the explanatory variable should be ordered for subtraction, where `order =
 #'   c("first", "second")` means `("first" - "second")` Needed for inference on
@@ -29,7 +29,7 @@
 #' @export
 calculate <- function(x,
                       stat = c(
-                        "mean", "median", "sd", "prop", "diff in means",
+                        "mean", "median", "sum", "sd", "prop", "diff in means",
                         "diff in medians", "diff in props", "Chisq", "F",
                         "slope", "correlation", "t", "z"
                       ),
@@ -53,8 +53,8 @@ calculate <- function(x,
       x$replicate <- 1L
     } else if (
       stat %in% c(
-        "mean", "median", "sd", "prop", "diff in means", "diff in medians",
-        "diff in props", "slope", "correlation"
+        "mean", "median", "sum", "sd", "prop", "diff in means",
+        "diff in medians", "diff in props", "slope", "correlation"
       )
     ) {
       stop_glue(
@@ -140,6 +140,8 @@ calc_impl.mean <- calc_impl_one_f(mean)
 
 calc_impl.median <- calc_impl_one_f(stats::median)
 
+calc_impl.sum <- calc_impl_one_f(sum)
+
 calc_impl.sd <- calc_impl_one_f(stats::sd)
 
 calc_impl.prop <- function(type, x, order, ...) {
diff --git a/R/utils.R b/R/utils.R
index 2bee15d5..af55c9f4 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -129,7 +129,7 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
   # but that's not as helpful to beginners with the cryptic error msg
   if (
     !stat %in% c(
-      "mean", "median", "sd", "prop", "diff in means", "diff in medians",
+      "mean", "median", "sum", "sd", "prop", "diff in means", "diff in medians",
       "diff in props", "Chisq", "F", "slope", "correlation", "t", "z"
     )
   ) {
@@ -166,7 +166,7 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
 }
 
 check_for_numeric_stat <- function(x, stat) {
-  if (stat %in% c("mean", "median", "sd")) {
+  if (stat %in% c("mean", "median", "sum", "sd")) {
     col <- base::setdiff(names(x), "replicate")
 
     if (!is.numeric(x[[as.character(col)]])) {
diff --git a/man/calculate.Rd b/man/calculate.Rd
index 05947bb8..4b81f413 100755
--- a/man/calculate.Rd
+++ b/man/calculate.Rd
@@ -4,18 +4,17 @@
 \alias{calculate}
 \title{Calculate summary statistics}
 \usage{
-calculate(x, stat = c("mean", "median", "sd", "prop", "diff in means",
-  "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation",
-  "t", "z"), order = NULL, ...)
+calculate(x, stat = c("mean", "median", "sum", "sd", "prop",
+  "diff in means", "diff in medians", "diff in props", "Chisq", "F",
+  "slope", "correlation", "t", "z"), order = NULL, ...)
 }
 \arguments{
 \item{x}{The output from \code{\link[=generate]{generate()}} for computation-based inference or the
 output from \code{\link[=hypothesize]{hypothesize()}} piped in to here for theory-based inference.}
 
 \item{stat}{A string giving the type of the statistic to calculate. Current
-options include \code{"mean"}, \code{"median"}, \code{"sd"}, \code{"prop"}, \code{"diff in means"},
-\code{"diff in medians"}, \code{"diff in props"}, \code{"Chisq"}, \code{"F"}, \code{"t"}, \code{"z"},
-\code{"slope"}, and \code{"correlation"}.}
+options include \code{"mean"}, \code{"median"}, \code{"sum"}, \code{"sd"}, \code{"prop"}, \code{"diff in means"}, \code{"diff in medians"}, \code{"diff in props"}, \code{"Chisq"}, \code{"F"}, \code{"t"},
+\code{"z"}, \code{"slope"}, and \code{"correlation"}.}
 
 \item{order}{A string vector of specifying the order in which the levels of
 the explanatory variable should be ordered for subtraction, where \code{order = c("first", "second")} means \code{("first" - "second")} Needed for inference on
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index daefde6c..1c017b52 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -428,3 +428,22 @@ test_that("calc_impl_one_f works", {
 test_that("calc_impl_diff_f works", {
   expect_true(is.function(calc_impl_diff_f(mean)))
 })
+
+test_that("calc_impl.sum works", {
+  expect_equal(
+    iris_tbl %>%
+      specify(Petal.Width ~ NULL) %>%
+      calculate(stat = "sum") %>%
+      `[[`(1),
+    sum(iris_tbl$Petal.Width)
+  )
+  
+  gen_iris16 <- iris_tbl %>%
+    specify(Petal.Width ~ NULL) %>%
+    generate(10)
+  
+  expect_equal(
+    gen_iris16 %>% calculate(stat = "sum"),
+    gen_iris16 %>% dplyr::summarise(stat = sum(Petal.Width))
+  )
+})
\ No newline at end of file

From f959c08983f300a1a647427397b95e4f732b4d53 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sat, 18 Aug 2018 12:20:00 +0300
Subject: [PATCH 26/78] Add `stat = "count"` option to `calculate()`.

---
 R/calculate.R                   | 75 ++++++++++++++++++---------------
 R/utils.R                       |  5 ++-
 man/calculate.Rd                |  7 +--
 tests/testthat/test-calculate.R | 42 ++++++++++++++----
 4 files changed, 82 insertions(+), 47 deletions(-)

diff --git a/R/calculate.R b/R/calculate.R
index 2343ce15..5b1aae43 100755
--- a/R/calculate.R
+++ b/R/calculate.R
@@ -3,9 +3,9 @@
 #' @param x The output from [generate()] for computation-based inference or the
 #'   output from [hypothesize()] piped in to here for theory-based inference.
 #' @param stat A string giving the type of the statistic to calculate. Current
-#'   options include `"mean"`, `"median"`, `"sum"`, `"sd"`, `"prop"`, `"diff in
-#'   means"`, `"diff in medians"`, `"diff in props"`, `"Chisq"`, `"F"`, `"t"`,
-#'   `"z"`, `"slope"`, and `"correlation"`.
+#'   options include `"mean"`, `"median"`, `"sum"`, `"sd"`, `"prop"`, `"count"`,
+#'   `"diff in means"`, `"diff in medians"`, `"diff in props"`, `"Chisq"`,
+#'   `"F"`, `"t"`, `"z"`, `"slope"`, and `"correlation"`.
 #' @param order A string vector of specifying the order in which the levels of
 #'   the explanatory variable should be ordered for subtraction, where `order =
 #'   c("first", "second")` means `("first" - "second")` Needed for inference on
@@ -29,9 +29,9 @@
 #' @export
 calculate <- function(x,
                       stat = c(
-                        "mean", "median", "sum", "sd", "prop", "diff in means",
-                        "diff in medians", "diff in props", "Chisq", "F",
-                        "slope", "correlation", "t", "z"
+                        "mean", "median", "sum", "sd", "prop", "count",
+                        "diff in means", "diff in medians", "diff in props",
+                        "Chisq", "F", "slope", "correlation", "t", "z"
                       ),
                       order = NULL,
                       ...) {
@@ -53,7 +53,7 @@ calculate <- function(x,
       x$replicate <- 1L
     } else if (
       stat %in% c(
-        "mean", "median", "sum", "sd", "prop", "diff in means",
+        "mean", "median", "sum", "sd", "prop", "count", "diff in means",
         "diff in medians", "diff in props", "slope", "correlation"
       )
     ) {
@@ -62,7 +62,7 @@ calculate <- function(x,
         "implemented) for `stat` = \"{stat}\". Are you missing ",
         "a `generate()` step?"
       )
-    } else if (!(stat %in% c("Chisq", "prop"))) {
+    } else if (!(stat %in% c("Chisq", "prop", "count"))) {
       # From `hypothesize()` to `calculate()`
       # Catch-all if generate was not called
 #      warning_glue("You unexpectantly went from `hypothesize()` to ",
@@ -144,35 +144,42 @@ calc_impl.sum <- calc_impl_one_f(sum)
 
 calc_impl.sd <- calc_impl_one_f(stats::sd)
 
-calc_impl.prop <- function(type, x, order, ...) {
-  col <- base::setdiff(names(x), "replicate")
-
-  ## No longer needed with implementation of `check_point_params()`
-  # if (!is.factor(x[[col]])) {
-  #   stop_glue(
-  #     "Calculating a {stat} here is not appropriate since the `{col}` ",
-  #     "variable is not a factor."
-  #   )
-  # }
-
-  if (is_nuat(x, "success")) {
-    stop_glue(
-      'To calculate a proportion, the `"success"` argument must be provided ',
-      'in `specify()`.'
-    )
-  }
-
-  success <- attr(x, "success")
-  x %>%
-    dplyr::group_by(replicate) %>%
-    dplyr::summarize(
-      stat = mean(
-        # rlang::eval_tidy(col) == rlang::eval_tidy(success), ...
-        !!sym(col) == success, ...
+calc_impl_success_f <- function(f, output_name) {
+  function(type, x, order, ...) {
+    col <- base::setdiff(names(x), "replicate")
+    
+    ## No longer needed with implementation of `check_point_params()`
+    # if (!is.factor(x[[col]])) {
+    #   stop_glue(
+    #     "Calculating a {stat} here is not appropriate since the `{col}` ",
+    #     "variable is not a factor."
+    #   )
+    # }
+    
+    if (is_nuat(x, "success")) {
+      stop_glue(
+        'To calculate a {output_name}, the `"success"` argument must be ',
+        'provided in `specify()`.'
       )
-    )
+    }
+    
+    success <- attr(x, "success")
+    x %>%
+      dplyr::group_by(replicate) %>%
+      dplyr::summarize(stat = f(!!sym(col), success))
+  }
 }
 
+calc_impl.prop <- calc_impl_success_f(
+  f = function(response, success, ...) {mean(response == success, ...)},
+  output_name = "proportion"
+)
+
+calc_impl.count <- calc_impl_success_f(
+  f = function(response, success, ...) {sum(response == success, ...)},
+  output_name = "count"
+)
+
 calc_impl.F <- function(type, x, order, ...) {
   x %>%
     dplyr::summarize(
diff --git a/R/utils.R b/R/utils.R
index af55c9f4..9c7d0e96 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -129,8 +129,9 @@ check_args_and_attr <- function(x, explanatory_variable, response_variable,
   # but that's not as helpful to beginners with the cryptic error msg
   if (
     !stat %in% c(
-      "mean", "median", "sum", "sd", "prop", "diff in means", "diff in medians",
-      "diff in props", "Chisq", "F", "slope", "correlation", "t", "z"
+      "mean", "median", "sum", "sd", "prop", "count", "diff in means",
+      "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation",
+      "t", "z"
     )
   ) {
     stop_glue(
diff --git a/man/calculate.Rd b/man/calculate.Rd
index 4b81f413..6861eb6e 100755
--- a/man/calculate.Rd
+++ b/man/calculate.Rd
@@ -4,7 +4,7 @@
 \alias{calculate}
 \title{Calculate summary statistics}
 \usage{
-calculate(x, stat = c("mean", "median", "sum", "sd", "prop",
+calculate(x, stat = c("mean", "median", "sum", "sd", "prop", "count",
   "diff in means", "diff in medians", "diff in props", "Chisq", "F",
   "slope", "correlation", "t", "z"), order = NULL, ...)
 }
@@ -13,8 +13,9 @@ calculate(x, stat = c("mean", "median", "sum", "sd", "prop",
 output from \code{\link[=hypothesize]{hypothesize()}} piped in to here for theory-based inference.}
 
 \item{stat}{A string giving the type of the statistic to calculate. Current
-options include \code{"mean"}, \code{"median"}, \code{"sum"}, \code{"sd"}, \code{"prop"}, \code{"diff in means"}, \code{"diff in medians"}, \code{"diff in props"}, \code{"Chisq"}, \code{"F"}, \code{"t"},
-\code{"z"}, \code{"slope"}, and \code{"correlation"}.}
+options include \code{"mean"}, \code{"median"}, \code{"sum"}, \code{"sd"}, \code{"prop"}, \code{"count"},
+\code{"diff in means"}, \code{"diff in medians"}, \code{"diff in props"}, \code{"Chisq"},
+\code{"F"}, \code{"t"}, \code{"z"}, \code{"slope"}, and \code{"correlation"}.}
 
 \item{order}{A string vector of specifying the order in which the levels of
 the explanatory variable should be ordered for subtraction, where \code{order = c("first", "second")} means \code{("first" - "second")} Needed for inference on
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index 1c017b52..b03a2453 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -275,14 +275,15 @@ test_that("`order` is working", {
   expect_error(calculate(gen_iris11, stat = "diff in means"))
 })
 
+gen_iris12 <- iris %>%
+  dplyr::mutate(
+    Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
+  ) %>%
+  specify(Sepal.Length.Group ~ NULL, success = ">5") %>%
+  hypothesize(null = "point", p = 0.3) %>%
+  generate(reps = 10, type = "simulate")
+
 test_that('success is working for stat = "prop"', {
-  gen_iris12 <- iris %>%
-    dplyr::mutate(
-      Sepal.Length.Group = dplyr::if_else(Sepal.Length > 5, ">5", "<=5")
-    ) %>%
-    specify(Sepal.Length.Group ~ NULL, success = ">5") %>%
-    hypothesize(null = "point", p = 0.3) %>%
-    generate(reps = 10, type = "simulate")
   expect_silent(gen_iris12 %>% calculate(stat = "prop"))
   expect_silent(gen_iris12 %>% calculate(stat = "z"))
 })
@@ -372,6 +373,7 @@ test_that("specify done before calculate", {
   iris_prop <- iris_tbl %>% dplyr::select(Sepal.Length.Group)
   attr(iris_prop, "response") <- "Sepal.Length.Group"
   expect_error(calculate(iris_prop, stat = "prop"))
+  expect_error(calculate(iris_prop, stat = "count"))
 })
 
 test_that("chisq GoF has params specified for observed stat", {
@@ -446,4 +448,28 @@ test_that("calc_impl.sum works", {
     gen_iris16 %>% calculate(stat = "sum"),
     gen_iris16 %>% dplyr::summarise(stat = sum(Petal.Width))
   )
-})
\ No newline at end of file
+})
+
+test_that("calc_impl_success_f works", {
+  expect_true(
+    is.function(calc_impl_success_f(
+      f = function(response, success, ...) {mean(response == success, ...)},
+      output_name = "proportion"
+    ))
+  )
+})
+
+test_that("calc_impl.count works", {
+  expect_equal(
+    iris_tbl %>%
+      specify(Sepal.Length.Group ~ NULL, success = ">5") %>%
+      calculate(stat = "count") %>%
+      `[[`(1),
+    sum(iris_tbl$Sepal.Length.Group == ">5")
+  )
+  
+  expect_equal(
+    gen_iris12 %>% calculate(stat = "count"),
+    gen_iris12 %>% dplyr::summarise(stat = sum(Sepal.Length.Group == ">5"))
+  )
+})

From 99a57c0c5fa6a0ff05f962ee07a5bb46fb6002b1 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sat, 18 Aug 2018 12:25:29 +0300
Subject: [PATCH 27/78] Update 'NEWS.md'.

---
 NEWS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/NEWS.md b/NEWS.md
index b4caca49..2901c160 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,6 +8,7 @@
 - Reduce code duplication (#173).
 - Make transparancy in `visualize()` to not depend on method and data volume.
 - Make `visualize()` work for "One sample t" theoretical type with `method = "both"`.
+- Add `stat = "sum"` and `stat = "count"` options to `calculate()` (#50).
 
 # infer 0.3.1
 

From c5258949e3164029d1b6b1d0a91833f44cdf580f Mon Sep 17 00:00:00 2001
From: rudeboybert <albert.ys.kim@gmail.com>
Date: Thu, 23 Aug 2018 09:10:02 -0400
Subject: [PATCH 28/78] glue package version requirement

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e61ed7b6..a894ff0a 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -26,7 +26,7 @@ Imports:
     rlang (>= 0.2.0),
     ggplot2,
     magrittr,
-    glue
+    glue (>= 1.3.0)
 Depends:
     R (>= 3.1.2)
 Suggests: 

From 8470f851f0744bad68201be8b42d08924eac3b86 Mon Sep 17 00:00:00 2001
From: rudeboybert <albert.ys.kim@gmail.com>
Date: Thu, 23 Aug 2018 10:28:24 -0400
Subject: [PATCH 29/78] Added glue::glue_collpase to NAMESPACE

---
 NAMESPACE         | 1 +
 R/print_methods.R | 1 +
 2 files changed, 2 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index a6bf6e27..ebc46efd 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -37,6 +37,7 @@ importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,stat_function)
 importFrom(ggplot2,xlab)
 importFrom(ggplot2,ylab)
+importFrom(glue,glue_collapse)
 importFrom(magrittr,"%>%")
 importFrom(methods,hasArg)
 importFrom(rlang,"!!")
diff --git a/R/print_methods.R b/R/print_methods.R
index a1cf028c..2ccffc2d 100644
--- a/R/print_methods.R
+++ b/R/print_methods.R
@@ -3,6 +3,7 @@
 #' @param x An object of class `infer`, i.e. output from [specify()] or
 #'   [hypothesize()].
 #' @param ... Arguments passed to methods.
+#' @importFrom glue glue_collapse
 #'
 #' @export
 print.infer <- function(x, ...) {

From 30b5801c079c0224758d50095d963463aa8fd895 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Fri, 24 Aug 2018 08:45:51 +0000
Subject: [PATCH 30/78] Get back to development version in `develop` branch.

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index a894ff0a..b2fea636 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: infer
 Type: Package
 Title: Tidy Statistical Inference
-Version: 0.3.1
+Version: 0.3.1.9000
 Authors@R: c(
     person("Andrew", "Bray", email = "abray@reed.edu", role = c("aut", "cre")),
     person("Chester", "Ismay", email = "chester.ismay@gmail.com", role = "aut"),

From 4fdbdbc4ab857d01df64774835a4d967f03d275d Mon Sep 17 00:00:00 2001
From: Brian Fannin <captain@PirateGrunt.com>
Date: Thu, 30 Aug 2018 19:31:51 -0400
Subject: [PATCH 31/78] Revised error message when `formula` argument is not a
 formula type. This partially resolves issue #110.

---
 R/specify.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/specify.R b/R/specify.R
index 93a69306..9440b418 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -44,7 +44,9 @@ specify <- function(x, formula, response = NULL,
   }
   if (methods::hasArg(formula)) {
     if (!rlang::is_formula(formula)) {
-      stop_glue("The `formula` argument is not recognized as a formula.")
+      stop_glue("The first unnamed argument must be a formula.
+                * You passed in '{get_type(formula)}'.
+                * Did you forget to name one or more aguments?")
     }
   }
 

From 961c32f8110532b0ed1f3c2895dbbf8db73abb2e Mon Sep 17 00:00:00 2001
From: Brian Fannin <captain@PirateGrunt.com>
Date: Fri, 31 Aug 2018 11:03:58 -0400
Subject: [PATCH 32/78] Catch additional misspecification of formula argument,
 add informative error message and test.

This addresses issue #110.
---
 R/specify.R                   | 11 ++++++++++-
 tests/testthat/test-specify.R |  4 ++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/R/specify.R b/R/specify.R
index 9440b418..517e3b8b 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -43,7 +43,16 @@ specify <- function(x, formula, response = NULL,
     stop_glue("Please give the `response` variable.")
   }
   if (methods::hasArg(formula)) {
-    if (!rlang::is_formula(formula)) {
+
+    tryCatch(
+       formula_arg_is_formula <- rlang::is_formula(formula)
+      , error = function(e) {
+        stop_glue("The argument you passed in for the formula does not exist.
+                  * Were you trying to pass in an unquoted column name?
+                  * Did you forget to name one or more aguments?")
+      }
+    )
+    if (!formula_arg_is_formula) {
       stop_glue("The first unnamed argument must be a formula.
                 * You passed in '{get_type(formula)}'.
                 * Did you forget to name one or more aguments?")
diff --git a/tests/testthat/test-specify.R b/tests/testthat/test-specify.R
index 392d5694..6d47fadb 100644
--- a/tests/testthat/test-specify.R
+++ b/tests/testthat/test-specify.R
@@ -57,6 +57,10 @@ test_that("sensible output", {
 
 test_that("formula argument is a formula", {
   expect_error(specify(mtcars_df, formula = "vs", success = 1))
+
+  # Issue #110: https://github.com/tidymodels/infer/issues/110
+  expect_error(specify(mtcars, am, success = "1"))
+  expect_error(specify(mtcars, response = am, "1"))
 })
 
 test_that("is_complete works", {

From 166af9ed246fee9c34691575ee52e090123c1639 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Fri, 31 Aug 2018 08:12:48 -0700
Subject: [PATCH 33/78] Fix typo

---
 R/specify.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/specify.R b/R/specify.R
index 517e3b8b..83acd4ed 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -49,7 +49,7 @@ specify <- function(x, formula, response = NULL,
       , error = function(e) {
         stop_glue("The argument you passed in for the formula does not exist.
                   * Were you trying to pass in an unquoted column name?
-                  * Did you forget to name one or more aguments?")
+                  * Did you forget to name one or more arguments?")
       }
     )
     if (!formula_arg_is_formula) {

From b124dc6bdf56dc7e0a995bc236804490742b2623 Mon Sep 17 00:00:00 2001
From: Brian Fannin <captain@PirateGrunt.com>
Date: Fri, 31 Aug 2018 11:03:58 -0400
Subject: [PATCH 34/78] Catch additional misspecification of formula argument,
 add informative error message and test.

This addresses issue #110.
---
 R/specify.R                   | 11 ++++++++++-
 tests/testthat/test-specify.R |  9 +++++++++
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/R/specify.R b/R/specify.R
index 9440b418..517e3b8b 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -43,7 +43,16 @@ specify <- function(x, formula, response = NULL,
     stop_glue("Please give the `response` variable.")
   }
   if (methods::hasArg(formula)) {
-    if (!rlang::is_formula(formula)) {
+
+    tryCatch(
+       formula_arg_is_formula <- rlang::is_formula(formula)
+      , error = function(e) {
+        stop_glue("The argument you passed in for the formula does not exist.
+                  * Were you trying to pass in an unquoted column name?
+                  * Did you forget to name one or more aguments?")
+      }
+    )
+    if (!formula_arg_is_formula) {
       stop_glue("The first unnamed argument must be a formula.
                 * You passed in '{get_type(formula)}'.
                 * Did you forget to name one or more aguments?")
diff --git a/tests/testthat/test-specify.R b/tests/testthat/test-specify.R
index 392d5694..5cb00f62 100644
--- a/tests/testthat/test-specify.R
+++ b/tests/testthat/test-specify.R
@@ -57,6 +57,15 @@ test_that("sensible output", {
 
 test_that("formula argument is a formula", {
   expect_error(specify(mtcars_df, formula = "vs", success = 1))
+
+  # Issue #110: https://github.com/tidymodels/infer/issues/110
+  expect_error(specify(mtcars, am, success = "1"))
+  expect_error(specify(mtcars, response = am, "1"))
+  expect_silent({
+    mtcars %>%
+      dplyr::mutate(am = factor(am)) %>%
+      specify(response = am, success = "1")
+  })
 })
 
 test_that("is_complete works", {

From a14987fa2b8893dd618dec243ded4f67309cd259 Mon Sep 17 00:00:00 2001
From: Richard Cotton <richierocks@gmail.com>
Date: Mon, 3 Sep 2018 08:40:26 -0400
Subject: [PATCH 35/78] use get_p_value() & get_confidence_interval()
 consistently

---
 NAMESPACE                                     |   1 +
 NEWS.md                                       |   2 +
 R/conf_int.R                                  |  75 +++++----
 R/deprecated.R                                |  49 ++++++
 R/p_value.R                                   | 134 ++++++++--------
 inst/doc/chisq_test.R                         |   2 +-
 inst/doc/chisq_test.Rmd                       |   2 +-
 inst/doc/chisq_test.html                      |  10 +-
 inst/doc/observed_stat_examples.R             |  30 ++--
 inst/doc/observed_stat_examples.Rmd           |  30 ++--
 inst/doc/observed_stat_examples.html          | 146 ++++++++++++------
 inst/doc/two_sample_t.R                       |   2 +-
 inst/doc/two_sample_t.Rmd                     |   2 +-
 inst/doc/two_sample_t.html                    |  10 +-
 man/deprecated.Rd                             |  38 +++++
 man/{get_ci.Rd => get_confidence_interval.Rd} |  30 ++--
 man/{get_pvalue.Rd => get_p_value.Rd}         |  28 ++--
 tests/testthat/test-conf_int.R                |  18 +--
 tests/testthat/test-p_value.R                 |  18 +--
 vignettes/chisq_test.Rmd                      |   2 +-
 vignettes/observed_stat_examples.Rmd          |  30 ++--
 vignettes/two_sample_t.Rmd                    |   2 +-
 22 files changed, 407 insertions(+), 254 deletions(-)
 create mode 100644 R/deprecated.R
 create mode 100644 man/deprecated.Rd
 rename man/{get_ci.Rd => get_confidence_interval.Rd} (71%)
 rename man/{get_pvalue.Rd => get_p_value.Rd} (65%)

diff --git a/NAMESPACE b/NAMESPACE
index a6bf6e27..fa237fef 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -9,6 +9,7 @@ export(conf_int)
 export(generate)
 export(get_ci)
 export(get_confidence_interval)
+export(get_p_value)
 export(get_pvalue)
 export(hypothesize)
 export(p_value)
diff --git a/NEWS.md b/NEWS.md
index 2901c160..1539f44f 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -9,6 +9,8 @@
 - Make transparancy in `visualize()` to not depend on method and data volume.
 - Make `visualize()` work for "One sample t" theoretical type with `method = "both"`.
 - Add `stat = "sum"` and `stat = "count"` options to `calculate()` (#50).
+- Deprecated `p_value()` and `get_pvalue()` (use `get_p_value()` instead) (#180).
+- Deprecated `cont_int()` and `get_ci()` (use `get_confidence_interval()` instead) (#180).
 
 # infer 0.3.1
 
diff --git a/R/conf_int.R b/R/conf_int.R
index 2632bf51..1889a88d 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -1,7 +1,6 @@
 #' Compute confidence interval
 #'
 #' Only simulation-based methods are (currently only) supported.
-#' `get_confidence_interval()` and `get_ci()` are both aliases of `conf_int()`.
 #'
 #' @param x Data frame of calculated statistics or containing attributes of
 #'   theoretical distribution values. Currently, dependent on statistics being
@@ -16,34 +15,42 @@
 #'
 #' @return A 1 x 2 tibble with values corresponding to lower and upper values in
 #'   the confidence interval.
+#' @section Aliases:
+#' `conf_int()` and `get_ci()` are deprecated aliases of `get_confidence_interval()`.
 #'
 #' @examples
+#' # Prepare the dataset
 #' mtcars_df <- mtcars %>%
 #'   dplyr::mutate(am = factor(am))
+#'
+#' # Calculate the difference in means in the dataset
 #' d_hat <- mtcars_df %>%
 #'   specify(mpg ~ am) %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
+#'
+#' # Same calculation on 100 bootstrap replicates
 #' bootstrap_distn <- mtcars_df %>%
 #'   specify(mpg ~ am) %>%
-#'   generate(reps = 100) %>%
+#'   generate(reps = 100, type = "bootstrap") %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
-#' bootstrap_distn %>% conf_int(level = 0.9)
-#' bootstrap_distn %>% conf_int(type = "se", point_estimate = d_hat)
 #'
-#' @name get_ci
-NULL
-
-#' @rdname get_ci
+#' # Use level to set the confidence level
+#' bootstrap_distn %>%
+#'   get_confidence_interval(level = 0.9)
+#'
+#' # To calculate std error, set the type and point estimate
+#' bootstrap_distn %>%
+#'   get_confidence_interval(type = "se", point_estimate = d_hat)
+#' @name get_confidence_interval
 #' @export
-conf_int <- function(x, level = 0.95, type = "percentile",
-                     point_estimate = NULL) {
+get_confidence_interval <- function(x, level = 0.95, type = "percentile",
+  point_estimate = NULL){
+
   check_ci_args(x, level, type, point_estimate)
 
-  if (type == "percentile") {
-    ci_vec <- stats::quantile(
-      x[["stat"]],
-      probs = c((1 - level) / 2, level + (1 - level) / 2)
-    )
+  if(type == "percentile") {
+    ci_vec <- stats::quantile(x[["stat"]],
+      probs = c((1 - level) / 2, level + (1 - level) / 2))
 
     ci <- tibble::tibble(ci_vec[1], ci_vec[2])
     names(ci) <- names(ci_vec)
@@ -52,47 +59,35 @@ conf_int <- function(x, level = 0.95, type = "percentile",
     multiplier <- stats::qnorm(1 - (1 - level) / 2)
     ci <- tibble::tibble(
       lower = point_estimate - multiplier * stats::sd(x[["stat"]]),
-      upper = point_estimate + multiplier * stats::sd(x[["stat"]])
-    )
+      upper = point_estimate + multiplier * stats::sd(x[["stat"]]))
   }
 
-  ci
+  return(ci)
 }
 
-check_ci_args <- function(x, level, type, point_estimate) {
-  if (!is.null(point_estimate)) {
-    if (!is.data.frame(point_estimate)) {
+check_ci_args <- function(x, level, type, point_estimate){
+
+  if(!is.null(point_estimate)){
+    if(!is.data.frame(point_estimate))
       check_type(point_estimate, is.numeric)
-    } else {
+    else
       check_type(point_estimate, is.data.frame)
-    }
   }
   check_type(x, is.data.frame)
   check_type(level, is.numeric)
-  if ((level <= 0) || (level >= 1)) {
+  if(level <= 0 || level >= 1){
     stop_glue("The value of `level` must be between 0 and 1 non-inclusive.")
   }
 
-  if (!(type %in% c("percentile", "se"))) {
+  if(!(type %in% c("percentile", "se"))){
     stop_glue('The options for `type` are "percentile" or "se".')
   }
 
-  if ((type == "se") && is.null(point_estimate)) {
-    stop_glue(
-      'A numeric value needs to be given for `point_estimate` for `type = "se"'
-    )
-  }
+  if(type == "se" && is.null(point_estimate))
+    stop_glue('A numeric value needs to be given for `point_estimate` ',
+      'for `type = "se"')
 
-  if ((type == "se") && is.vector(point_estimate)) {
+  if(type == "se" && is.vector(point_estimate))
     check_type(point_estimate, is.numeric)
-  }
 }
 
-
-#' @rdname get_ci
-#' @export
-get_ci <- conf_int
-
-#' @rdname get_ci
-#' @export
-get_confidence_interval <- conf_int
diff --git a/R/deprecated.R b/R/deprecated.R
new file mode 100644
index 00000000..8c4e7b67
--- /dev/null
+++ b/R/deprecated.R
@@ -0,0 +1,49 @@
+#' Deprecated functions
+#'
+#' These functions should no longer be used. They will be removed in a
+#' future release of \code{infer}.
+#' @param x See the non-deprecated function.
+#' @param level See the non-deprecated function.
+#' @param type See the non-deprecated function.
+#' @param point_estimate See the non-deprecated function.
+#' @param obs_stat See the non-deprecated function.
+#' @param direction See the non-deprecated function.
+#' @seealso \code{\link{get_p_value}}, \code{\link{get_confidence_interval}}
+#' @name deprecated
+NULL
+
+
+#' @rdname deprecated
+#' @export
+conf_int <- function(x, level = 0.95, type = "percentile",
+  point_estimate = NULL) {
+  .Deprecated("get_confidence_interval")
+  get_confidence_interval(
+    x, level = level, type = type, point_estimate = point_estimate
+  )
+}
+
+#' @rdname deprecated
+#' @export
+get_ci <- function(x, level = 0.95, type = "percentile",
+  point_estimate = NULL) {
+  .Deprecated("get_confidence_interval")
+  get_confidence_interval(
+    x, level = level, type = type, point_estimate = point_estimate
+  )
+}
+
+
+#' @rdname deprecated
+#' @export
+p_value <- function(x, obs_stat, direction) {
+  .Deprecated("get_p_value")
+  get_p_value(x = x, obs_stat = obs_stat, direction = direction)
+}
+
+#' @rdname deprecated
+#' @export
+get_pvalue <- function(x, obs_stat, direction) {
+  .Deprecated("get_p_value")
+  get_p_value(x = x, obs_stat = obs_stat, direction = direction)
+}
diff --git a/R/p_value.R b/R/p_value.R
index 963329a4..6be7147c 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -1,7 +1,6 @@
 #' Compute p-value
 #'
-#' Only simulation-based methods are (currently only) supported. `get_pvalue()`
-#' is an alias of `p_value`.
+#' Only simulation-based methods are (currently only) supported.
 #'
 #' @param x Data frame of calculated statistics or containing attributes of
 #'   theoretical distribution values.
@@ -12,125 +11,124 @@
 #'
 #' @return A 1x1 data frame with value between 0 and 1.
 #'
+#' @section Aliases:
+#' `p_value` and `get_pvalue()` are deprecated aliases of `get_p_value()`.
+#'
 #' @examples
+#' # Prepare the dataset
 #' mtcars_df <- mtcars %>%
 #'   dplyr::mutate(am = factor(am))
+#'
+#' # Calculate the difference in means in the dataset
 #' d_hat <- mtcars_df %>%
 #'   specify(mpg ~ am) %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
+#'
+#' # Same calculation on 100 permutation replicates
 #' null_distn <- mtcars_df %>%
 #'   specify(mpg ~ am) %>%
 #'   hypothesize(null = "independence") %>%
 #'   generate(reps = 100) %>%
 #'   calculate(stat = "diff in means", order = c("1", "0"))
-#' null_distn %>%
-#'   p_value(obs_stat = d_hat, direction = "right")
 #'
-#' @name get_pvalue
+#' # What proportion of replicates had a difference
+#' # in means more extreme than in the dataset?
+#' null_distn %>%
+#'   get_p_value(obs_stat = d_hat, direction = "right")
+#' @name get_p_value
 NULL
 
-#' @rdname get_pvalue
+#' @rdname get_p_value
 #' @export
-p_value <- function(x, obs_stat, direction) {
+get_p_value <- function(x, obs_stat, direction){
+
   check_type(x, is.data.frame)
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
 
-  is_simulation_based <- !is_nuat(x, "generate") && attr(x, "generate")
+  is_simulation_based <- !is.null(attr(x, "generate")) &&
+    attr(x, "generate")
 
-  if (is_simulation_based) {
-    pvalue <- simulation_based_p_value(
-      x = x, obs_stat = obs_stat, direction = direction
-    )
-  }
+  if(is_simulation_based)
+    pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
+      direction = direction)
 
   ## Theoretical-based p-value
   # Could be more specific
-  # else if (
-  #   is_nuat(x, "theory_type") || is_nuat(x, "distr_param")
-  # ) {
-  #   stop_glue(
-  #     "Attributes have not been set appropriately. ",
-  #     "Check your {{infer}} pipeline again."
-  #   )
-  # }
-  #
-  # if (!("stat" %in% names(x))) {
-  #   # Theoretical distribution
-  #   which_distribution(
-  #     x,
-  #     theory_type = attr(x, "theory_type"),
-  #     obs_stat = obs_stat,
-  #     direction = direction
-  #   )
+  # else if(is.null(attr(x, "theory_type")) || is.null(attr(x, "distr_param")))
+  #   stop_glue("Attributes have not been set appropriately. ",
+  #             "Check your {{infer}} pipeline again.")
+
+  # if(!("stat" %in% names(x))){
+  #    # Theoretical distribution
+  #  which_distribution(x,
+  #                     theory_type <- attr(x, "theory_type"),
+  #                     obs_stat = obs_stat,
+  #                     direction = direction)
   # }
 
-  pvalue
+  return(pvalue)
 }
 
-simulation_based_p_value <- function(x, obs_stat, direction) {
-  if (direction %in% c("less", "left")) {
+simulation_based_p_value <- function(x, obs_stat, direction){
+
+  if(direction %in% c("less", "left")){
     p_value <- x %>%
       dplyr::summarize(p_value = mean(stat <= obs_stat))
-  } else if (direction %in% c("greater", "right")) {
+  }
+  else if(direction %in% c("greater", "right")){
     p_value <- x %>%
       dplyr::summarize(p_value = mean(stat >= obs_stat))
-  } else {
+  }
+  else{
     p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
   }
 
   p_value
 }
 
-two_sided_p_value <- function(x, obs_stat) {
-  if (stats::median(x$stat) >= obs_stat) {
+two_sided_p_value <- function(x, obs_stat){
+
+  if(stats::median(x$stat) >= obs_stat){
     basic_p_value <- get_percentile(x$stat, obs_stat) +
-      (1 - get_percentile(
-        x$stat, stats::median(x$stat) + stats::median(x$stat) - obs_stat
-        )
-      )
+      (1 - get_percentile(x$stat, stats::median(x$stat) +
+          stats::median(x$stat) - obs_stat))
   } else {
     basic_p_value <- 1 - get_percentile(x$stat, obs_stat) +
-      get_percentile(
-        x$stat, stats::median(x$stat) + stats::median(x$stat) - obs_stat
-      )
+      (get_percentile(x$stat, stats::median(x$stat) +
+          stats::median(x$stat) - obs_stat))
   }
 
-  if (basic_p_value >= 1) {
+  if(basic_p_value >= 1)
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
     # scenario instead of using >=
-    tibble::tibble(p_value = 1)
-  } else {
-    tibble::tibble(p_value = basic_p_value)
-  }
+    return(tibble::tibble(p_value = 1))
+  else
+    return(tibble::tibble(p_value = basic_p_value))
 }
 
-#' @rdname get_pvalue
-#' @export
-get_pvalue <- p_value
-
-# which_distribution <- function(x, theory_type, obs_stat, direction) {
+# which_distribution <- function(x, theory_type, obs_stat, direction){
+#
 #   param <- attr(x, "distr_param")
-#   if (!is_nuat(x, "distr_param2")) {
+#   if(!is.null(attr(x, "distr_param2")))
 #     param2 <- attr(x, "distr_param2")
-#   }
-# 
-#   if (theory_type == "Two sample t") {
-#     return(
-#       pt(q = obs_stat, df = param, lower.tail = set_lower_tail(direction))
-#     )
-#   }
+#
+#   if(theory_type == "Two sample t")
+#     return(pt(q = obs_stat,
+#                              df = param,
+#                              lower.tail = set_lower_tail(direction))
+#          )
+#
 # }
 
-# theory_t_pvalue <-
+#theory_t_pvalue <-
 
-# set_lower_tail <- function(direction) {
-#   if (direction %in% c("greater", "right")) {
+# set_lower_tail <- function(direction){
+#   if(direction %in% c("greater", "right"))
 #     lower_tail <- FALSE
-#   } else {
+#   else
 #     lower_tail <- TRUE
-#   }
-# 
+#
 #   lower_tail
 # }
diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
index 69d754ed..7d0faec3 100644
--- a/inst/doc/chisq_test.R
+++ b/inst/doc/chisq_test.R
@@ -46,7 +46,7 @@ chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
 chisq_null_distn %>% 
-  get_pvalue(obs_stat = obs_chisq, direction = "greater")
+  get_p_value(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
 fli_small %>%
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
index f2dd959a..d56b5201 100644
--- a/inst/doc/chisq_test.Rmd
+++ b/inst/doc/chisq_test.Rmd
@@ -99,7 +99,7 @@ chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
 
 ```{r}
 chisq_null_distn %>% 
-  get_pvalue(obs_stat = obs_chisq, direction = "greater")
+  get_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
index 285dae6a..25831c30 100644
--- a/inst/doc/chisq_test.html
+++ b/inst/doc/chisq_test.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-08-06" />
+<meta name="date" content="2018-09-03" />
 
 <title>Chi-squared test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Chi-squared test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-08-06</em></h4>
+<h4 class="date"><em>2018-09-03</em></h4>
 
 
 
@@ -180,12 +180,12 @@ <h2>Randomization approach to <span class="math inline">\(\chi^2\)</span>-statis
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
 chisq_null_distn <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -223,7 +223,7 @@ <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>, <span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. `infer` currently does not check these for you.</code></pre>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="compute-theoretical-p-value" class="section level2">
 <h2>Compute theoretical p-value</h2>
diff --git a/inst/doc/observed_stat_examples.R b/inst/doc/observed_stat_examples.R
index dd087087..67be6ff5 100644
--- a/inst/doc/observed_stat_examples.R
+++ b/inst/doc/observed_stat_examples.R
@@ -37,7 +37,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_bar, direction = "two_sided")
+  get_p_value(obs_stat = x_bar, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( t_bar <- fli_small %>%
@@ -53,7 +53,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_bar, direction = "two_sided")
+  get_p_value(obs_stat = t_bar, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( x_tilde <- fli_small %>%
@@ -69,7 +69,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_tilde, direction = "two_sided")
+  get_p_value(obs_stat = x_tilde, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( p_hat <- fli_small %>%
@@ -85,7 +85,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = p_hat, direction = "two_sided")
+  get_p_value(obs_stat = p_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 null_distn <- fli_small %>%
@@ -109,7 +109,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( z_hat <- fli_small %>% 
@@ -125,7 +125,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = z_hat, direction = "two_sided")
+  get_p_value(obs_stat = z_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( Chisq_hat <- fli_small %>%
@@ -144,7 +144,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
 ## ------------------------------------------------------------------------
 ( Chisq_hat <- fli_small %>%
@@ -160,7 +160,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>% 
@@ -176,7 +176,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( t_hat <- fli_small %>% 
@@ -192,7 +192,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_hat, direction = "two_sided")
+  get_p_value(obs_stat = t_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>% 
@@ -209,7 +209,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( F_hat <- fli_small %>% 
@@ -225,7 +225,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = F_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = F_hat, direction = "greater")
+  get_p_value(obs_stat = F_hat, direction = "greater")
 
 ## ------------------------------------------------------------------------
 ( slope_hat <- fli_small %>% 
@@ -241,7 +241,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = slope_hat, direction = "two_sided")
+  get_p_value(obs_stat = slope_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( correlation_hat <- fli_small %>% 
@@ -257,7 +257,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = correlation_hat, direction = "two_sided")
+  get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 
 ## ----echo=FALSE, eval=FALSE----------------------------------------------
 #  # **Standardized observed stat**
@@ -274,7 +274,7 @@ null_distn %>%
 #  null_distn %>%
 #    visualize(obs_stat = t_hat, direction = "two_sided")
 #  null_distn %>%
-#    get_pvalue(obs_stat = t_hat, direction = "two_sided")
+#    get_p_value(obs_stat = t_hat, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 ( x_bar <- fli_small %>% 
diff --git a/inst/doc/observed_stat_examples.Rmd b/inst/doc/observed_stat_examples.Rmd
index cec5e8f1..3a2f3dc7 100644
--- a/inst/doc/observed_stat_examples.Rmd
+++ b/inst/doc/observed_stat_examples.Rmd
@@ -72,7 +72,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_bar, direction = "two_sided")
+  get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
 
 ### One numerical variable (standardized mean $t$)
@@ -93,7 +93,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_bar, direction = "two_sided")
+  get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
 
 
@@ -116,7 +116,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_tilde, direction = "two_sided")
+  get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
 
 ### One categorical (one proportion)
@@ -138,7 +138,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = p_hat, direction = "two_sided")
+  get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
 
 Logical variables will be coerced to factors:
@@ -176,7 +176,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### Two categorical (2 level) variables (z)
@@ -198,7 +198,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = z_hat, direction = "two_sided")
+  get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
 
 Note the similarities in this plot and the previous one.
@@ -227,7 +227,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
 
 ### Two categorical (>2 level) variables
@@ -249,7 +249,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
 
 ### One numerical variable, one categorical (2 levels) (diff in means)
@@ -271,7 +271,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -293,7 +293,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_hat, direction = "two_sided")
+  get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
 
 Note the similarities in this plot and the previous one.
@@ -318,7 +318,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### One numerical, one categorical (>2 levels) -  ANOVA
@@ -340,7 +340,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = F_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = F_hat, direction = "greater")
+  get_p_value(obs_stat = F_hat, direction = "greater")
 ```
 
 ### Two numerical vars - SLR 
@@ -362,7 +362,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = slope_hat, direction = "two_sided")
+  get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
 
 ### Two numerical vars - correlation
@@ -384,7 +384,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = correlation_hat, direction = "two_sided")
+  get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
 
 
@@ -409,7 +409,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_hat, direction = "two_sided")
+  get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
 
 
diff --git a/inst/doc/observed_stat_examples.html b/inst/doc/observed_stat_examples.html
index 68e443ae..3cba2531 100644
--- a/inst/doc/observed_stat_examples.html
+++ b/inst/doc/observed_stat_examples.html
@@ -135,9 +135,9 @@ <h3>One numerical variable (mean)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -180,9 +180,9 @@ <h3>One numerical variable (standardized mean <span class="math inline">\(t\)</s
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAFQCAYAAABXpMEwAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AADUcSURBVHgB7d0LdBTl2cDxJ+QKIQkEMEhVjIIWLyhUhWqx0gJHpbVW1IL2VKBWPumxRWhR+3nwU6jV2irWS9W2FiuHSr2gWKUUhQqCIlhqFQqUVkWQiBBCLiSQkHz7vDpxs9ns7GZ2Z2dm/3MOZOf+Pr93dubZmXdmslpCndAhgAACCCCAAAIZJNAlg2IlVAQQQAABBBBAwAiQALEhIIAAAggggEDGCZAAZVyVEzACCCCAAAIIkACxDSCAAAIIIIBAxgmQAGVclRMwAggggAACCJAAsQ0ggAACCCCAQMYJ5AQp4srFi70Vzpe+FLM8Xbp0kaysLDl8+HDM6bw+UuPIy8uThoYGrxfVtny5ubnS2NhoO53XJ9A4dNs6dOiQ14tqWz7dtoIQR0FBgTQ1NZl/tkF7eALdrrKzs30fh7XfOnjwoPj9aTA5OTnmOJLKOA6/+Bdp2bzlky3z/fdbt9DsS74hWaHtIRmdJiS6pIPJWFhoGXlf+Yp07969w6UFKgHSDdlTnU158vPzRb+Enit3goi6Y+/WrZvU1NRIc3NzgnN7a3KN5cCBA4GIQ3eKWid+76xty+9x9OjRQ+rq6nz/fdfkWhMgv++3NLHWbUvrRBNTP3cai9ZHSuN4epHIs+1PMhw+f3Qo08hLCp8uJT/0rzopSwstZPlykYsu6nBprl0C07MDr7zyiqxfv75dJe3Zs0eWLl0qW7Z8ml1+Wlyt0NWrV8uaNWsC8au8w1pgBAIIIIAAAgi4KuBKAvTxxx/LlVdeKe+88448+eSTctNNN7UGuWHDBpk0aZJs3bpVZs6cKYsWhbLMUFdfXy8TJ06UFStWyPz58824VJ7eay0QHxBAAAEEEEAg8AKuXAJ7+eWXZdy4cXLFFVeY65QXhU5JvR+6hti/f3+ZO3euzJkzR0477TS5/PLL5eqrr5axY8fKwoULZdiwYTJt2jRTCVOmTJG1a9fK8OHDA18pBIgAAggggAACqRVwJQEaP358axR6xkcb/fbp08dcCtuxY4cMHjzYjC8rKzPXZHfu3Cnbtm2TMWPGtM43dOhQ2bRpU5sE6LnnnjNnjnQibUsz5ZhjWqf3xIeiopjF0Ovo2qBQ22r4udM4tNPGZn4/S6ftG4ISh34nimy2QT9sd/r9CEIc+l3Xthp+j0W3K/3O618/d1b5CwsLfd/mT7crjSeVbTDrQ9/DaC2ltIlxVpI2BG0DpFtV7CNn/Cuza5Hq6pH3lltuCbVJWi7XXnutSXQ+/PBD0Y1PdwxWV1JSIpWVlVJRUSHFxcXWYPNZk6XwTtsTrVy50gzSneS0//mf8NHp/9y1q20ZNHY96Aah0wbEfu+0PoISh8bSNY5t0Ot1pjv2IMShzvpd9/sPHo1Dt60gxKGx6M0ofu+0PqwfoqmK5VAo6Y2WAOlR7rMjuLO163L0n/2RM7712N2X7GoCdOutt8o111wjM2bMkCOPPFJOOumkdreAayt2PQBpZYbfHq7DI3eCs2fPbqOwK9S+yFPd7t0xi2PdBabtnfzcaX317NlTtDF7Kn+BuGGkCXgQ7mbTOPQAtXfvXjfYUroO3bb27duX0nW4sXA9w613HNXW1rqxupStQ5M4PeOgsfi50xh69eplfnCn9O4pF5D0rKIeR1IaRwd3NX+cxPj0zI/+jE7WMjW1LYxRPlfOYW7cuLF1R/y5z31OzjvvPHM3mG58kbeF6tmffv36Se/evc2GaZXdGm718xcBBBBAAAEEEOisgCsJ0Lp16+S3v/2tKaPeDv/aa6/JgAEDzC9Ubei8+NMHGOrlLP21p/9GjBghS5YsMQ/X0zMLeiv8kCFDOhsn8yGAAAIIIIAAAq0CrlwCu/TSS+XOO++UyZMnm8tamtzonWDaTZ06tfX2d73WP2vWLDN81KhR5hlAEyZMMI27tCF1eXm5Gcd/CCCAAAIIIICAEwFXEiC9q0bb6+gTdrW9iNX6Xguut8LrLe9VVVWiT0q1Om2/oPNoewxt+xOUBndWfPxFAAEEEEAAgfQJuJIAWeHpY8c76sKTn/Bp/H7LaHgsfEYgXgGnd3DpDwb9oRHrO2dXFr1c7fdG7XYxMh4BBDJXwNUEKHOZiRyBxAT0DhW9k0s7J89WCn+URCIl0ARM//n9Tp9EYmZaBBDILAESoMyqb6L1mYA+PV3vgHS701fSaAJEhwACCARVwJW7wIKKR1wIIIAAAggg4E8BEiB/1hulRgABBBBAAAEHAiRADvCYFQEEEEAAAQT8KUAC5M96o9QIIIAAAggg4ECABMgBHrMigAACCCCAgD8FSID8WW+UGgEEEEAAAQQcCJAAOcBjVgQQQAABBBDwpwAJkD/rjVIjgAACCCCAgAMBEiAHeMyKAAIIIIAAAv4UIAHyZ71RagQQQAABBBBwIEAC5ACPWRFAAAEEEEDAnwIkQP6sN0qNAAIIIIAAAg4ESIAc4DErAggggAACCPhTgATIn/VGqRFAAAEEEEDAgQAJkAM8ZkUAAQQQQAABfwqQAPmz3ig1AggggAACCDgQIAFygMesCCCAAAIIIOBPARIgf9YbpUYAAQQQQAABBwIkQA7wmBUBBBBAAAEE/ClAAuTPeqPUCCCAAAIIIOBAgATIAR6zIoAAAggggIA/BUiA/FlvlBoBBBBAAAEEHAiQADnAY1YEEEAAAQQQ8KcACZA/641SI4AAAggggIADARIgB3jMigACCCCAAAL+FCAB8me9UWoEEEAAAQQQcCBAAuQAj1kRQAABBBBAwJ8CJED+rDdKjQACCCCAAAIOBEiAHOAxKwIIIIAAAgj4U4AEyJ/1RqkRQAABBBBAwIEACZADPGZFAAEEEEAAAX8KkAD5s94oNQIIIIAAAgg4ECABcoDHrAgggAACCCDgTwESIH/WG6VGAAEEEEAAAQcCJEAO8JgVAQQQQAABBPwpkOPPYkcvdc/og9M3tGfsEnXp0kWysrKkoKAgfWVMwpo1Du1KSkqSsLT0LiInJ8cTcVim6dTQ7TI3NzedRTDr1jL0tPkupb2QcRTA+q57wTSO4nY4icah22deXl6H0/hhhMahXXFxsbS0tPihyB2WUfdbul2lMo7aUH03RilBj9CwTySjjExwkCYkejSJfeSMf6FNNpMGKgHaZxOs66P3xS5Rfn6+2ZHU19e7XrRkrlAPlLoz3L9/vzQ3Nydz0a4vS5O4mpqatMeh20Zpaanr8YevsKGhQWpra8MHpeWzJj/7bL5LaSlYgistKysTr5gmWPQ2k+uBVr/vdXV1bYb7rUdj0O9ZdXW1NDXZHSq9HV1RUZHocSSlcRw6FBWhKurQzg0sCs2mpwNiHznjX3a+zaRcArMBYjQCCCCAAAIIBE+ABCh4dUpECCCAAAIIIGAjQAJkA8RoBBBAAAEEEAieAAlQ8OqUiBBAAAEEEEDARoAEyAaI0QgggAACCCAQPAESoODVKREhgAACCCCAgI0ACZANEKMRQAABBBBAIHgCJEDBq1MiQgABBBBAAAEbARIgGyBGI4AAAggggEDwBEiAglenRIQAAggggAACNgIkQDZAjEYAAQQQQACB4AmQAAWvTokIAQQQQAABBGwESIBsgBiNAAIIIIAAAsETIAEKXp0SEQIIIIAAAgjYCJAA2QAxGgEEEEAAAQSCJ0ACFLw6JSIEEEAAAQQQsBEgAbIBYjQCCCCAAAIIBE+ABCh4dUpECCCAAAIIIGAjQAJkA8RoBBBAAAEEEAieAAlQ8OqUiBBAAAEEEEDARiDHZjyjEUAgAwVycnIkOztb8vLy0hJ9U1OTVFdXp2XdrBQBBDJDgAQoM+qZKBFISKBLly6yefNm2bJlS0LzJWPiwYMHy9FHH52MRbEMBBBAoEMBEqAOaRiBQGYLrFu3Tp544gnXEaZMmUIC5Lo6K0Qg8wRoA5R5dU7ECCCAAAIIZLwACVDGbwIAIIAAAgggkHkCJECZV+dEjAACCCCAQMYLkABl/CYAAAIIIIAAApknQAKUeXVOxAgggAACCGS8AAlQxm8CACCAAAIIIJB5AtwGn3l1TsQJCOjzcNLRpWu96YiVdSKAAALpECABSoc66/SFQElJiXTr1s0XZaWQCCCAAAKJCZAAJebF1BkkoGdh3n33XVmwYIHrUY8cOVLOPvts19fLChFAAIFMESABypSaJs5OCezbt09WrVrVqXmdzDRw4EAnszMvAggggICNQHoaONgUitEIIIAAAggggEAqBUiAUqnLshFAAAEEEEDAkwIkQJ6sFgqFAAIIIIAAAqkUIAFKpS7LRgABBBBAAAFPCpAAebJaKBQCCCCAAAIIpFKABCiVuiwbAQQQQAABBDwp4FoC1NjYKK+//rq89dZb0tLS0gZjz549snTpUtmyZUub4QcPHpTVq1fLmjVrROenQwABBBBAAAEEkiHgSgJUUVEh48ePN4nM888/L1deeaVocqPdhg0bZNKkSbJ161aZOXOmLFq0yAyvr6+XiRMnyooVK2T+/PlmXGTiZCbkPwQQQAABBBBAIEEBVxKgJ598UsaOHSvTp0+Xm2++WQYMGCDLli0zRZ07d67MmTNHrrvuOnnkkUfk0UcflUOHDsnChQtl2LBhZvoHH3xQDhw4IGvXrk0wPCZHAAEEEEAAAQTaC7jyJOgpU6ZIVlZW69r3798veoanqalJduzYIYMHDzbjysrKzLuXdu7cKdu2bZMxY8a0zjN06FDZtGmTDB8+vHXYY489Jm+//bbpz87Olv8744zWcZ740KNHzGLoqxbUJT8/P+Z0Xh9pvbizuLjY60W1LV9ubq5YcehnuvQI6DbV49Pvj9aD9Tk9pUnOWvW7XlBQIDk5rux2k1PoKEvROHR/6/fvh7XfKioqatcsI0rYnh6k25T+S+VVkrrQ9zBaQ5SSkMxnR3dnTPrN0LMysY+c8a+jyWZSV76JeXl5rcVYvny5SXouuOAC2b17txQWFrZJjvQFlJWVlaKXzawDkc6snzVZCu+07dD27dvNIK38bK8lQKGdRKzO+gLGmsYP46zkVneKfu+snbvGYcXl95j8Wn5rewqvE7/GYpU7CLFoDEGJQ+tFt7NUJg5W3afyr3UsSWUcWufROt3rRx8TberYwzT50WUl60hyOPbqxJUEyCrD4sWLTXueu+++W7p37y51dXVy+HDbIupZIf2VpBtl+Dgd3rVrV2tR5u+MGTPa9O8KXWrzVLd3b8zi6Jkf3XD1bJifO62vnj17ir43q7m52c+hiCbgNTU1Jg6NiS49Arod6Q8h7axtKz0lSd5a9Qy3ftdra2uTt9A0LEnP/OiPWt1/+7nTGHr16iVVVVXmaoSfY9GzWNZVlZTFEWqaEq375FsabUziw4pCsxSE/sU+csa/XLtrK64lQI8//ri50+u+++4T3RFopxuffom0QbR1GUh3ev369ZPevXu37gB1Wh1+9NFH60c6BBBAAAEEEEDAkYArjaBffPFFefnll+XXv/51a/KjpdbLVtrQWc8Mabdy5Urza09/8Y0YMUKWLFkiDQ0Nope69Fb4IUOGmOn4DwEEEEAAAQQQcCLgyhkgvbPro48+MneCWYUdN26c/PCHP5SpU6e23v6ul4NmzZplJhk1apR5BtCECRPMZSK9jb68vNyanb8IIIAAAggggECnBVxJgJ566qkOC9i/f39zy7tehw2/00PPDs2ePdu0x9C2P9pPhwACCCCAAAIIJEPAM1lFePITHpg27qJDAAEEEEAAAQSSKeBKG6BkFphlIYAAAggggAACTgVIgJwKMj8CCCCAAAII+E6ABMh3VUaBEUAAAQQQQMCpAAmQU0HmRwABBBBAAAHfCZAA+a7KKDACCCCAAAIIOBUgAXIqyPwIIIAAAggg4DsBEiDfVRkFRgABBBBAAAGnAiRATgWZHwEEEEAAAQR8J0AC5Lsqo8AIIIAAAggg4FSABMipIPMjgAACCCCAgO8ESIB8V2UUGAEEEEAAAQScCpAAORVkfgQQQAABBBDwnQAJkO+qjAIjgAACCCCAgFMBEiCngsyPAAIIIIAAAr4TIAHyXZVRYAQQQAABBBBwKkAC5FSQ+RFAAAEEEEDAdwIkQL6rMgqMAAIIIIAAAk4FSICcCjI/AggggAACCPhOgATId1VGgRFAAAEEEEDAqQAJkFNB5kcAAQQQQAAB3wmQAPmuyigwAggggAACCDgVIAFyKsj8CCCAAAIIIOA7ARIg31UZBUYAAQQQQAABpwIkQE4FmR8BBBBAAAEEfCcQdwI0evRoqa2tbRfgvHnz5Iorrmg3nAEIIIAAAggggIBXBXJiFWzt2rWybNkyM8mrr74qd9xxhxQUFLTOcvjwYXn22Wdl4MCBrcP4gAACCCCAAAIIeF0gZgI0YMAAmT59uhw8eFAaGxvlhRdekOzs7NaYcnNz5dhjj5Wf/OQnrcP4gAACCCCAAAIIeF0gZgLUq1cvWb16tYnhoosukgULFkj37t29HhPlQwABBBBAAAEEYgrETIDC51y8eLE5E7R9+3bzN3ycJkVHHnlk+CA+I4AAAggggAACnhWIOwF6+umn5eqrr5aqqqp2wVx22WXypz/9qd1wBiCAAAIIIIAAAl4UiDsBmjp1qlx88cVy7bXXSmlpaZtYuCzWhoMeBBBAAAEEEPC4QFwJUHV1tezevVvuvPNOOeKIIzweEsVDAAEEEEAAAQRiC8T1HKDi4mIpLy+X9evXx14aYxFAAAEEEEAAAR8IxHUGSOOYM2eOXH/99bJjxw6TDOXkfDarnhU6+eSTfRAuRUQAAQQQQAABBEQ+y2JsNLQN0P79+2XKlCntpqQRdDsSBiCAAAIIIICAhwXiToA++ugjaWlpiRpK+MMRo07AQAQQQAABBBBAwEMCcSdAlZWVHSZAXbt2lZ49e3ooLIqCAAIIIIAAAgh0LBB3AjRo0CBzCSzaorxyCcxzKZhNUtilSxfJyspq8361aL5eH6ZxaFdSUuL1otqWT9u2WXHoq17o0iOg25T1o0rrwfqcntIkZ63Wd93v25XGofWTl5eXHJg0LUXj0E5v8uno6kaaipbwanW/pdtVKuOoDdV3Y5SS9QgN+0QyysgEB2lCokeTZB3Lm2zWH3cCpC9D1ZefWl1NTY38/e9/lwceeMDcHm8NT+fffelcebR174tdovz8fLMjqa+vjza3b4bpC3J1Z6htxJqbm31T7mgF1eRHt22NIwgH3Wgx+mGY+u/79Puj9WB99kPZOypjWVmZNDQ0SG1tbUeT+GK4Hmj1+15XV+eL8nZUSI1B98H6mJemJrtDZUdL8cbwoqIi0eNISuM4dChqsO0fjRx1srgGFoWm0tetxz5yxrUoM1G+zaRxJ0CnnHJKu0V96UtfkgMHDsjtt98uv/nNb9qNZwACCCCAAAIIIOBFgbieAxSr4PoOsLfffjvWJIxDAAEEEEAAAQQ8JRD3GaA33nijzek1PdVWUVEhP/3pT+W8887zVFAUBgEEEEAAAQQQiCUQdwI0ZsyYqI2g9TLY7NmzY62DcQgggAACCCCAgKcE4k6Atm/f3q6FeWFhoYQ/EdpTkVEYBBBAAAEEEECgA4G4EyC9VbCqqkoWLFggW7dulb59+8qQIUNk5MiRvr8dsgMbBiOAAAIIIIBAQAXiToD++c9/il4G0ydCDx48WD7++GPZtWuXnH/++bJo0SLfP8smoPVLWAgggAACCCAQRSDuu8CuueYa+eIXvygffPCBvPXWW7Jz507zHKDNmzfLvffeG2XRDEIAAQQQQAABBLwpEFcCpM/6Wb9+vbnj66ijjjKR6FM09RLY9OnTZfny5d6MjlIhgAACCCCAAAJRBOJKgPSx5/qIbU2EIjt9GmhKnz4ZuUL6EUAAAQQQQAABhwJxJUD6qgN91s8NN9wg69atM8mQPtJ9yZIl8qtf/UpGjx7tsBjMjgACCCCAAAIIuCcQVwKkxXnooYdMA+izzjpL9J02paWlcuGFF8oZZ5whM2bMcK/ErAkBBBBAAAEEEHAoEPddYAMHDpQ333xTXnrpJdGGz3pW6PTTT5dzzjnHYRGYHQEEEEAAAQQQcFcg7gRIi6VvztWzPmPHjjWljNYmyN3iszYEEEAAAQQQQCBxgbgvgekzfyZNmiSPPfaYWYs2fi4vL5d77rmHRtCJuzMHAggggAACCKRRIO4E6Prrr5f33ntPzj77bFPcrl27yty5c00j6MWLF6cxBFaNAAIIIIAAAggkJhDXJbBDhw7J0qVLZePGjdKvXz+zBr01fsKECVJTU2Nej3HJJZcktmamRgABBBBAAAEE0iQQ1xkgfehhY2OjefpzZDmrq6ulsrIycjD9CCCAAAIIIICAZwXiSoByc3PlK1/5itx4443mVRhWNPp0aG0DpO8Do0MAAQQQQAABBPwiEFcCpMHMmzdP9OGHxxxzjJSUlIi2ATrzzDPNAxL1dRh0CCCAAAIIIICAXwTiagOkweiDD1etWiVbtmyRf/zjH6JtgE499VQ56aST/BIr5UQAAQQQQAABBIxA3AmQTq1Jz6BBg8w//BBAAAEEEEAAAb8KJJQA+TVIyu1fAW1/pom3W112drZ54Gdzc7Or63UrPtaDAAIIIPCJAAkQW4JnBfTuw969e7tePn3iOR0CCCCAQLAFSICCXb+BiO7BBx+UNWvWuB7L/fff7/o6WSECCCCAgDsCJEDuOLMWBwL6rKk9e/Y4WELnZtXLYHQIIIAAAsEUcK9xRTD9iAoBBBBAAAEEfChAAuTDSqPICCCAAAIIIOBMgATImR9zI4AAAggggIAPBUiAfFhpFBkBBBBAAAEEnAmQADnzY24EEEAAAQQQ8KEACZAPK40iI4AAAggggIAzARIgZ37MjQACCCCAAAI+FCAB8mGlUWQEEEAAAQQQcCZAAuTMj7kRQAABBBBAwIcCJEA+rDSKjAACCCCAAALOBEiAnPkxNwIIIIAAAgj4UIAEyIeVRpERQAABBBBAwJkACZAzP+ZGAAEEEEAAAR8KuJ4ALVu2TJqamtpQ6Zu+ly5dKlu2bGkz/ODBg7J69WpZs2aNNDY2thlHDwIIIIAAAggg0FkBVxOgp59+Wm677TY5fPhwa3k3bNggkyZNkq1bt8rMmTNl0aJFZlx9fb1MnDhRVqxYIfPnzzfjWlpaWufjAwIIIIAAAggg0FmBnM7OmMh8esZn1qxZsnfv3nazzZ07V+bMmSOnnXaaXH755XL11VfL2LFjZeHChTJs2DCZNm2amWfKlCmydu1aGT58eLtlMAABBBBAAAEEEEhEwJUESM/4aOJy4YUXysiRI1vLp4nRjh07ZPDgwWZYWVmZdOvWTXbu3Cnbtm2TMWPGtE47dOhQ2bRpU5sE6N5775V169aZaXJycuThUOLkqa60NGZxunTpIllZWdK1a9eY03l9pMahXY8ePbxeVMrnEwHdpko//f7k5ua2fvZJ8aMW0/qu5+XlRR3vl4HWfis/P98vRY5aTq0P7UpKSsTvVxf0+KfbVSrjqA0t/1AUyZ6hYZ9IRhmZ4KDs0PT6L/aRM/6F2jWccSUB0i/KRRdd1K7Uu3fvlsLCQpMEWCN1Y6ysrJSKigopLi62BpvPmiyFd927d5eePZVfRDeA5vCRXvjcHLtE+gXUDbbZZjovhBJPGYISRzyxMk3qBaztKUjfkaDEokmQVT+p3xJSswbrh5vGkcrEITWlb7tULX+qt62OjPQol6wESH9Ka0OX2EfOtrHH6rNrNONKAtRRAbOzs9u0B9Lp9KxQQUGBRI7T4ZFnSr773e+2WfSuJ59s05/2nqqqmEXQxFC/hNreyc+d1pfGUl1dndSdovWL2c82lL1zAnpQqvr0+6M/cqzPnVuaN+bSM9wNDQ1SW1vrjQJ1shR6Rk7PNtTV1XVyCd6YTWPQ/VZNTU27G3O8UcL4S1FUVGSOI3qcTFnXwY1I+5O4wqLQsgpC/2IfOeNfod05SlcbQUcWu1evXuZLpHd7WZ2e/enXr5/07t3bnAmKHG718xcBBBBAAAEEEOisQFoTIL1spQ2dFy9ebMq/cuVKc0lLf/GNGDFClixZYn4x6W3yeiv8kCFDOhsn8yGAAAIIIIAAAq0Cab0EpqWYOnVq6+3vejlI7xbTbtSoUeYZQBMmTDCXicaPHy/l5eVmHP8hgAACCCCAAAJOBFxPgFatWtWmvP379ze3vOs1/vC7iPTs0OzZs831WW37o/10CCCAAAIIIIBAMgQ8k1WEJz/hgWnjLjoEEEAAAQQQQCCZAmltA5TMQFgWAggggAACCCAQrwAJULxSTIcAAggggAACgRHwzCWwwIgSCAIIOBI47rjjRJ81o8/Nsbrwz9awVP3VB77pa3vC31mYqnWxXAQQSJ8ACVD67FkzAghEEdCnwx84cEAWLFgQZWxqB+mzycaNG2fuPCUBSq01S0cg3QIkQOmuAdaPAALtBPQpw88880y74akeoGefNAGiQwCB4AvQBij4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfICdIIfb0WjA9Y5eoS5cukpWVJQUFBV4reULl0Ti0KykpSWg+JkbAqwJFRUXS0tKS1OJZ3/Xc3NykLtfthWkc+p3Py8tze9VJXZ/GoV1xcXHS6zqpBY1jYTk5OaLbVbK32fBV14bquzF8wKefe4T+fiIZZWSCgzQh0aNJ7CNn/Attspk0UAnQPptgXR+9L3aJ8vPzzY6kvr7e9aIlc4WawOnOcP/+/dLc3Jy0ResOqm/fvklbHgtCIF6BmpoaaWyMtruPdwntpysrK5OGhgapra1tP9JHQ/RAq9/3uro6H5W6fVE1Bt0HV1dXS1OT3aGy/fxeGqIJux5HUhrHoUNRQ66KOrRzA4tCs+npgNhHzviXnW8zKZfAbIAYjQACCCCAAALBEyABCl6dEhECCCCAAAII2AiQANkAMRoBBBBAAAEEgidAAhS8OiUiBBBAAAEEELARIAGyAWI0AggggAACCARPgAQoeHVKRAgggAACCCBgI0ACZAPEaAQQQAABBBAIngAJUPDqlIgQQAABBBBAwEaABMgGiNEIIIAAAgggEDwBEqDg1SkRIYAAAggggICNAAmQDRCjEUAAAQQQQCB4AiRAwatTIkIAAQQQQAABGwESIBsgRiOAAAIIIIBA8ARIgIJXp0SEAAIIIIAAAjYCJEA2QIxGAAEEEEAAgeAJkAAFr06JCAEEEEAAAQRsBEiAbIAYjQACCCCAAALBE8gJXkhElEyB3Nxc6d69e8xFdunSRZqbm6WkpCTmdIxEAAEEEEDAKwIkQF6pCY+WIy8vT/TfmjVrXC9hQUGBnHHGGa6vlxUigAACCARfgAQo+HXsOEI9uzNnzhzHy0l0AX379pV58+YlOhvTI4AAAgggYCtAGyBbIiZAAAEEEEAAgaAJkAAFrUaJBwEEEEAAAQRsBUiAbImYAAEEEEAAAQSCJkACFLQaJR4EEEAAAQQQsBUgAbIlYgIEEEAAAQQQCJoACVDQapR4EEAAAQQQQMBWgATIlogJEEAAAQQQQCBoAjwHKGg1SjwIINBpAX2auT73qrS0VFpaWjq9nI5mLCwslG7dunU0WrKysqS2tlbq6uo6nIYRCCCQHAESoOQ4shQEEAiAQFFRkeirXZ5//nmpqKhwPaJx48aJPgGdDgEEUi9AApR6Y9aAAAI+E/jb3/4mGzdudL3Uo0ePJgFyXZ0VZqoAbYAyteaJGwEEEEAAgQwWIAHK4MondAQQQAABBDJVgAQoU2ueuBFAAAEEEMhgAdoA+aDyu3fvLrm5uWkpaXZ2dlrWy0oRQAABBBBIpQAJUCp1k7RsTX6qqqpk27ZtSVpi/IsZNGiQ6K3BdAgggAACCARJgATIJ7X55ptvyn333ed6aW+55RY588wzXV8vK0QAAQQQQCCVArQBSqUuy0YAAQQQQAABTwqQAHmyWigUAggggAACCKRSgAQolbosGwEEEEAAAQQ8KeDpNkAHDx6U9evXm/fjaDuUdN0JpTXXPfQvK9EqDD1WP1and1jpu39ycmJXgz6anw4BBBBAAAEEkicQ+8ibvPUkvKT6+nqZPHmynHzyyfLhhx/Kk08+KXfffbdJGBJemMMZFElTmZrQv8OJLKupKebUhw9/sjS7ly7m5+fHXA4jEUAAAQQQQCAxAc8mQAsXLpRhw4bJtGnTTERTpkyRtWvXyvDhwxOLMAlTW2d+lq5eLbJrV9xL/O3vfhf3tLEmXLRoUazRjEMAAQQQQACBBAWyQmcfWhKcx5XJb775ZhkzZoyce+65Zn0PP/ywuQSmZ4Ws7rbbbpNVq1aZXr2M9HzYOGuaZP3VTLEy9K8xkQV+WvZEZok2ba9evaSxsVGqq6ujjU7pMH0GkL6d+qOPPkrpeqItXC/99enTR/bv3y8NDQ3RJknpsN69e0tT6CyePoPJ7U4ffllYWCh79uwR60yhm2XQ2PUsbF1dnZurNesqLS01b2TX2N3udFvXbb6ystJ859xev27velmczh0BtfboIdAdgATWUnXFd6ThiT+1m6Ns/qOSlZfXbni8AzQBaf50Ym3soVt/QldaPp032p+DoasnhRddFG2UGebZM0AVFRVSXFzcWnD9vGPHjtZ+/aCXx6wnFevfgxde2GZ8MnuaQw8jLEryjsna0cXzBdT2T3pgSEenSUCsdVvtmDRJS3anB/9u3bqZf8ledrTladLV3PzJ11HrRberWLFHW0ayhmnsnX0Ipcah9eIkedJLr+m4/GodlCx3rQMncSRaH7quIpv2e4kuU6fXH2m6bVnbV7RlWOPi2SdEm9/pMMs+1nJ0Gu2SXcbw716s9SdrnMahdaL7t1TFFG9ZncbuZP54523u4FxJ09e/Llldu8YbarvpdJvXOtAuL5RImWN56MdXMjq7/YZnE6DInZ4CdY1AHjduXBujXQlcnmozY5p69OCiG5/+0vZzp7+ae/bsaX41Wztwv8ajCUdNTU3Mg5QfYtM4dOe+d+9ePxQ3Zhl129q3b1/MafwwsqysTA4cOCC1tbV+KG6HZdQfY3qgSsfZwQ4L1YkRGoOeXdczvNYBuBOL8cQsmrDrcSSlcYRuSorW7d0T2sd0LYg2KuFhGoceT5J1tcPuB5xnby/SU/B6Gtrq9HO/fv2sXv4igAACCCCAAAKdFvBsAjRixAhZsmSJafuhbQHWrFkjQ4YM6XSgzIgAAggggAACCFgCnr0ENmrUKFkduutqwoQJ5jLR+PHjpby83Co3fxFAAAEEEEAAgU4LeDYB0vYLs2fPNu0xtO2P9tMhgAACCCCAAALJEPB8VpGKuzGSAccyEEAAAQQQQMC/Ap5tA+RfUkqOAAIIIIAAAl4XIAHyeg1RPgQQQAABBBBIugAJUNJJWSACCCCAAAIIeF2ABMjrNUT5EEAAAQQQQCDpAiRASSdlgQgggAACCCDgdQHP3wWWCKDfbpW33j/jt3JH1ok+El9fQ6LvbvJ7LPruGH0Ni76ixM+dvrxWH4vfo0cPP4dhyq514vftSgPZunWreadd5Ct9/FZB+t3Q94D5vU4Ohl7tsGnTJvMaH7/Hoq8g0npJZRzNp58mLVFe29QlP0+ykvSYmo8//lgOHTqUtPcv6r48VufZt8HHKjTjvCXw17/+Va677jpZtWqVHHHEEd4qXIaWZubMmfLf//5XnnrqqQwV8F7YZ5xxhkyePFmmTp3qvcJlYInWrVsn3/72t+W5556Tz3/+8xko4L2Q77jjDtHjyfLly10pnL9/5rpCxEoQQAABBBBAIGgCJEBBq1HiQQABBBBAAAFbgez/C3W2UzEBAjEEtC1T79695cwzz5S8vLwYUzLKLQFtDzBgwAA56aST3Fol67ER0PYIX/jCF6Rfv342UzLaDQHdb2kbubPOOsu0zXJjnawjtoDut4499lgZPHhw7AmTNJY2QEmCZDEIIIAAAggg4B8BLoH5p64oKQIIIIAAAggkSSBQt8EnyYTFxCGwbds2+eCDD2T48OHS0W29GzZsEL3V1OpOOOGEpN3eaC0z0/+q7/r160VP5+slyNzc3Kgk8U4XdWYGJiSwb98+efPNN+Xkk0+WI488Muq8tbW18s4777QZp98luuQLbN++XT788MPWBffq1UsGDhzY2h/+YcuWLfL+++/L0KFDzWX98HF8To6APjLlvffea7Mw3X9F2/5TfQzhElibaqAnHoHp06ebZ+Ucf/zxsmLFCpkxY4a5jh4+rz6D5vzzzzdtHqzhesvpqaeeavXy16FAfeiZHHpbtR5odQefn58vd999t0mGwhcd73Th8/C5cwJ6S7U+euDcc881SZDeXj1t2rR2C9Pvzf3332/aaVkj77zzTusjf5MoMGvWLNHnYlnPxNL2JVdeeWW7Ndxzzz2yceNGkxytWbNG7rvvPjnmmGPaTccAZwL6uJQ///nPrQvRfdf+/ftl8eLFrcP0gyvHkNADregQiFvg7bffbgklMq3Th57X0HL99de39lsf/v3vf7dcddVVVi9/UyDw+9//viW0025d8jXXXNPy2muvtfZbH+Kdzpqev50TCO2wW8aNG9cSev6SWUDoAaEtY8eObamsrGy3wIcffrhl3rx57YYzIPkC48ePbwmd1Ym54Hfffbflm9/8ZkvooZtmuj/+8Y8tt99+e8x5GOlcoKGhoSWUjLa8+uqr7RbmxjGENkBtck567AT0rqJHHnmkdTLN3EMbcWu/9SG08cpRRx0lf/nLX8yDxg4cOGCN4m+SBPQypJ6qtzr9rE+2jezinS5yPvoTE9C7vB577DEpLy83M+r3Qi916ZOsIzv9fnTv3l0WLFgg+kC+0N4/chL6kyCg+51QAir6hOH58+fLjh07oi5VHxqqZ4b0LiTtOvouRZ2ZgZ0W+N3vfmeuCpxzzjntluHGMYQEqB07A2IJ6A7CavOze/duefzxx+U73/lOu1n0sf96Pb2mpsb8vfzyy2XPnj3tpmNA5wUqKiqkuLi4dQH6ee/eva391od4p7Om52/nBQoLC83M+mqCe++911wG1kdERHa6c3/99dfNZcs//OEP8uMf/zhyEvqTIPCf//zHtEPUJFMTUb0c+cILL7RbsrZLKSkpaR3e0XepdQI+OBbQH8/PPPOMTJw4Meqy3DiG0Ag6Kj0DLYFly5bJ5s2bTa/+KrIy9dApY7nhhhtk0qRJURuvfe973xP9161bNzOvNsLVs0HaDoguOQJ6xiH87IJeM7eS0/A1xDtd+Dx87ryAbuu33XabOavzv//7v1EX9Oijj5o2KfqD4utf/7p84xvfMGcn9KwpXfIEBg0aJIsWLTLv+9Kl6rOx1D50abLNSviOtOFwpUePB/oMprKysqjrc+MYwhmgqPQMtAQ0gdFfRvqvoKDADP7Xv/4loXY/8v3vf1++9rWvWZO2+btz5842d4BpY0L9lUWXPAE9s6Cn961OP0d7yF6801nL4W/nBfSSy49+9CMpKiqS2bNnR30wqL7sUe80si636MND9SCgZ+rokitQVVUlelee1el+SBtE6xm68K5Pnz7tvksd3cEXPh+fOy/w4osvysUXX9zhAtw4hpAAdcjPCBXQMz56iUv/6VNs9TKWvmjzlltukS9/+cttkPRuI92xa7dy5Up56KGHzGc9KOjL7UaOHGn6+S85AiNGjJAlS5aYNlhaL3rnypAhQ8zCtd+65BhruuSUhKVYAvq9OPHEE+XGG280d0paw/Wv3vqr7YL0UQV6t55eAtNOf1DopcvTTz/d9PNf8gT0Mov+WNN9k7az0ruPdL+lyWf4/kofIaGPJdBHe+iZ1Oeff77dna3JKxVL0rOk+n045ZRT2mCE77fcOIZwG3wbfnrsBB588EEJ3SHR5lbr0tJSefbZZ83zaPTUv97OWF1dLXpbr97iqA0QR48ebd4Yb/3qtVsP4+0FdEd96623mh23uobudpHLLrvMzHjXXXeZA622eYg1nf1amCJeAU1kQnfimcn1uSZWp7e7awPbCy64QPRt16eddpq5RV5vJmhsbDTfD02YrMvL1nz8TY6ANkx/6aWXzPdA2/bombkjjjiizf5K16TJkd76rvuz/v37y5w5cyQnh1YiyamFtkvRNnA33XSTeWRE+Jjw/ZYbxxASoHB9PiddQM/+6PV1fUYNXWoEtKG5tv2x21nHO11qSslSownoGQo9KIcnTNGmY5gzAb3kpXfkqXWsThNSPTuhd+jReUMglccQEiBv1DGlQAABBBBAAAEXBWgD5CI2q0IAAQQQQAABbwiQAHmjHigFAggggAACCLgoQALkIjarQgABBBBAAAFvCJAAeaMeKAUCCCCAAAIIuChAAuQiNqtCAAEEEEAAAW8IkAB5ox4oBQIIOBTQ54Yk0iU6fSLLZloEEPC+AAmQ9+uIEiKAgI2APlb//PPPt5nqs9GJTv/ZnHxCAIGgCJAABaUmiQOBDBbQ1xjU1dXFLZDo9HEvmAkRQMA3AiRAvqkqCopAZgts2LBBJk2aJF/96ldF3xT9xhtvGJBXXnlFnn76afM2dR1uvfxS3+c0efJkM72+y07fm6ZdR9ObkfyHAAIZI0AClDFVTaAI+FdA3+B93nnnSUFBgUl+9NUR+u6szZs3m/c6HXXUUdKtWzfzAkt9u/oDDzwgV1xxhRx33HFy1VVXiT5O/8ILL5R169ZFnd6/MpQcAQQ6K8Cb3jorx3wIIOCagF6y0iRGX/6qL7L81re+JYMGDTJv+Na/w4YNk23btpnkSAu1e/du+eUvf9n6clJNhvr06WPewH7ddde1m961QFgRAgh4RoAEyDNVQUEQQKAjAU1wjj/+eBk4cKCMGTPGvFldL2v16tUr6iyaKO3atUueeeYZc5borbfekvr6emloaIg6PQMRQCDzBLgElnl1TsQI+E5A386tbX5+9rOfmTNBU6dONZe3VqxYETWWuXPnSnl5uZlezwZdeuml5sxR1IkZiAACGSlAApSR1U7QCPhLYOvWraKNmjXxeeGFF8wlLr30df/995tAtE2Q1emZnhtuuEHuuusu0+ZHk6FLLrnEzNPc3Nxuems+/iKAQGYJkABlVn0TLQK+FZg4caI89dRTcvjwYdGHGOrdXnpZTLvS0lJzyUsTJe300lhFRYVowqNth37wgx/IwYMHWy+BhU/f1NRk5uE/BBDILAESoMyqb6JFwJcCJ5xwgvziF7+Qm2++WUpKSszlrVNPPdX0a0DnnnuuZGdny4knnija3ufnP/+5PPHEE+ayV9++fUUvoY0fP170VvrI6devX2+G8R8CCGSWQFZLqMuskIkWAQT8LKBndnr06GFuiY+Mo6qqyoyzhu/YsUM0AcrJiX6/R+T01nz8RQCB4AuQAAW/jokQAQQQQAABBCIEuAQWAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIUACFAFCLwIIIIAAAggEX4AEKPh1TIQIIIAAAgggECFAAhQBQi8CCCCAAAIIBF+ABCj4dUyECCCAAAIIIBAhQAIUAUIvAggggAACCARfgAQo+HVMhAgggAACCCAQIfD/mZvH0CqCLGIAAAAASUVORK5CYII=" 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RFAAAEEEEDAdwIEQL5rMgqMAAIIIIAAAnYFCIDsCjI/AggggAACCPhOgADId01GgRFAAAEEEEDArgABkF1B5kcAAQQQQAAB3wkQAPmuySgwAggggAACCNgVIACyK8j8CCCAAAIIIOA7AQIg3zUZBUYAAQQQQAABuwIEQHYFmR8BBBBAAAEEfCdAAOS7JqPACCCAAAIIIGBXgADIriDzI4AAAggggIDvBAiAfNdkFBgBBBBAAAEE7AoQANkVZH4EEEAAAQQQ8J0AAZDvmowCI4AAAggggIBdgWy7C2B+BBBIPYHs7GzJysqS3Nzcbqlcc3OzVFVVdcu6WSkCCKSHAAFQerQztUQgIYHMzEzZunWrbNu2LaH5nMg8dOhQOfroo51YFMtAAAEEOhUgAOqUhgkIpLfAhg0b5IknnnAdYdq0aQRArquzQgTST4A+QOnX5tQYAQQQQACBtBcgAEr7TQAABBBAAAEE0k+AACj92pwaI4AAAgggkPYCBEBpvwkAgAACCCCAQPoJEAClX5tTYwQQQAABBNJegAAo7TcBABBAAAEEEEg/AW6DT782p8YJCOjzcLojddd6u6OurBMBBBDoDgECoO5QZ52+ECgpKZGCggJflJVCIoAAAggkJkAAlJgXudNIQM/CvPfee7Jo0SLXaz169Gg5++yzXV8vK0QAAQTSRYAAKF1amnp2SeDAgQOyZs2aLs1rZ6bBgwfbmZ15EUAAAQRiCHRPB4cYhWIyAggggAACCCCQTAECoGTqsmwEEEAAAQQQ8KQAAZAnm4VCIYAAAggggEAyBQiAkqnLshFAAAEEEEDAkwIEQJ5sFgqFAAIIIIAAAskUIABKpi7LRgABBBBAAAFPCrgWADU1Ncnrr78ub7/9trS1tbXD2Lt3ryxfvly2bdvWbnxDQ4OsXbtW1q1bJzo/CQEEEEAAAQQQcELAlQCooqJCJk6caAKZ559/Xq688krR4EbTpk2bZMqUKbJ9+3aZPXu2LFmyxIyvq6uTyZMny6pVq2ThwoVmWnjgZDLyHwIIIIAAAgggkKCAKwHQk08+KePHj5eZM2fKzTffLIMGDZIVK1aYos6fP1/mzZsn1113nTzyyCPy6KOPSmNjoyxevFhGjBhh8j/44INy6NAhWb9+fYLVIzsCCCCAAAIIINBRwJUnQU+bNk0yMjKCaz948KDoGZ7m5mbZtWuXDB061EwrKysz717avXu37NixQ8aNGxecZ/jw4bJlyxYZOXJkcNxjjz0m77zzjhnOysqSeZdfFZzmhQ+9evWKWgx91YK65OXlRc3n9YnWizuLi4u9XtSY5cvJyRGrHvqZ1D0Cuk1Z3x9tB+tz95TGmbXqdz0/P1+ys13Z7TpT6AhL0Xro/tbv3w9rv1VUVNShW0aEant6lG5T+i+ZV0lqA9/DSB1RcnNyJSM31xEf65iY69DymjOzopbLlW9iaGVWrlxpgp4LLrhA9uzZIz179mwXHOkLKPfv3y962cw6EGkN9LMGS6FJ+w7t3LnTjNLGDw2yQvN112fdSURL1hcwWh4/TLPcY9XXL3Wx6mHVyw/lTsUyhraD9dnv9bSCBz/XQ+uQKvXQdtBtK5mBgxttbR1LklmPzvaH1vbgZD07W1ei6whsqlGTKwGQVYKlS5ea/jx33323FBYWSm1trbS0tFiTzV89K6S/knSjDJ2m43v06NEu76xZs9oN1772Vrvh7h6o2rcvahH0zI9uuHo2zM9J26t3796i781qbW31c1VEA/Dq6mpTD60TqXsEdDvSH0KarG2re0ri3Fr1DLd+12tqapxbaDcsSc/86I9a3X/7OWkd+vTpI5WVleZqhJ/romexrKsqSatHoGtKpNTQ2ODYyQfdtvTYb/URjrS+RMa15UUPcaJPTWRNMfI+/vjj5k6v++67T3RHoEk3Pv0SaWWty0C60xswYID07ds3uAPUvDr+6KOP1o8kBBBAAAEEEEDAloArnaBffPFFefnll+U3v/lNMPjRUutlK+3orGeGNK1evdr82tNffKNGjZJly5ZJfX296KUuvRV+2LBhJh//IYAAAggggAACdgRcOQOkd3Z98skn5k4wq7ATJkyQH/7whzJ9+vTg7e96OWjOnDkmy5gxY8wzgCZNmmQuE+lt9OXl5dbs/EUAAQQQQAABBLos4EoA9NRTT3VawIEDB5pb3vU6bOidHnp2aO7cuaY/hvb90WESAggggAACCCDghIBnoorQ4Ce0Ytq5i4QAAggggAACCDgp4EofICcLzLIQQAABBBBAAAG7AgRAdgWZHwEEEEAAAQR8J0AA5Lsmo8AIIIAAAgggYFeAAMiuIPMjgAACCCCAgO8ECIB812QUGAEEEEAAAQTsChAA2RVkfgQQQAABBBDwnQABkO+ajAIjgAACCCCAgF0BAiC7gsyPAAIIIIAAAr4TIADyXZNRYAQQQAABBBCwK0AAZFeQ+RFAAAEEEEDAdwIEQL5rMgqMAAIIIIAAAnYFCIDsCjI/AggggAACCPhOgADId01GgRFAAAEEEEDArgABkF1B5kcAAQQQQAAB3wkQAPmuySgwAggggAACCNgVIACyK8j8CCCAAAIIIOA7AQIg3zUZBUYAAQQQQAABuwIEQHYFmR8BBBBAAAEEfCdAAOS7JqPACCCAAAIIIGBXgADIriDzI4AAAggggIDvBAiAfNdkFBgBBBBAAAEE7AoQANkVZH4EEEAAAQQQ8J0AAZDvmowCI4AAAggggIBdAQIgu4LMjwACCCCAAAK+E4g7ABo7dqzU1NR0qOCCBQvkiiuu6DCeEQgggAACCCCAgFcFsqMVbP369bJixQqT5dVXX5U77rhD8vPzg7O0tLTIs88+K4MHDw6O4wMCCCCAAAIIIOB1gagB0KBBg2TmzJnS0NAgTU1N8sILL0hWVlawTjk5OXLsscfKT37yk+A4PiCAAAIIIIAAAl4XiBoA9enTR9auXWvqcNFFF8miRYuksLDQ63WifAgggAACCCCAQFSBqAFQ6JxLly41Z4J27txp/oZO06DoyCOPDB3FZwQQQAABBBBAwLMCcQdATz/9tFx99dVSWVnZoTKXXXaZ/PnPf+4wnhEIIIAAAggggIAXBeIOgKZPny4XX3yxXHvttVJaWtquLlwWa8fBAAIIIIAAAgh4XCCuAKiqqkr27Nkjd955pxxxxBEerxLFQwABBBBAAAEEogvE9Ryg4uJiKS8vl40bN0ZfGlMRQAABBBBAAAEfCMR1BkjrMW/ePLn++utl165dJhjKzj48q54VOvnkk31QXYqIAAIIIIAAAgiIHI5iYmhoH6CDBw/KtGnTOuSkE3QHEkYggAACCCCAgIcF4g6APvnkE2lra4tYldCHI0bMwEgEEEAAAQQQQMBDAnEHQPv37+80AOrRo4f07t3bQ9WiKAgggAACCCCAQOcCcQdAQ4YMMZfAIi3KK5fAcnNzIxWv28bFCgozMzMlIyOj3fvVuq2wNlas9dBUUlJiYynemFX7tln10Fe9kLpHQLcp6/uj7WB97p7SOLNW67vu9+1K66Ht47X9baKtpPXQpDf5dHZ1I9Fldld+3W/pdpXMetQEjq9NESqYm5MrGQ4de61jolPbVkvIq7siFD3+PkD6MlR9+amVqqur5R//+Ic88MAD5vZ4a3x3/m1sbOzO1XdYd9WBAx3GhY7Iy8szO5K6urrQ0b77rC/I1Q1W+4i1trb6rvyhBdbgR7dtrUcqHHRD6+anz+p/4LPvj7aD9dlPdQgva1lZmdTX10tNTU34JF8N64FWv++1tbW+Knd4YbUOug/Wx7w0NzeHT/bVcFFRkehxJKn16OT42tjUKBmf/Qi2i6bblnapcepY3pYf/Uds3GeATjnllA51+9KXviSHDh2S22+/XX772992mM4IBBBAAAEEEEDAiwJxPQcoWsH1HWDvvPNOtCxMQwABBBBAAAEEPCUQ9xmgN954o93pNT3VVlFRIT/72c/kvPPO81SlKAwCCCCAAAIIIBBNIO4AaNy4cRE7QetlsLlz50ZbB9MQQAABBBBAAAFPCcQdAO3cubNDD/OePXtK6BOhPVUzCoMAAggggAACCHQiEHcApLcKVlZWyqJFi2T79u3Sv39/GTZsmIwePdr3t0N2YsNoBBBAAAEEEEhRgbgDoH/+85+il8H0idBDhw6VTz/9VD7++GM5//zzZcmSJb5/lk2Kti/VQgABBBBAAIEIAnHfBXbNNdfIF7/4Rfnwww/l7bfflt27d5vnAG3dulXuvffeCItmFAIIIIAAAggg4E2BuAIgfdbPxo0bzR1fRx11lKmJPkVTL4HNnDlTVq5c6c3aUSoEEEAAAQQQQCCCQFwBkD6eWh+xrYFQeNKngSb16ZPhK2QYAQQQQAABBBCwKRBXAKSvOtBn/dxwww2yYcMGEwzpI92XLVsmv/71r2Xs2LE2i8HsCCCAAAIIIICAewJxBUBanIceesh0gD7rrLNE32lTWloqF154oZxxxhkya9Ys90rMmhBAAAEEEEAAAZsCcd8FNnjwYHnzzTflpZdeEu34rGeFTj/9dDnnnHNsFoHZEUAAAQQQQAABdwXiDoC0WPrmXD3rM378eFPKSH2C3C0+a0MAAQQQQAABBBIXiPsSmD7zZ8qUKfLYY4+ZtWjn5/LycrnnnnvoBJ24O3MggAACCCCAQDcKxB0AXX/99fL+++/L2WefbYrbo0cPmT9/vukEvXTp0m6sAqtGAAEEEEAAAQQSE4jrElhjY6MsX75cNm/eLAMGDDBr0FvjJ02aJNXV1eb1GJdcckliayY3AggggAACCCDQTQJxnQHShx42NTWZpz+Hl7Oqqkr2798fPpphBBBAAAEEEEDAswJxBUA5OTnyla98RW688UbzKgyrNvp0aO0DpO8DIyGAAAIIIIAAAn4RiCsA0sosWLBA9OGHxxxzjJSUlIj2ATrzzDPNAxL1dRgkBBBAAAEEEEDALwJx9QHSyuiDD9esWSPbtm2Tt956S7QP0KmnnionnXSSX+pKORFAAAEEEEAAASMQdwCkuTXoGTJkiPmHHwIIIIAAAggg4FeBhAIgv1aScvtXQPufaeDtVsrKyjIP/GxtbXV1vW7Vj/UggAACCPxPgACILcGzAnr3Yd++fV0vnz7xnIQAAgggkNoCBECp3b4pUbsHH3xQ1q1b53pd7r//ftfXyQoRQAABBNwRIAByx5m12BDQZ03t3bvXxhK6NqteBiMhgAACCKSmgHudK1LTj1ohgAACCCCAgA8FCIB82GgUGQEEEEAAAQTsCRAA2fNjbgQQQAABBBDwoQABkA8bjSIjgAACCCCAgD0BAiB7fsyNAAIIIIAAAj4UIADyYaNRZAQQQAABBBCwJ0AAZM+PuRFAAAEEEEDAhwIEQD5sNIqMAAIIIIAAAvYECIDs+TE3AggggAACCPhQgADIh41GkRFAAAEEEEDAngABkD0/5kYAAQQQQAABHwoQAPmw0SgyAggggAACCNgTIACy58fcCCCAAAIIIOBDAdcDoBUrVkhzc3M7Kn3T9/Lly2Xbtm3txjc0NMjatWtl3bp10tTU1G4aAwgggAACCCCAQFcFXA2Ann76abntttukpaUlWN5NmzbJlClTZPv27TJ79mxZsmSJmVZXVyeTJ0+WVatWycKFC820tra24Hx8QAABBBBAAAEEuiqQ3dUZE5lPz/jMmTNH9u3b12G2+fPny7x58+S0006Tyy+/XK6++moZP368LF68WEaMGCEzZsww80ybNk3Wr18vI0eO7LAMRiCAAAIIIIAAAokIuBIA6RkfDVwuvPBCGT16dLB8Ghjt2rVLhg4dasaVlZVJQUGB7N69W3bs2CHjxo0L5h0+fLhs2bKlXQB07733yoYNG0ye7Oxs+d0Pbgjm98KH0tLSqMXIzMyUjIwM6dGjR9R8Xp+o9dDUq1cvrxeV8vlEQLcp6/uTk5MT/OyT4kcspvVdz83NjTjdLyOt/VZeXp5fihyxnNoemkpKSsTvVxf0+KfbVTLrURNYfmMEybzcPMlwaFvQNtF/Tm1bzVlZEUp8eJQrAZBW5qKLLjq81s8+7dmzR3r27GkqbE3UjXH//v1SUVEhxcXF1mjzWYOl0FRYWCi9e/c2o3QDSGbjh6433s+tra1Rs2pDa5lj5Yu6EA9NTJV6eIg0rYtibU+p9B1JlbpoEGS1j183UuuHm9bDa8eORE21/MnetjozMuMd6p5iBaWdrStxl+hzuBIAdVaErEB0FtofSPPpWaH8/HwJn6bjw8+UfPe732236NrX3mo33N0DVZWVUYuggaF+CbW/k5+TtpfWpaqqytGdon4Zwtvcz06UPX4BPShVfvb90R851uf4l+C9nHqGu76+XmpqarxXuARKpGfk9GxDbW1tAnN5L6vWQfdb1dXVHW7M8V5po5eoqKjIHEf0OJm01MmNSI1NjZLx2VUAu+vWbUuP/Y2Nkc41Jb70tvycqDO52gk6vCR9+vQxXyK928tKevZnwIAB0rdvX3MmKHy8NcxfBBBAAAEEEECgqwLdGgDpZSvt6Lx06VJT/tWrV5tLWvqLb9SoUbJs2TLzi0lvk9db4YcNG9bVejIfAggggAACCCAQFOjWS2BaiunTpwdvf9fLQXq3mKYxY8aYZwBNmjTJXCaaOHGilJeXm2n8hwACCCCAAAII2BFwPQBas2ZNu/IOHDjQ3PKu1/hD7yLSs0Nz584112e1H4gOkxBAAAEEEEAAAScEPBNVhAY/oRXTzl0kBBBAAAEEEEDASYFu7QPkZEVYFgIIIIAAAgggEK8AAVC8UuRDAAEEEEAAgZQR8MwlsJQRpSIIIGBL4LjjjhN9Hog+N8dKoZ+tccn6qw9h09f2hD+jLFnrY7kIINA9AgRA3ePOWhFAoBMBfTr8oUOHZNGiRZ3kSN5ofTbZhAkTzJ2nBEDJc2bJCHhBgADIC61AGRBAoJ2APmX4mWeeaTfOjQE9+6QBEAkBBFJfgD5Aqd/G1BABBBBAAAEEwgQIgMJAGEQAAQQQQACB1BcgAEr9NqaGCCCAAAIIIBAmQAAUBsIgAggggAACCKS+AAFQ6rcxNUQAAQQQQACBMAECoDAQBhFAAAEEEEAg9QUIgFK/jakhAggggAACCIQJEACFgTCIAAIIIIAAAqkvQACU+m1MDRFAAAEEEEAgTIAAKAyEQQQQQAABBBBIfQECoNRvY2qIAAIIIIAAAmECBEBhIAwigAACCCCAQOoLEAClfhtTQwQQQAABBBAIEyAACgNhEAEEEEAAAQRSX4AAKPXbmBoigAACCCCAQJgAAVAYCIMIIIAAAgggkPoCBECp38bUEAEEEEAAAQTCBAiAwkAYRAABBBBAAIHUFyAASv02poYIIIAAAgggECZAABQGwiACCCCAAAIIpL4AAVDqtzE1RAABBBBAAIEwAQKgMBAGEUAAAQQQQCD1BQiAUr+NqSECCCCAAAIIhAkQAIWBMIgAAggggAACqS9AAJT6bUwNEUAAAQQQQCBMgAAoDIRBBBBAAAEEEEh9AQKg1G9jaogAAggggAACYQIEQGEgDCKAAAIIIIBA6gsQAKV+G1NDBBBAAAEEEAgTIAAKA2EQAQQQQAABBFJfgAAo9duYGiKAAAIIIIBAmAABUBgIgwgggAACCCCQ+gLZqVTF3NxcT1Wnd+/eUcuTmZkpGRkZkp+fHzWf1ydqPTSVlJR4vaiUD4G4BIqKiqStrS2uvPFmsr7rOTk58c7iyXxaD/3Oe21/myiW1kNTcXGx422daFns5s/OzhbdrpzeZkPLVRM4vjaFjvjsc25OrmQ4dOy1jolObVstWVkRSnx4VEoFQI2NjYdr5oFPVQcORC1FXl6e2ZHU1dVFzef1iRrA6QZ78OBBaW1tday4uoPq37+/Y8tjQQjEK1BdXS1NTZF29/EuoWO+srIyqa+vl5qamo4TfTRGD7T6fa+trfVRqTsWVeug++Cqqippbm7umMFHYzRg1+NIUuvRyfG1salRMj77EWyXTLetrEDQ4tSxvC0/+o8NLoHZbTHmRwABBBBAAAHfCRAA+a7JKDACCCCAAAII2BUgALIryPwIIIAAAggg4DsBAiDfNRkFRgABBBBAAAG7AgRAdgWZHwEEEEAAAQR8J0AA5Lsmo8AIIIAAAgggYFeAAMiuIPMjgAACCCCAgO8ECIB812QUGAEEEEAAAQTsChAA2RVkfgQQQAABBBDwnQABkO+ajAIjgAACCCCAgF0BAiC7gsyPAAIIIIAAAr4TIADyXZNRYAQQQAABBBCwK0AAZFeQ+RFAAAEEEEDAdwIEQL5rMgqMAAIIIIAAAnYFCIDsCjI/AggggAACCPhOgADId01GgRFAAAEEEEDArgABkF1B5kcAAQQQQAAB3wlk+67EFNhVgZycHCksLIy6zszMTGltbZWSkpKo+ZiIAAIIIICAVwQIgLzSEh4tR25urui/devWuV7C/Px8OeOMM1xfLytEAAEEEEh9AQKg1G9j2zXUszvz5s2zvZxEF9C/f39ZsGBBorORHwEEEEAAgZgC9AGKSUQGBBBAAAEEEEg1AQKgVGtR6oMAAggggAACMQUIgGISkQEBBBBAAAEEUk2AACjVWpT6IIAAAggggEBMAQKgmERkQAABBBBAAIFUEyAASrUWpT4IIIAAAgggEFOAACgmERkQQAABBBBAINUEeA5QqrUo9UEAgS4L6NPM9blXpaWl0tbW1uXldDZjz549paCgoLPJkpGRITU1NVJbW9tpHiYggIAzAgRAzjiyFAQQSAGBoqIi0Ve7PP/881JRUeF6jSZMmCD6BHQSAggkX4AAKPnGrAEBBHwm8Pe//102b97seqnHjh1LAOS6OitMVwH6AKVry1NvBBBAAAEE0liAACiNG5+qI4AAAgggkK4CBEDp2vLUGwEEEEAAgTQWoA+QDxq/sLBQcnJyuqWkWVlZ3bJeVooAAggggEAyBQiAkqnr0LI1+KmsrJQdO3Y4tMT4FzNkyBDRW4NJCCCAAAIIpJIAAZBPWvPNN9+U++67z/XS3nLLLXLmmWe6vl5WiAACCCCAQDIF6AOUTF2WjQACCCCAAAKeFCAA8mSzUCgEEEAAAQQQSKYAAVAydVk2AggggAACCHhSwNN9gBoaGmTjxo3m/TjaD6W77oTSlsvOzjblSKQV9bH60ZLeYaXv/tFlR0v6aH4SAggggAACCDgnEP3I69x6El5SXV2dTJ06VU4++WT56KOP5Mknn5S777474SAk4RVHmEGDlNzcXGlsbEzoBYnNzc0RlnZ4VEtLixmI9dLFvLy8wzPxCQEEEEAAAQRsC3g2AFq8eLGMGDFCZsyYYSo5bdo0Wb9+vYwcOdJ2pRNdgAZAmvRslN6OHm+6b85f480aNd+SJUuiTmciAggggAACCCQmkBE4+9CW2Czu5L755ptl3Lhxcu6555oVPvzww+YSmJ4VstJtt90ma9asMYN6Gekvt99jTXL8r16Gqq+vl9bW1riXXXPckXHnjZaxT58+0tTUJFVVVdGyJWWaPgNI3079ySefJGX50Raq5v369ZODBw8a+2h5kzGtb9++omfxEgl6nSqHPvyyZ8+esnfvXrHOFDq17HiWo3XXs7C1tbXxZHc0T2lpqXkju9bd7aTbum7z+/fvN985t9ev27v1g8vtdafj+tTao4dAzzVH5RXfkfon/tyhXEe89oZkBL43dpLVBta2bw3bWabO21yQJ3knlne6GM+eAaqoqJDi4uJgwfXzrl27gsP6QS+PWU8q1r/1JxzdbrqTA9r/KPuzM0HxLrc0RsZEGlvXrweG7kgaBERbt9ZDA1AN0pxOevAvKCgw/5xedqTladBlBbn6JdTtKlrdIy3DqXFa964+hFLroe1iJ3jSS6/dcflVy632lru2gZ16JNoeuq5Y/fcSXabm1++IblvW9hVpGdY0pw4AkdYRbZxlHyuPTne6jKHfvWjrd2qatd/S/Zt+1uR0neItq92625k/3nlbOzlX0nJSuWT06BFvVTvk023e6i6iXU3MsTzw48uJpN/laB1IPBsAhe/0FKhHGPKECRPaGX388cfthr0+oAcX3fj0l7afk/5q7t27t/nVbO3A/VofDTiqq6ujHqT8UDethx5w9+3b54fiRi2jblsHDhyImscPE8vKyuTQoUNSU1Pjh+J2Wkb9MaYHqu44O9hpobowQeugZ9f1DK91AO7CYjwxiwbsehxJaj0CNyVFSvv2BvYxPeydAbKWq/XQ44lTVzti/YDz7O1FegpeT0NbST8PGDDAGuQvAggggAACCCDQZQHPBkCjRo2SZcuWmb4f2hdg3bp1MmzYsC5XlBkRQAABBBBAAAFLwLOXwMaMGSNr166VSZMmmctEEydOlPLyzjszWRXiLwIIIIAAAgggEEvAswGQ9l+YO3eu6Y+hfX90mIQAAggggAACCDgh4PmoIhl3YzgBxzIQQAABBBBAwL8Cnu0D5F9SSo4AAggggAACXhcgAPJ6C1E+BBBAAAEEEHBcgADIcVIWiAACCCCAAAJeFyAA8noLUT4EEEAAAQQQcFyAAMhxUhaIAAIIIIAAAl4X8PxdYIkA+u1Weev9M34rd3ib6CPx9TUk+u4mv9dF3x2jr2HRV5T4OenLa/Wx+L169fJzNUzZtU38vl1pRbZv327eaRf+Sh+/NZB+N/SdWX5vk4bAqx22bNliXuPj97roK4i0XZJZj9bTT5O2CK9tyszLlQyHHlPz6aefSmNjY/A9gHa/G7ovj5Y8+zb4aIVmmrcE/va3v8l1110na9askSOOOMJbhUvT0syePVv++9//ylNPPZWmAt6r9hlnnCFTp06V6dOne69waViiDRs2yLe//W157rnn5POf/3waCnivynfccYfo8WTlypWuFM7fP3NdIWIlCCCAAAIIIJBqAgRAqdai1AcBBBBAAAEEYgpk/V8gxcxFBgSiCGhfpr59+8qZZ54pubm5UXIyyS0B7Q8waNAgOemkk9xaJeuJIaD9Eb7whS/IgAEDYuRkshsCut/SPnJnnXWW6ZvlxjpZR3QB3W8de+yxMnTo0OgZHZpKHyCHIFkMAggggAACCPhHgEtg/mkrSooAAggggAACDgmk1G3wDpmwmDgEduzYIR9++KGMHDlSOrutd9OmTaK3mlrphBNOcOz2RmuZ6f5XfTdu3Ch6Ol8vQebk5EQkiTdfxJkZmZDAgQMH5M0335STTz5ZjjzyyIjz1tTUyLvvvttumn6XSM4L7Ny5Uz766KPggvv06SODBw8ODod+2LZtm3zwwQcyfPhwc1k/dBqfnRHQR6a8//777Ram+69I23+yjyFcAmvXDAzEIzBz5kzzrJzjjz9eVq1aJbNmzTLX0UPn1WfQnH/++abPgzVebzk99dRTrUH+2hSoCzyTQ2+r1gOt7uDz8vLk7rvvNsFQ6KLjzRc6D5+7JqC3VOujB84991wTBOnt1TNmzOiwMP3e3H///aafljXxzjvvtD7y10GBOXPmiD4Xy3omlvYvufLKKzus4Z577pHNmzeb4GjdunVy3333yTHHHNMhHyPsCejjUv7yl78EF6L7roMHD8rSpUuD4/SDK8eQwAOtSAjELfDOO++0BQKZYP7A8xrarr/++uCw9eHf//5321VXXWUN8jcJAn/4wx/aAjvt4JKvueaattdeey04bH2IN5+Vn79dEwjssNsmTJjQFnj+kllA4AGhbePHj2/bv39/hwU+/PDDbQsWLOgwnhHOC0ycOLEtcFYn6oLfe++9tm9+85ttgYdumnx/+tOf2m6//fao8zDRvkB9fX1bIBhte/XVVzsszI1jCH2A2sWcDMQS0LuKHnnkkWA2jdwDG3Fw2PoQ2HjlqKOOkr/+9a/mQWOHDh2yJvHXIQG9DKmn6q2kn/XJtuEp3nzh8zGcmIDe5fXYY49JeXm5mVG/F3qpS59kHZ70+1FYWCiLFi0SfSBfYO8fnoVhBwR0vxMIQEWfMLxw4ULZtWtXxKXqQ0P1zJDehaSps+9SxJkZ2WWB3//+9+aqwDnnnNNhGW4cQwiAOrAzIpqA7iCsPj979uyRxx9/XL7zne90mEUf+6/X06urq83fyy+/XPbu3dshHyO6LlBRUSHFxcXBBejnffv2BYetD/Hms/Lzt+sCPXv2NDPrqwnuvfdecxlYHxERnnTn/vrrr5vLln/84x/lxz/+cXgWhh0Q+M9//mP6IWqQqYGoXo584YUXOixZ+6WUlJQEx3f2XQpm4INtAf3x/Mwzz8jkyZMjLsuNYwidoCPSM9ISWLFihWzdutUM6q8iK1IPnDKWG264QaZMmRKx89r3vvc90X8FBQVmXu2Eq2eDtB8QyRkBPeMQenZBr5lbwWnoGuLNFzoPn7suoNv6bbfdZs7q/PSnP424oEcffdT0SdEfFF//+tflG9/4hjk7oWdNSc4JDBkyRJYsWWLe96VL1WdjqX3g0mS7lfAdacfhyoAeD/QZTGVlZRHX58YxhDNAEekZaQloAKO/jPRffn6+Gf2vf/1LAv1+5Pvf/7587Wtfs7K2+7t79+52d4BpZ0L9lUVyTkDPLOjpfSvp50gP2Ys3n7Uc/nZdQC+5/OhHP5KioiKZO3duxAeD6sse9U4j63KLPjxUDwJ6po7krEBlZaXoXXlW0v2QdojWM3ShqV+/fh2+S53dwRc6H5+7LvDiiy/KxRdf3OkC3DiGEAB1ys8EFdAzPnqJS//pU2z1Mpa+aPOWW26RL3/5y+2Q9G4j3bFrWr16tTz00EPmsx4U9OV2o0ePNsP854zAqFGjZNmyZaYPlraL3rkybNgws3Adti45RsvnTElYiiWg34sTTzxRbrzxRnOnpDVe/+qtv9ovSB9VoHfr6SUwTfqDQi9dnn766WaY/5wT0Mss+mNN903az0rvPtL9lgafofsrfYSEPpZAH+2hZ1Kff/75Dne2OlcqlqRnSfX7cMopp7TDCN1vuXEM4Tb4dvwMxBJ48MEHJXCHRLtbrUtLS+XZZ581z6PRU/96O2NVVZXobb16i6N2QBw7dqx5Y7z1qzfWepgeW0B31LfeeqvZcatr4G4Xueyyy8yMd911lznQap+HaPlir4Uc8QpoIBO4E89k1+eaWElvd9cOthdccIHo265PO+00c4u83kzQ1NRkvh8aMFmXl635+OuMgHZMf+mll8z3QPv26Jm5I444ot3+StekwZHe+q77s4EDB8q8efMkO5teIs60QvulaB+4m266yTwyInRK6H7LjWMIAVCoPp8dF9CzP3p9XZ9RQ0qOgHY0174/sXbW8eZLTilZaiQBPUOhB+XQgClSPsbZE9BLXnpHnlpHSxqQ6tkJvUOP5A2BZB5DCIC80caUAgEEEEAAAQRcFKAPkIvYrAoBBBBAAAEEvCFAAOSNdqAUCCCAAAIIIOCiAAGQi9isCgEEEEAAAQS8IUAA5I12oBQIIIAAAggg4KIAAZCL2KwKAQQQQAABBLwhQADkjXagFAggYFNAnxuSSEo0fyLLJi8CCHhfgADI+21ECRFAIIaAPlb//PPPj5Hr8ORE8x+ek08IIJAqAgRAqdKS1AOBNBbQ1xjU1tbGLZBo/rgXTEYEEPCNAAGQb5qKgiKQ3gKbNm2SKVOmyFe/+lXRN0W/8cYbBuSVV16Rp59+2rxNXcdbL7/U9zlNnTrV5Nd32el70zR1lt9M5D8EEEgbAQKgtGlqKoqAfwX0Dd7nnXee5Ofnm+BHXx2h787aunWrea/TUUcdJQUFBeYFlvp29QceeECuuOIKOe644+Sqq64SfZz+hRdeKBs2bIiY378ylBwBBLoqwJveuirHfAgg4JqAXrLSIEZf/qovsvzWt74lQ4YMMW/41r8jRoyQHTt2mOBIC7Vnzx751a9+FXw5qQZD/fr1M29gv+666zrkd60irAgBBDwjQADkmaagIAgg0JmABjjHH3+8DB48WMaNG2ferK6Xtfr06RNxFg2UPv74Y3nmmWfMWaK3335b6urqpL6+PmJ+RiKAQPoJcAks/dqcGiPgOwF9O7f2+fn5z39uzgRNnz7dXN5atWpVxLrMnz9fysvLTX49G3TppZeaM0cRMzMSAQTSUoAAKC2bnUoj4C+B7du3i3Zq1sDnhRdeMJe49NLX/fffbyqifYKspGd6brjhBrnrrrtMnx8Nhi655BIzT2tra4f81nz8RQCB9BIgAEqv9qa2CPhWYPLkyfLUU09JS0uL6EMM9W4vvSymqbS01Fzy0kBJk14aq6ioEA14tO/QD37wA2loaAheAgvN39zcbObhPwQQSC8BAqD0am9qi4AvBU444QT55S9/KTfffLOUlJSYy1unnnqqGdYKnXvuuZKVlSUnnniiaH+fX/ziF/LEE0+Yy179+/cXvYQ2ceJE0Vvpw/Nv3LjRjOM/BBBIL4GMtkBKrypTWwQQ8LOAntnp1auXuSU+vB6VlZVmmjV+165dogFQdnbk+z3C81vz8RcBBFJfgAAo9duYGiKAAAIIIIBAmACXwMJAGEQAAQQQQACB1BcgAEr9NqaGCCCAAAIIIBAmQAAUBsIgAggggAACCKS+AAFQ6rcxNUQAAQQQQACBMAECoDAQBhFAAAEEEEAg9QUIgFK/jakhAggggAACCIQJEACFgTCIAAIIIIAAAqkvQACU+m1MDRFAAAEEEEAgTOD/AZieCrosOvSzAAAAAElFTkSuQmCC" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -225,9 +225,9 @@ <h3>One numerical variable (median)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" 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Ciz25faIYAAAggggEAMAQKgGChMQgABBBBAAIHMFiAAyuz2pXYIIIAAAgggEEOAACgGCpMQQAABBBBAILMFCIAyu32pHQIIIIAAAgjEECAAioHCJAQQQAABBBDIbIH8dFWvublZ3nnnHcnJyZEzzjhDCgoKgps+cOCAbNmyRU444QQZOXJkcLqus3nzZrPOuHHjwtYJLsQAAggggAACCCDQTYG0HAFqaGiQa665Rt5++21Zu3atfP/73xcNbjRt3bpVpk+fLtu3b5fZs2fL8uXLzfTGxkaZNm2arFq1SpYuXWrm+f1+M48/CCCAAAIIIIBATwTScgRoxYoV5sjOzTffbMq6ZMkSqampkdGjR8vChQtlwYIFcvrpp8ull14qM2bMkMmTJ8uyZcukqqpKbrjhBrOOBlAbN26U8ePH96S+rIsAAggggAACCEhaAqBNmzbJhRdeKBs2bBCfzydXXnml5ObmSltbm+zatcsEQtoWgwcPltLSUtm9e7fs2LFDqqurg000duxY2bZtW1gA9OCDD8q7775rlsnPz5d7f/CT4PJpHeg4rReZ8jrK06dfv8jJjPdAQNtYX9lwJFD/P5xOau2GcjjtYGv7allYWGgrO/IJEdCuFZrwDUGxOKi+gZfFbFOelS8vL+420hIAffbZZ/KnP/1JBg4cKK2trbJo0SJ5+OGH5fDhw1JWVmZgA6UsLy+XQ4cOyd69e6VPnz6ByWZYg6XQ1N7eLvrSpIEVCQEEEEAAAQQQSEQgLQGQBifDhw+XWbNmmTLdcsst8tZbb8mYMWOiAhc9KlRcXCx5HZFbaFCj00tKSsLqNHPmzLDx+vXvhY2nbSRG3yRfR3k1wCPZE9CAuL6+Pmy/sJe7u3IqKiqSiooKRwul/3P6ItkR0KMTLS0tdjIjlzAB/b7QF75hLNZG9OiPHhHWAxheSv7i/15sFavcaTnOPmjQIBk1alRw+yNGjDCns/r372++0AIdonUBPfozZMgQGTBggBkOrBSYHhjnHQEEEEAAAQQQSFYgLQHQWWedJa+//ro5XaURul4Nph2gNaLUjs7aSVrT6tWrpV9Hvxl9TZw4UVauXClNTU2il8mvW7fOHDFKtqKshwACCCCAAAIIBATScgrsggsuMEd8Ape/T5gwQSZNmmTKoKexApe/ayfBefPmmek6Xy+Znzp1qumIOWXKFKmsrAyUm3cEEEAAAQQQQCBpgbQEQHrTw7lz54reD0iDHO3jE0jDhg0zl7zX1tZK3759A5PN0aH58+dLXV2d6fujR4tICCCAAAIIIICADYG0RhV6iXtnKTT4CV2md+/eoaMMI4AAAggggAACPRZISx+gHpeSDBBAAAEEEEAAAYsCBEAWMckKAQQQQAABBLwhQADkjXailAgggAACCCBgUYAAyCImWSGAAAIIIICANwQIgLzRTpQSAQQQQAABBCwKEABZxCQrBBBAAAEEEPCGAAGQN9qJUiKAAAIIIICARQECIIuYZIUAAggggAAC3hAgAPJGO1FKBBBAAAEEELAoQABkEZOsEEAAAQQQQMAbAgRA3mgnSokAAggggAACFgUIgCxikhUCCCCAAAIIeEOAAMgb7UQpEUAAAQQQQMCiAAGQRUyyQgABBBBAAAFvCBAAeaOdKCUCCCCAAAIIWBQgALKISVYIIIAAAggg4A0BAiBvtBOlRAABBBBAAAGLAgRAFjHJCgEEEEAAAQS8IUAA5I12opQIIIAAAgggYFGAAMgiJlkhgAACCCCAgDcECIC80U6UEgEEEEAAAQQsChAAWcQkKwQQQAABBBDwhgABkDfaiVIigAACCCCAgEWBfIt5OZ5VXl6eQ2XIidpubm6uFJWURE1nQvIC+fn5UlxcLO3t7cln4pE1CwoKHC+p7sPO/U85Xn3rBcDTOmkwQ7XVxP4aJLE6kJOTI/rymm97TvxjPBkVAFlt8R5m5u9Y3+/XvyRbAuoZeNnK0635sO+4tWUoFwIIeEcg/ndwRgVAPp/PoXaJRvZ3HKVoampyqDyZudnCwkJpbm4W59o5fa5uCID0SFs2WKerVfXXM56p1cY3Nb6BI0Be8+3qczT+8aHUWJIrAggggAACCCDgqAABkKP8bBwBBBBAAAEEnBAgAHJCnW0igAACCCCAgKMCBECO8rNxBBBAAAEEEHBCgADICXW2iQACCCCAAAKOChAAOcrPxhFAAAEEEEDACYGEA6BzzjlHjh49GlXGRx99VC677LKo6UxAAAEEEEAAAQTcKhD3PkAbN26U1157zZT97bfflttvv93ciTdQGb0nwAsvvCDDhw8PTOIdAQQQQAABBBBwvUDcAOikk06SWbNmmZvPtba2yssvvxx2K2y9Xf8JJ5wgt9xyi+srSgERQAABBBBAAIGAQNwAqH///rJ27Vqz7Le//W158sknpVevXoF1eUcAAQQQQAABBDwpEDcACq3RihUrzJGgnTt3mvfQeRoUHXvssaGTGEYAAQQQQAABBFwrkHAA9Nxzz8mMGTOktrY2qjLf/e535emnn46azgQEEEAAAQQQQMCNAgkHQDNnzpQLL7xQfvzjH0tFRUVYXTgtFsbBCAIIIIAAAgi4XCChAOjIkSOyf/9+ueOOO2TQoEEurxLFQwABBBBAAAEE4gskdB+gPn36SGVlpWzevDl+bsxFAAEEEEAAAQQ8IJDQESCtx4IFC+TGG2+UXbt2mWAoP/+/q+pRoVNOOcUD1aWICCCAAAIIIICAyH+jmC40tA/Q559/Ltdcc03UknSCjiJhAgIIIIAAAgi4WCDhAGjfvn3i9/tjViUvLy/mdCYigAACCCCAAAJuFEg4ADp06FCnAVBJSYn069fPjfWjTAgggAACCCCAQJRAwgHQySefbE6BReXQMYFTYLFUmIYAAggggAACbhVIOADSh6Hqw08Dqa6uTt59911ZtGiRuTw+MJ13BBBAAAEEEEDA7QIJB0CnnnpqVF2+8Y1vSENDg/z617+WP/7xj1HzmYAAAggggAACCLhRIKH7AMUruD4D7G9/+1u8RZiHAAIIIIAAAgi4SiDhI0CbNm2Stra2YOF1eO/evfKrX/1Kzj777OB0BhBAAAEEEEAAAbcLJBwAVVdXx+wErafB5s+f7/Z6Uj4EEEAAAQQQQCAokHAAtHPnzqjL4MvKyiT0jtDBXOMM1NTUmLkjR44MLnXgwAHZsmWLnHDCCRI6vbm52Tx+IycnR8aNGycFBQXBdRhAAAEEEEAAAQSSFUi4D5A+D0xvhPjEE0/IL3/5S3nggQfkjTfekJaWloS3XVtbK7Nnz5b169cH19m6datMnz5dtm/fbuYtX77czGtsbJRp06bJqlWrZOnSpWZeZzdiDGbGAAIIIIAAAgggkIBAwkeAPvjgA9HTYHpH6NGjR8tnn30me/bskXPPPVc0aCkuLu5yc3feeadUVFSELbdw4ULznLHTTz9dLr30UpkxY4ZMnjxZli1bJlVVVXLDDTeY5fURHBs3bpTx48eHrc8IAggggAACCCDQXYGEA6Crr75azjzzTLnvvvtk6NCh5mjQe++9JxdddJHce++9MmfOnLjbfvHFF83donXdQNKO1PpwVQ2oNA0ePFhKS0tl9+7dsmPHDhNwBZYdO3asbNu2LSwA0u1q52xNeiru4etvDiye1ve6jlN0kUnL0yci2ItchvHuCaipngbNhiOBubkJH5ztHmI3llZvHnPTDbAuFtU2LSoq6mIpZicjoN0kNOGbjF5i66ixGz6XEivtf5Zq6+IxXQkFQHqvn82bN4seBQoEMIoxZswYmTVrlrz00ktxAyANcp5//nlZvHixLFmyJFj+/fv3i/YjCuy8OqO8vFz0sRt6hZmedgskHdZ8QlPv3r1lwIABZpJ+ULvpi1Efm9be3h5aXIZ7KKDtq6ZuauceVsn1q2Ntr4nUEk97nrFywjeWip1p+j3tNV/9Ho6XEgqANOrTimsgFJnq6+vDLo+PnK9HefRS+Z/+9KeizwwLTRq0hN5dWufp8no6LXKeTo9c/6qrrgrNTurXvxc2nraRGMo+X5tonyeSPQENgnV/i9xn7G3BPTnpL1mnf83q/5y+SHYECgsLu9Vn0s5WsyMX/b7QV3f6pGaHjJ1aavCjR4RbW1vtZJimXPzF8S+cSigA0oDk7I57/ehprttvv12+9rWviV6hpR2Uf//738t1113XaXU+/vhj+fDDD+XGG280yyigYuqVX9q/R7/QNK/Ah70e/RkyZIg5sqPDgaTDxx9/fGCUdwQQQAABBBBAIGmBhAIgzf3BBx+U73znO3LGGWfIwIED5ejRo6JXal1wwQVy0003dVqAk046Sd58883gfD0Npv18pnVc4aVJOzqvWLHCPFB19erVpp+QPll+4sSJsnLlSvOu21q3bp0JvsxK/EEAAQQQQAABBHogkHAANHz4cHOvntdff10++ugjc5rqK1/5ikyYMKEHmxeZOXNm8PJ3PdU2b948k9+kSZNk7dq1MnXqVNPxasqUKVJZWdmjbbEyAggggAACCCCgAgkHQLqwnqY6//zzzWXqOh6rT5BOj5c04AlNw4YNM5e8a3+Zvn37Bmfp+Ua9w7Q+dV77/ug4CQEEEEAAAQQQsCGQ8LW2es8fvWHhY489ZrarfXf0iMw999xjpaNkaPATWjG90ovgJ1SEYQQQQAABBBDoqUDCAZB2YtYOzV//+tfNNvWojN7EUDtBax8eEgIIIIAAAggg4BWBhM4r6aWFr776qvzjH/8wV2hp5bS/jvbP0VNUTz75pLkholcqTTkRQAABBBBAILsFEjoCpJet6+XreofmyHTkyBFz48LI6YwjgAACCCCAAAJuFUgoANLHD3zzm9+Um2++WT755JNgXfTu0NoHSJ8HRkIAAQQQQAABBLwikFAApJV59NFHpampSb70pS+Zx1VoH6Bx48aZGyTq4zBICCCAAAIIIICAVwQS6gOkldGnuK9Zs0ZqampEH4KqfYBOO+00GTVqlFfqSjkRQAABBBBAAAEjkHAApEtr0HPyySebF34IIIAAAggggIBXBRI+BebVClJuBBBAAAEEEEAgUoAAKFKEcQQQQAABBBDIeAECoIxvYiqIAAIIIIAAApECBECRIowjgAACCCCAQMYLEABlfBNTQQQQQAABBBCIFCAAihRhHAEEEEAAAQQyXoAAKOObmAoigAACCCCAQKQAAVCkCOMIIIAAAgggkPECBEAZ38RUEAEEEEAAAQQiBQiAIkUYRwABBBBAAIGMFyAAyvgmpoIIIIAAAgggEClAABQpwjgCCCCAAAIIZLwAAVDGNzEVRAABBBBAAIFIAQKgSBHGEUAAAQQQQCDjBQiAMr6JqSACCCCAAAIIRAoQAEWKMI4AAggggAACGS+Qn0k1zM93qDo5OVGMubl5UlxaGjWdCckLaPuWlJRIe3t78pl4ZE3H9uUQn9zcXHFDOUKK5OlBPFPXfGqrif01tcZe8/XlxD/G41DEkJpGcuyL0R9dH7/fLz6fL3oGU5IWCJg61s5Jl7z7KwY+0Lu/pt01ssHarljnuen+i2fnPj2Zk/PFj1B8e6LY+brq6839N8aXc0g1CYBCMJIfjEb2+9ulubk5+SxZM0qgqKhIWlpaCCyjZFIzQb9M+EKxZ+vNLxB79U9lTgRAqdQVUV/9Uea1zwP9n4uX4h8fircm8xBAAAEEEEAAAY8KEAB5tOEoNgIIIIAAAggkL0AAlLwdayKAAAIIIICARwUIgDzacBQbAQQQQAABBJIXIABK3o41EUAAAQQQQMCjAgRAHm04io0AAggggAACyQsQACVvx5oIIIAAAggg4FEBAiCPNhzFRgABBBBAAIHkBQiAkrdjTQQQQAABBBDwqAABkEcbjmIjgAACCCCAQPICBEDJ27EmAggggAACCHhUgADIow1HsRFAAAEEEEAgeQECoOTtWBMBBBBAAAEEPCpAAOTRhqPYCCCAAAIIIJC8AAFQ8nasiQACCCCAAAIeFSAA8mjDUWwEEEAAAQQQSF6AACh5O9ZEAAEEEEAAAY8KEAB5tOEoNgIIIIAAAggkL0AAlLwdayKAAAIIIICARwUIgDzacBQbAQQQQAABBJIXIABK3o41EUAAAQQQQMCjAgRAHm04io0AAggggAACyQsQACVvx5oIIIAAAggg4FEBAiCPNhzFRgABBBBAAIHkBQiAkrdjTQQQQAABBBDwqEDaAqDW1lbZsGGDvP/+++L3+8O4Dhw4IK+++qrU1NSETW9ubpa1a9fKunXrRNcnIYAAAggggAACNgTSEgDt3btXpkyZYgKZF198US6//HLR4EbT1q1bZfr06bJ9+3aZPXu2LF++3ExvbGyUadOmyapVq2Tp0qVmXmTgZBbkDwIIIIAAAggg0E2BtARAzzzzjEyePFlmzZolc+fOlZNOOklee+01U9SFCxfKggUL5Nprr5WHHnpIHnnkEWlpaZFly5ZJVVWVWX7x4sXS0NAgGzdu7Gb1WBwBBBBAAAEEEIgWyI+eZH/KNddcIzk5OcGMP//8c9EjPG1tbbJr1y4ZPXq0mTd48GApLS2V3bt3y44dO6S6ujq4ztixY2Xbtm0yfvz44LQ77rhD1q9fb8bz8/Nl2S8WBOelc6AupG6B7ebnF0jfAQMCo7xbEMjNzZXCwkILObk/i9D/F6dKq/9T+iLZEdA2LS4utpMZucQUwDcmi7WJeXl51vJKR0atXZQ3LZ9uoV9af/3rX03Qc95558n+/fulrKwsLDgqLy+XQ4cOiZ4269OnT9BIhzVYCk1Dhw6VUaNGmUnaMO3t7aGzHR3W03X0W7LbBAUFBSZozoZTobo/Ox18qHM2WNvdSzvPTQN4N31GdV5S780J/GDAN3Vt58X9N6K7cRROWgKgwFZXrFhh+vPcfffd0qtXL6mvrxefzxeYbd71qJBG8foFEDpPp5eUlIQtq32JQlP9+vdCR9M3HEPZ52sTPdJFsiegQXCsfcbeFtyTU1FRkejLyaT/f/p/R7IjoD8E9fQ+yb6Afl/oC1/7tpqjBpj6g8xrP+r9xQVxQdIWAD3++OPmSq/77rtP9FSXpv79+5svNO0QHfiw16M/Q4YMkQEdp490OJB0+Pjjjw+M8o4AAggggAACCCQtkJZO0K+88oq88cYb8sADDwSDHy2xRpTa0VmPDGlavXq19OvXz7wmTpwoK1eulKamJtHL5PVS+DFjxpjl+IMAAggggAACCPREIC1HgPTKrn379pkrwQKFvfjii+X666+XmTNnBi9/13OM8+bNM4tMmjTJ3ANo6tSpotP1MvrKysrA6rwjgAACCCCAAAJJC6QlAHr22Wc7LeCwYcPMJe+1tbXSt2/f4HJ6dGj+/PlSV1dn+v443SE0WDAGEEAAAQQQQMDzAmkJgBJRCg1+Qpfv3bt36CjDCCCAAAIIIIBAjwXS0geox6UkAwQQQAABBBBAwKIAAZBFTLJCAAEEEEAAAW8IEAB5o50oJQIIIIAAAghYFCAAsohJVggggAACCCDgDQECIG+0E6VEAAEEEEAAAYsCBEAWMckKAQQQQAABBLwhQADkjXailAgggAACCCBgUYAAyCImWSGAAAIIIICANwQIgLzRTpQSAQQQQAABBCwKEABZxCQrBBBAAAEEEPCGAAGQN9qJUiKAAAIIIICARQECIIuYZIUAAggggAAC3hAgAPJGO1FKBBBAAAEEELAoQABkEZOsEEAAAQQQQMAbAgRA3mgnSokAAggggAACFgUIgCxikhUCCCCAAAIIeEOAAMgb7UQpEUAAAQQQQMCiAAGQRUyyQgABBBBAAAFvCBAAeaOdKCUCCCCAAAIIWBQgALKISVYIIIAAAggg4A0BAiBvtBOlRAABBBBAAAGLAgRAFjHJCgEEEEAAAQS8IUAA5I12opQIIIAAAgggYFEg32JejmeVn+9QdXJyouqem5snJWVlUdOZkLxAQUGBlJSUiN/vTz4Tj6zp2L4c4pObmytuKEdIkTw9iGfqmk9tNbG/psY4p+M7zov7b/sX+0VnKg5FDJ0Vp2fT29vbe5ZBsmvH+D72+9ulpaUl2RxZL4aABkCtra3iWDvHKFMmT9JAE2t7LYynPcvOcmJ/7UymZ9M1ANKX13y7Ki8BUM/2iy/Wjo6A9MNOv6xJ9gR0Z25raxOfz2cvU5fmFPhF62Tx+MK2q4+nXc/Q3PTLWVNXX3ih6zCcuEDgCJDXfKO/mcPrTB+gcA/GEEAAAQQQQCALBAiAsqCRqSICCCCAAAIIhAsQAIV7MIYAAggggAACWSBAAJQFjUwVEUAAAQQQQCBcIKM6QYdXzemx/1w2aKsUXut8Zqve5IMAAggggEAqBAiAElDVHvDFxcXmMsBYi9d9cQVC6LyCwgIZMHhw6KQeDR89elTq6up6lAcrI4AAAggggMB/BAiAEtgTNADSV01NjdTX10etcUybTyLPJe76ZJf84Ve/ilo2mQk/+tGPTACWzLqsgwACCCCAAALRAgRA0SadTjl48KDU1tZGzR/UcX+ayADoyJEjsmbNmqhlk5lwxRVXEAAlA8c6CCCAAAIIdCIQ+b3dyWJMRgABBBBAAAEEMkeAAChz2pKaIIAAAggggECCAgRACUKxGAIIIIAAAghkjgB9gDKnLakJAggg4FmBoqKipMuuF6norUJ6koeuz/Mbk24CT65IAOTJZqPQCCCAQGYJ5OXlyf79+x253cfgjluW6K1OSNklQACUXe1NbRFAAAHXCuzZs0d2796d9vKVlZURAKVd3fkN0gfI+TagBAgggAACCCCQZgECoDSDszkEEEAAAQQQcF6AAMj5NqAECCCAAAIIIJBmAQKgNIOzOQQQQAABBBBwXoAAyPk2oAQIIIAAAgggkGYBAqA0g7M5BBBAAAEEEHBegADI+TagBAgggAACCCCQZgECoDSDszkEEEAAAQQQcF6AGyE63waUAAEEXCSgdyTWRyvYTrm5uZKf3/VHrs/nE7/fb3vz5IcAAhECXf83RqzAKAIIIJCpAhr46POkUhWAFBQUxKXT7be1tUlLS0vc5ZiJAAI9FyAA6rkhOaRQoKKiIvhrXL8cuvoCsV0U/SKqq6uznS35uVxgw4YNcvjw4bSXcuLEiVJSUpL27bJBBLJRwNUBUHNzs2zevNl8AY4bNy7tX37ZuEO4qc6BX+MbN26UnTt3pr1oEyZMEA3ASAgggAACmSfg2gCosbFRrrrqKjnllFPk008/lWeeeUbuvvvu4NGAzGsKatSZwKpVq+TNN9/sbHbKpg8dOpQAKGW6ZIwAAgg4K+Daq8CWLVsmVVVVMnfuXFm8eLE0NDSIHgkgIYAAAggggIA9AT3a3tVLt9bVMsnMt1eL7ueU09HZz5WXG2jgU11dLWeddZap1R/+8AdzCkyPCgXSbbfdJmvWrDGjenXFS7++JzDL+rtewdHU1CTt7e1Redede474jxwJnz7ua5L74vPh05IcGzhwYNYe+dJ/qM8//9zYJ8mX9Gr9+/cXvSLIqaR1P3DggOhVQelKvf61x2wqm/uhqHtn/+upbodsd9c+d9oJPN2puLg4az9j1Vr3eadSrO9UW2VpKy2SopGVnWbn2lNge/fulT59+gQLrsO7du0KjuuAnh4LfEHpe9OI48Pm2xzRzrf5ne0kMS5t1YCsr6X+I4EdxKlYVf85nNq2Bp6lpaXmpcMBC5ttGy8vJy9J1vqWl5fHK57VebqPt3UEfdrWfgesA5UJfBg7uc/lx/ihEyhfsu/6GdVVMNv+xWeMU3V3+n89t8O9MAlgLbd+5ra2tiaxtohfP+M61nTK3YnPtgCUbtuXwP6eqjLqZ3qqgl79fysKVDTGu2sDoMgPCwWK/HV08cUXh1Vpz57//HoNm5iOEX/0UaG2tlbz6z0dm8+WbWgQXF9f3+WXSLZ42KynfoEcc8wx5oo3PfpBsi/Qr18/R64ss18T9+Wo3w19+/aVgwcPOhbEuE/FXon0+1h/iHrtili9pUW85No+QAMGDJBDhw4Fy67DQ4YMCY4zgAACCCCAAAIIJCvg2gBI74excuVKcy5e+0GsW7dOxowZk2w9WQ8BBBBAAAEEEAgKuPYU2KRJk2Tt2rUydepU0XOPU6ZMkcrKzjszBWvEAAIIIIAAAggg0IWAawMg7dA2f/58c85Rz+/qOAkBBBBAAAEEELAh4Pqoonfv3jbqSR4IIIAAAggggEBQwLV9gIIlZAABBBBAAAEEELAsQABkGZTsEEAAAQQQQMD9AgRA7m8jSogAAggggAAClgUIgCyDkh0CCCCAAAIIuF+AAMj9bUQJEUAAAQQQQMCygOuvAutOfZ26VN73rW9Kx/X6sq/jmSYFHc+Uq8jpiCtHDJc8Lt3vTvN1uaw+M0bvCRV4VlSXK7BAwgJqu23bNvP8va5uH59wpiwYJqDPJXLqMyqsIBk4cvToUfn000/F6QcYZyCtqVLgOWBe23/1ER7xkmufBh+v0G6dd8kll8iJJ54od955p1uLSLkQiCmgj5o588wz5e6775bJkyfHXIaJCLhV4IUXXpA5c+bIli1bpFevXm4tJuVymQCnwFzWIBQHAQQQQAABBFIvQACUemO2gAACCCCAAAIuE8j7347ksjJ5tjh6vvHUU081p8E8WwkKnpUC2q9K+/5UVVVJRUVFVhpQae8K6P47aNAgGTdunHTV78O7taTktgXoA2RblPwQQAABBBBAwPUCnAJzfRNRQAQQQAABBBCwLUAAZFn08OHD8vrrr8uePXss50x2CKRH4KOPPpKPP/44PRtjKwhYEKipqZG//OUvcuDAAQu5kUW2CBAAWWzpP//5z3LdddfJv//9b7n11ltl4cKFFnMnKwRSL6D77k033SQffvhh6jfGFhCwIHDPPffIXXfdJVu3bpUf/OAHsnPnTgu5kkU2CGTUjRCdbDC9ydnjjz9u/hErKyvl8ssvl0svvVSuvPJK6devn5NFY9sIJCSgAfySJUukvLw8oeVZCAGnBfRI5Zo1a+TZZ581N0l96qmn5IknnpCf//znTheN7XtAgCNAlhpJrzx47LHHRIMfTU1NTaJ3J9XAiISAFwRKSkrk4YcfluHDh3O3bS80GGWUf/3rXzJ69GgT/CjH2LFjzR3NoUEgEQGOACWilOAyZWVlZkl9rMC9994r5557rgwYMCDBtVkMAWcFqqurgwXw+/3BYQYQcKuA9rUMPWLZp08fOXjwoFuLS7lcJkAA1IMGeeCBB6Strc3koKe7Bg8eLM3NzXLbbbeJfoH84he/6EHurIpA6gS0r89LL71kNqBfGnqqloSA1wT0yHvoUXb9PNYjmSQEEhEgAEpEqZNl9JdHIADSf8SGhgbzPJrjjjtOfvazn3FDrk7cmOy8QEFBQfCXM89Ocr49KEFyAgMHDpQPPvgguLI+0+7YY48NjjOAQDwBAqB4Ol3Mu+yyy8KW0KBn5MiR8pOf/CRsOiMIuE1g6NChcsUVV7itWJQHgW4J6J2ftbvBJ598YgKfF198Uc4444xu5cHC2StAAGSp7fWy4Q0bNpjX008/Hcz1/vvvN530ghMYQAABBBCwIqCnb6+++mqZMWOGeYTLsGHDJPKHqZUNkUlGCvAojIxsViqFAAIIZI9Aa2ur6X/J6dzsaXMbNSUAsqFIHggggAACCCDgKQHuA+Sp5qKwCCCAAAIIIGBDgADIhiJ5IIAAAggggICnBAiAPNVcFBYBBBBAAAEEbAgQANlQJA8EEEAAAQQQ8JQAAZCnmovCIoAAAggggIANAQIgG4rkgQACjgscOXKkW2Xo7vLdypyFEUDA9QIEQK5vIgqIAAJdCbzyyivm4cNdLReY393lA+vxjgACmSNAAJQ5bUlNEMhagb///e9SX1+fcP27u3zCGbMgAgh4RoAAyDNNRUERyG6BrVu3yvTp0+Vb3/qW/PCHP5RNmzYZkLfeekuee+452bVrl5l++PBhM12fC3XVVVeZ5fW5ZytXroy7fHbrUnsEsk+AACj72pwaI+A5gX379snZZ58txcXFJsjJycmRCRMmyEcffSSDBg0SfbhraWmpeRBmYWGhLFq0yDwT6sQTT5Qrr7xSGhoa5Pzzz5d33nkn5vKeA6HACCDQYwEehtpjQjJAAIFUC+gpKw1ibr31VhPAfO9735OTTz5Z/H6/ea+qqpIdO3aY4EjLsn//fvnd735nHpSp4/qAzIEDB5qHFV977bUSubwuQ0IAgewSIADKrvamtgh4UkADli9/+csyfPhwqa6ulvPOO0/0tFb//v1j1kcDpT179sjzzz9vjhK9//770tjYKE1NTTGXZyICCGSfAKfAsq/NqTECnhPQp3xrn5/f/OY35kjQzJkzRU9vrVq1KmZdFi5cKJWVlWZ5PRp0ySWXmCNHMRdmIgIIZKUAAVBWNjuVRsBbAtu3bxft1KyBz8svv2xOcekpsPvvv99URPsEBZIe6ZkzZ47cddddps+PBkMXXXSRWae9vT1q+cB6vCOAQHYJEABlV3tTWwQ8KzBt2jR59tlnxefzid7EUK/20tNimioqKswpLw2UNOmpsb1794oGPNp36LrrrpPm5ubgKbDQ5dva2sw6/EEAgewSIADKrvamtgh4UmDEiBHy29/+VubOnSvl5eXm9NZpp51mxrVCZ511luTl5cnIkSNF+/vceeed8tRTT5nTXsccc4zoKbQpU6aIXkofufzmzZvNNP4ggEB2CeR0XEXhz64qU1sEEPCygB7Z6du3r7kkPrIetbW1Zl5gut4bSAOg/PzY13tELh9Yj3cEEMh8AQKgzG9jaogAAggggAACEQKcAosAYRQBBBBAAAEEMl+AACjz25gaIoAAAggggECEAAFQBAijCCCAAAIIIJD5AgRAmd/G1BABBBBAAAEEIgQIgCJAGEUAAQQQQACBzBcgAMr8NqaGCCCAAAIIIBAhQAAUAcIoAggggAACCGS+AAFQ5rcxNUQAAQQQQACBCIH/B6Andybq4hFVAAAAAElFTkSuQmCC" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -270,9 +270,9 @@ <h3>One categorical (one proportion)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -326,9 +326,9 @@ <h3>Two categorical (2 level) variables</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -371,9 +371,9 @@ <h3>Two categorical (2 level) variables (z)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAFQCAYAAABXpMEwAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AADHMSURBVHgB7d0JcFXl/fDxX/aEJXuAolRjQaRUKyiCr8VxQV6lFa1gBZ0WodZMad1wqtQydhS7ahEraqUzjNvwFoFiQWUsiFMUXhBaiq+yif9WjRIwQmLISkLe8ztyQpab5K7nnOee75k5ufee5Xl+z+e59+Z3z5rSag3CgAACCCCAAAIIBEggNUBtpakIIIAAAggggIAtQALEGwEBBBBAAAEEAidAAhS4LqfBCCCAAAIIIEACxHsAAQQQQAABBAInQAIUuC6nwQgggAACCCBAAsR7AAEEEEAAAQQCJ5CeTC0+vHq1Ec3JsKI8ZkSk3gSZfcKnxZvqfV+rfmjVxi/Xr2itqpaWda+fdDvtNPt56oVjJfXCcSenu/QsKytLWlpapLm52aUaO1Xz1ludJvjrZZoVjr53jvsrLN9Ek3nCh+/o0F2i7x8dTPh+zrzsMunXr9+XAYf4m1QJUGNjY4gm+m9SHyuko/4LyzcRFViR6JePGb3pPluWVWWDNfrmC+jgQZEX/k8XiJY5d4iMHtVleqIn5ObmSkNDg3j2feDz76G+VgdoatiU6I4wtHz9d6mfLb6jQ3eg/kDVwYjv5w0bRCZP/jLgEH/ZBRYChUkIIIAAAgggkNwCJEDJ3b+0DgEEEEAAAQRCCJAAhUBhEgIIIIAAAggktwAJUHL3L61DAAEEEEAAgRACJEAhUJiEAAIIIIAAAsktQAKU3P1L6xBAAAEEEEAghAAJUAgUJiGAAAIIIIBAcguQACV3/9I6BBBAAAEEEAghQAIUAoVJCCCAAAIIIJDcAiRAyd2/tA4BBBBAAAEEQggk1a0wQrSPSQgggEBEAjk5OZKSkhLROvFcWG8x4JvbnMSzYZSFgM8ESIB81iGEgwAC3gmkp6dLfn6+HUBrq/u3m9W0q84aq70joGYEAiNAAhSYrqahCCAQrsDdd98t7733XriLd1julry8Dq8jeXH+1KkyIJIVWBYBBKIW4BigqOlYEQEEEEAAAQRMFSABMrXniBsBBBBAAAEEohYgAYqajhURQAABBBBAwFQBEiBTe464EUAAAQQQQCBqARKgqOlYEQEEEEAAAQRMFSABMrXniBsBBBBAAAEEohYgAYqajhURQAABBBBAwFQBEiBTe464EUAAAQQQQCBqARKgqOlYEQEEEEAAAQRMFSABMrXniBsBBBBAAAEEohZwLQE6duyYbNmyRXbu3Cmd77FTWVkpr732muzdu7dDQxobG2XTpk2yefNm0fUZEEAAAQQQQACBeAi4kgBVVFTItGnT7ERmzZo1ctNNN4kmNzrs2LFDZs6cKfv27ZN77rlHVq1aZU+vr6+Xm2++Wd544w154YUX7HmdEyd7Qf4ggAACCCCAAAIRCriSAC1fvly+/e1vy5w5c2TevHkydOhQWbdunR3qwoUL5aGHHpLbbrtNFi9eLEuWLJGmpiZZtmyZjB071l7+ySeflLq6Otm6dWuEzWNxBBBAAAEEEECgq4Ard4MvKyuTlJSUttqrq6tFt/A0NzdLeXm5nHPOOfa8gQMHSp8+feSTTz6R/fv3y8SJE9vWGT16tOzatUvGjRvXNu3FF1+U3bt3269TU1Nlzllntc3z8xNFz/VzgD6ILduKwZU3pw/aGmkImdYK+mlqjXTFBC3fYpVbG6LszKwsyc51/52u3wWZmZmSG0Xduq7XQ4YVQCLVtHx9D+lnjKGrQJo1Sd8FieyDrrWaM0V9dND3kN+H470E6Mr/GP0ycoYNGzbYSc9VV10lhw4dkr59+3ZIjvLy8uTw4cOiu83af4Hpc02W2g+620yPK9IhIyNDMg1JgPTDdVLEDp8/nQT0jen9v6JOQfnkpX4B+SkBau7GJS0tzU5EupmdsMn6Y0vrbv+jK2GVJaBg7d9Efj/o50pH5x9ZAppgdJH62dIxkX1gMpDzvWzC+6epF2hXEiAnhtWrV9vH8yxYsED69esntbW10tKivx9PDrpVKDs72/4Caz9Pp+fk5Jxc0Hqmu9PaDwesXW0mDAVWkEdMCNSjGAdZ9R61xjqP6vd7tfrLVLe4dPzk+C/qemu3db11goPbQ0lJiTQ0NEhNTU3EVaenp4uu7+XQYFVencAA+lpl6yklvf1zSGAIvi66yIpOP1tVvo7Su+CcLYf6PvX7kGUFqO/37gbXEqDnn3/ePtPr8ccfF93VpUNRUZGdBOkB0VnW5nIddOvP4MGDpbi42H5uTzwxfciQIc5LHhFAAAEEEEAAgagFnK1ZURcQzoqvvvqqvP766/LUU0+1JT+6nv7a0gOddcuQDhs3bpSCggJ7HD9+vKxdu9b+JaenyW/evFlGjRplL8cfBBBAAAEEEEAgFgFXtgDpmV0HDx60zwRzgp0yZYrccccdMnv27LbT3/UAxPvvv99eZMKECfY1gKZPny46XU+jLy0tdVbnEQEEEEAAAQQQiFrAlQRoxYoV3QZ42mmn2ae8V1VVSX5+fttyunVo/vz59n58PfZHXzMggAACCCCAAALxEPBNVtE++WnfsP79+7d/yXMEEEAAAQQQQCBmAVeOAYo5SgpAAAEEEEAAAQTiKEACFEdMikIAAQQQQAABMwRIgMzoJ6JEAAEEEEAAgTgKkADFEZOiEEAAAQQQQMAMARIgM/qJKBFAAAEEEEAgjgIkQHHEpCgEEEAAAQQQMEOABMiMfiJKBBBAAAEEEIijAAlQHDEpCgEEEEAAAQTMECABMqOfiBIBBBBAAAEE4ihAAhRHTIpCAAEEEEAAATMESIDM6CeiRAABBBBAAIE4CpAAxRGTohBAAAEEEEDADAESIDP6iSgRQAABBBBAII4CJEBxxKQoBBBAAAEEEDBDgATIjH4iSgQQQAABBBCIowAJUBwxKQoBBBBAAAEEzBAgATKjn4gSAQQQQAABBOIoQAIUR0yKQgABBBBAAAEzBEiAzOgnokQAAQQQQACBOAqQAMURk6IQQAABBBBAwAwBEiAz+okoEUAAAQQQQCCOAiRAccSkKAQQQAABBBAwQ4AEyIx+IkoEEEAAAQQQiKNAehzL8ryoAs8jCC+ADGsxU2INr0XxX6qPVWRW/ItNihL1Q6tjq09a02zFURMiluycHMkpcP+dnpaWJtnZ2ZKeHvnXW0pKSoiWuDsp06oukWppVvn62fLL+8dd3d5r03eNGiWyD3qPwr9LOFtNcvwbYltk+t3U0xD5N0RPpXk874jH9YdbvX6wTIk13DbFc7lBVmF1J8Z4lpssZeVaDam1xhafN6ihvl4ajrj/Ti8pKZGGhgapqQmVlvWMpkmTru/l0GRVXp3AAPpaZR+zRq2HoatAkTVJP1tVXWcxxRLIPqHQYIBGbz+ikyoBMqA/CBEBIwT6W1GG++WQiC1ATU1NUluraR4DAgggkBiBcL/jElM7pSKAgC8F9FdepTUeDCM6/RIpDLHc55WVUrt3b4g5PU865ZRTpH///iRAPTMxFwEEYhQgAYoRkNURSFaBt3QLzNq1vTav75GqkAnQuvXr5W///aDX9TsvUFZWJpMmTeo8mdcIIIBAXAWc45niWiiFIYAAAggggAACfhYgAfJz7xAbAggggAACCCREgAQoIawUigACCCCAAAJ+FiAB8nPvEBsCCCCAAAIIJESABCghrBSKAAIIIIAAAn4WIAHyc+8QGwIIIIAAAggkRIAEKCGsFIoAAggggAACfhYgAfJz7xAbAggggAACCCREgAshJoSVQhFAIFqBgQMH2jcyLYjyRqqpqalG3ww1WjfWQwCByARIgCLzYmkEEEiwwIABA6S5uVneeeedBNfUtfjCwkIZOnRo1xlMQQCBpBMgAUq6LqVBCJgvUFVVJffff7/rDbn44ovlvvvuc71eKkQAAfcFOAbIfXNqRAABBBBAAAGPBUiAPO4AqkcAAQQQQAAB9wVIgNw3p0YEEEAAAQQQ8FiABMjjDqB6BBBAAAEEEHBfgATIfXNqRAABBBBAAAGPBUiAPO4AqkcAAQQQQAAB9wVIgNw3p0YEEEAAAQQQ8FiABMjjDqB6BBBAAAEEEHBfgATIfXNqRAABBBBAAAGPBUiAPO4AqkcAAQQQQAAB9wVIgNw3p0YEEEAAAQQQ8FjA9XuBrVu3Ti699FL7bs/a9qNHj8q7777bgWHcuHH268bGRtm+fbukpKTImDFjJCMjo8NyvEAAAQQQQAABBKIRcDUBWrlypSxcuFD0hoPp6V9WvW3bNlm0aFGHOzBrAlRfXy+zZs2SkSNHyqeffirLly+XBQsW2MlQNA1lHQQQQAABBBBAwBFwJQFqbm627+z8+eefO/W2Pb7//vsyefJkmTFjRts0fbJs2TIZO3as3Hnnnfb0srIy2bp1qzhbhzoszAsEEEAAAQQQQCACAVcSoJaWFjtxmTRpkr37q318mgBpUrN06VIZNmyYnH/++fZWnv3798vEiRPbFh09erTs2rWrQwL0zDPPyDvvvGMvo1uUHrB2k5kw6I68fBMC9TDGHKvuTA/r93PV+v5Js8bWBAaZksCyKbpnAX3fJ/L7Qb/0s6zxeM9hBHaufrb04NhE9oHJuOqjQ/aXD77+29xLdK4kQFlZWfZWnlCxaAKkgyZBzz33nL3l55FHHpGKigrJzc1tW0Wfl5eXt73WJ4cPH7Z3j+nztLQ0STMkAdJ/Ls6bSGNn6CrA0fldTZwp+v7Bx9FIvsdEfz847x99ZOgq4LjwHd3VRqeY9N3TEroJbVNdSYDaagvxZMmSJZKfny+pqaly9dVXyzXXXGMnOprQ6JYjZ9DdaDk5ul3g5DBnzpyTL6xnB6zjhEwYCqwgj5gQqEcxDrLqrbXGOo/q93u1+rNAfU5+OuIfcUn8i6TEMAUareWqw1w2msX6Wisds8amaFYOwDpFVhv1s1UVgLZG00Rny09DNCu7vI5u6exp8DSZa2pqkg8//NBOfjTIzMxMGThwoL31p7i42N7C4wSvW3sGDx7svOQRAQQQQAABBBCIWsDTBEhPa9czu7Zs2WI3YPfu3aIHSp977rkyfvx4Wbt2rTQ0NEhlZaVs3rxZRo0aFXVDWREBBBBAAAEEEHAEPN0Fptf30bO8Fi9ebI+fffaZzJ071z5FfsKECbJp0yaZPn26vYVo2rRpUlpa6sTNIwIIIIAAAgggELWA6wnQm2++2SHY8847T55++mmprq62D3rWpEgHPatr/vz5UlNTYx/741w3qMPKvEAAAQQQQAABBKIQcD0B6i7GvLy8kLP69+8fcjoTEUAAAQQQQACBaAU8PQYo2qBZDwEEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBEiAYtFjXQQQQAABBBAwUoAEyMhuI2gEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBEiAYtFjXQQQQAABBBAwUoAEyMhuI2gEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBEiAYtFjXQQQQAABBBAwUoAEyMhuI2gEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBEiAYtFjXQQQQAABBBAwUoAEyMhuI2gEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBEiAYtFjXQQQQAABBBAwUoAEyMhuI2gEEEAAAQQQiEWABCgWPdZFAAEEEEAAASMFSICM7DaCRgABBBBAAIFYBNJjWdlv6xb4LaBu4smwppsSazdNSPjkPlYNWQmvxcwK9EOrY2sCw09JYNkU3bNApjU7kd8PaVb5+tlK5Pun5xb6e65+ttQokX3gb4Geo3O2muT0vJgv5jb3EkVSJUBHemmsX2brB8uUWL0wG2RVWndi9KJ+v9eZawVYa40tCQy0JIFlU3TPAk3W7OqeF4lpbl9r7WPWqPUwdBUosibpZ6uq6yymWALZJxQaDNDo7Ue0k8wZ0BRCRAABBBBAAAEE4iNAAhQfR0pBAAEEEEAAAYMESIAM6ixCRQABBBBAAIH4CJAAxceRUhBAAAEEEEDAIIGwE6ArrrhCjh492qVpzzzzjNx4441dpjMBAQQQQAABBBDwq0CPZ4Ft3bpV1q1bZ8f+1ltvyW9/+1vJznaOAbeOlG9pkZdeekmGDRvm1/YRFwIIIIAAAggg0EWgxwRo6NChMmfOHGlsbJRjx47JK6+8ImlpeoWEL4eMjAw5/fTT5b777nMm8YgAAggggAACCPheoMcEqKioSDZt2mQ3YvLkybJ06VLp16+f7xtFgAgggAACCCCAQE8CPSZA7VdcvXq1vSXoo48+sh/bz9Ok6Ctf+Ur7STxHAAEEEEAAAQR8KxB2ArRy5Uq55ZZbpKqq6/Uxr7/+ennxxRd920gCQwABBBBAAAEE2guEnQDNnj1brr32Wvnxj38shYWF7ctgt1gHDV4ggAACCCCAgN8FwkqAvvjiCzl06JD87ne/kwEDBvi9TcSHAAIIIIAAAgj0KBDWdYByc3OltLRUtm/f3mNhzEQAAQQQQAABBEwQCGsLkDbkoYcekrvuukvKy8vtZCg9/eSqulVo5MiRJrSXGBFAAAEEEEAAATmZxfSCoccAVVdXS1lZWZclOQi6CwkTEEAAAQQQQMDHAmEnQAcPHpTW1taQTWl/ccSQCzARAQQQQAABBBDwkUDYCdDhw4e7TYBycnKkoKDAR80iFAQQQAABBBBAoHuBsBOgESNG2LvAQhXFLrBQKkxDAAEEEEAAAb8KhJ0A6c1Q9eanzlBTUyP/+te/5IknnrBPj3em84gAAggggAACCPhdIOwE6Bvf+EaXtnzrW9+Suro6+fWvfy1//vOfu8xnAgIIIIBA+AJ6q2m9Nklm+KtEvKTWEfpoThH9iXvyZ27ERbMCAkYJhJ0AddcqvQfYSy+91N1spiOAAAIIhClQbC2XfWIMc5W4LnbcKu1gXEukMAT8KxB2AvT2229Lc3NzW0v0eUVFhfzqV7+SSy65pG06TxBAAAEEohd4y1q1Yv366AuIds3LL5cpKSnRrs16CBgnEHYCNHHixJAHQetusPnz5xvXcAJGAAEE/ChwVIMKcdPphMdqnekrRUUJr4YKEPCLQNgJ0EcffdTlNPi+fftK+ytC+6VRxIEAAggggAACCPQkEHYCpPcDq7J+lSxdulT27dsngwYNklGjRsmll14qmZmJPGSvp/CZhwACCCCAAAIIRC4QdgL0zjvviO4G0ytCn3POOfLZZ5/JgQMH5Morr5RVq1ZJdrYeutf7sG7dOjtpar/lqLKyUv75z3/K6aefLsOHD28rpLGx0b4Ba4q1X3rMmDGSkZHRNo8nCCCAAAIIIIBAtAJh3Q1eC7/11lvlwgsvlI8//lh27twpn3zyiX0doD179shjjz0WVv0rV66UBx98sMP1hHbs2CEzZ860tyrdc889djKlhdXX18vNN98sb7zxhrzwwgui87q7FUdYlbMQAggggAACCCBwQiCsBEiv9bN9+3b7jK9TTz3VXlW3yugusDlz5siGDRt6BNUzxu677z75+9//3mW5hQsX2neav+2222Tx4sWyZMkSaWpqkmXLlsnYsWNl3rx58uSTT9rXG9q6dWuX9ZmAAAIIIIAAAghEKhDWLrDU1FR764smQp2H2traDqfHd56vr/UK0uPGjZNJkybZu7+cZTQxKi8vt3ep6bSBAwdKnz597K1L+/fvt3e5OcuOHj1adu3aZZfjTNPESHed6aA3ZH38iiucWb5+VPRCX0fofXB9rRDC26nqfaxuR6AXsgvrgxtDYJwMHQOe4asG/btJP1v6GQu6Q3dvY2erSZ/uFvDR9GO9xOK0pcfF9PgevdbPvffeK9u2bbOToYaGBlm7dq388Y9/lCt6STyysrJk8uTJXc4YO3TokOiZZLo1yRny8vJEb7yq1xjSA6+dQZ9//vnnzkv7UZMePZZIRz0+SK9uasKowZsQp1cx4tPz+0N9dEhk/3xZA3+DKJDI95UJZTt9bkKsxNjz96DTl909hv1D8k9/+pN897vflQsuuEBKSkrk6NGj9nE6V199tdx9993dld/jdE1g2t9fTBfWrUKacHWep9P1rvPth7KysvYv5cDy5R1e+/VFgRXYEb8G54O4Blkx6LbGrtsbfRCcD0LQnwW11pjIWxaU+KCdhOCNQNC/m/RKSPrZqvKG3/e1OlvmG3wfqUhWLzGGnQANGzbM3t203rpCqR74rEnKueeeKxdddFEvVXQ/u8i66JbuQtOzvXQrkQ669Wfw4MFSXFxsP3fW1ulDhgxxXvKIAAIIIIAAAghELRDWLjCndE1S9Dge3eLzk5/8xD4I2pkXzaPuutIDnVevXm2vvnHjRikoKLDH8ePH27vYdFebnia/efPmmOuLJkbWQQABBBBAAIHkEwg7AdJr/ujp6s8++6ytoFtuSktL5dFHH+31IOie2GbPni0rVqyQG2+80T4L7Oc//7m9+IQJE+zjg6ZPny66q2vq1Kl2fT2VxTwEEEAAAQQQQCAcgbB3gd111132RRCdBEWPx9FT2PX09tNOO02uu+66cOqTN998s8Nyuq6e8q5Xmc7Pz2+bp1uH9B5jNTU19rE/+poBAQQQQAABBBCIh0BYWYVel+e1116T9957zz4+RyvWU+N164wmKHp7jHAToO6Cbp/8tF+mf//+7V/yHAEEEEAAAQQQiFkgrF1gepr6sWPH7OvzdK7xiy++6HCwcuf5vEYAAQQQQAABBPwmEFYCpNfYueyyy2Tu3Ln2rTCcRujVofUYIL0fGAMCCCCAAAIIIGCKQFgJkDbmmWeeET0j66tf/aroxQr1GCC9QaleIFFvh8GAAAIIIIAAAgiYIhDWMUDamMLCQvsA5r1798q///1v+xigs88+W77+9a+b0lbiRMAYAb0U/8nroHcMW+fpmMjh5LXZE1kLZSOAAALeCYSdAGmIeuDziBEj7NG7kKkZgeQX0A+mXnFVb//b5EFz/5cHdVIlAggg4KZARAmQm4FRFwIIiHy8apXeTdh9Cuu6WwwIIIBAMguEfQxQMiPQNgQQQAABBBAIlgAJULD6m9YigAACCCCAgCVAAsTbAAEEEEAAAQQCJ0ACFLgup8EIIIAAAgggQALEewABBBBAAAEEAidAAhS4LqfBCCCAAAIIIEACxHsAAQQQQAABBAInQAIUuC6nwQgggAACCCBAAsR7AAEEEEAAAQQCJ0ACFLgup8EIIIAAAgggQALEewABBBBAAAEEAidAAhS4LqfBCCCAAAIIIEACxHsAAQQQQAABBAInQAIUuC6nwQgggAACCCBAAsR7AAEEEEAAAQQCJ0ACFLgup8EIIIAAAgggkA4BAggggAACkvrl7+FMjyharXqPeVQ31QZTIKkSoAJD+jDDitOUWL0i7WNVnOVV5T6ol02zPuiEgIUwoKBAUqw2F3nY7iar7uMe1q9V6z/FNGvkO1o1ug7Od1NO11m+m9LcS0RJlQAd6aWxfpmtHyxTYvXCbJBVad2J0Yv6/VCnJn+FfgiEGAIjoP/0Nfn46/r17rc5O1umfutb9ue+3v3aO9SoCWCLNVZ1mMoLRyD7xJMGZ4KPH3v7EZ1UCZCP+4HQEEAAATMEqvjXb0ZHEWWsAs7WrFjLYX0EEEAAAQQQQMAYARIgY7qKQBFAAAEEEEAgXgIkQPGSpBwEEEAAAQQQMEaABMiYriJQBBBAAAEEEIiXAAlQvCQpBwEEEEAAAQSMESABMqarCBQBBBBAAAEE4iVAAhQvScpBAAEEEEAAAWMESICM6SoCRQABBBBAAIF4CZAAxUuSchBAAAEEEEDAGAESIGO6ikARQAABBBBAIF4CJEDxkqQcBBBAAAEEEDBGgATImK4iUAQQQAABBBCIlwAJULwkKQcBBBBAAAEEjBEgATKmqwgUAQQQQAABBOIlQAIUL0nKQQABBBBAAAFjBEiAjOkqAkUAAQQQQACBeAmQAMVLknIQQAABBBBAwBgBEiBjuopAEUAAAQQQQCBeAiRA8ZKkHAQQQAABBBAwRiDdmEgJFAEPBLz6heBVvR4QUyUCCCDgiQAJkCfsVGqCQJ4VZB8TAiVGBBBAAIGIBUiAIiZjhaAI6FaY/7HGf+3Z43qTv3LWWXKR67VSIQIIIBAcARKg4PQ1LY1CoErXeffdKNaMbZUDw4eLpKTEVghrI4AAAgh0K8ChBt3SMAMBBBBAAAEEklXA8y1AR48etX5gd/yFPW7cONu7sbFRtm/fbv0QTpExY8ZIRkZGsvYD7UIAAQQQQAABFwU8T4C2bdsmixYtkqFDh7Y1WxOg+vp6mTVrlowcOVI+/fRTWb58uSxYsMBOhtoW5AkCCCCAAAIIIBCFgOcJ0Pvvvy+TJ0+WGTNmdAh/2bJlMnbsWLnzzjvt6WVlZbJ161Zxtg51WJgXCCCAAAIIIIBABAK+SIA0qVm6dKkMGzZMzj//fHsrz/79+2XixIltTRk9erTs2rWrQwL0yCOPyJYtW+xl0tPT5bkpU9qW9/MTRS/yc4A+iK2vFUOOx3F4/uHwuP1Uj4DbAv2sCr2+9IR+7tOske/o0L3vHDis39F+H5p6CdDz73jdAqSDJkHPPfec6JYfTWwqKiokNze3LXx9Xl5e3vZanwwcOFDOOOMMe1paWpo0d5jr3xf64TIlVi8U9Uiv4yeM9A3KuVBe9AJ1IuC+QItVpY5eDvr93GqNfEeH7gX10cHrfvoyip7/aj/2NHieAC1ZskTy8/MlNTVVrr76arnmmmvsREcTmpaWk8TNzc2Sk9Nxm8D3v//9Dm07YB0nZMJQYAVZbUKgHsWovVx/YhxkPeqp6A3W6PZQ4naF1IdAwAWcz72XDLrlR//z8B0duheyT0z24js5dETdT83qfpY9x9MEqKmpST788EMpLCy0g8nMzLS36ujWn+LiYjl8+HBb+Pp8yJAhba95EhyB9R98ILJjh+sNvmzqVNfrpEIEEEAAAXcEnN157tTWqRY9rV3P7HKO49m9e7d8/vnncu6558r48eNl7dq10tDQIJWVlbJ582YZNWpUpxJ4iQACCCCAAAIIRC7g6RYgvb6PnuW1ePFie/zss89k7ty5ogc0T5gwQTZt2iTTp0+3d49NmzZNSktLI28hayCAAAIIIIAAAp0EPE2ANJbzzjtPnn76aamurrYPetakSAdNgubPny81NTX2sT/6mgEBBBBAAAEEEIiHgG+yirw8vfd216F///5dJzIFAQQQQAABBBCIQcDTY4BiiJtVEUAAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhagAQoajpWRAABBBBAAAFTBUiATO054kYAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhagAQoajpWRAABBBBAAAFTBUiATO054kYAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhagAQoajpWRAABBBBAAAFTBUiATO054kYAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhagAQoajpWRAABBBBAAAFTBUiATO054kYAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhagAQoajpWRAABBBBAAAFTBUiATO054kYAAQQQQACBqAVIgKKmY0UEEEAAAQQQMFWABMjUniNuBBBAAAEEEIhaID3qNX24YoEPYwoVUoY10ZRYQ8XvxrQ+ViVZblREHQgg4BsB/dxnexyN/lNMs0a+o0N3hLPVJCf0bF9Nbe4lmqRKgI700li/zNYPlimxemE2yKq0zhrrrVGfMyCAQDAEnM+9l60tsipvscYqL4Pwcd1Ogtrg4xid0Hr7EZ1UCZDTaB4RQAABBMwS0KRDtwB5tWWhyar7qFlkRBujAAlQjICsjgACCCAQo0B6ur3babtVzMetrTEWFvnqF6akiG75YQiWAAlQsPqb1iKAAAK+Ffj4v/8V2a5pkLvDf6dOJQFyl9wXtTnHM/kiGIJAAAEEEEAAAQTcECABckOZOhBAAAEEEEDAVwIkQL7qDoJBAAEEEEAAATcESIDcUKYOBBBAAAEEEPCVAAmQr7qDYBBAAAEEEEDADQESIDeUqQMBBBBAAAEEfCXAafC+6g5/BqOXhXd70Mzci3rdbif1IYAAAgh4I0AC5I27MbXqVVnzPYi2v1WnjgwIIIAAAggkQoAEKBGqSVSmbonRS9Sv2rnT/VYVFsrUIUPcr5caEUAAAQSSXoAEKOm7OPYG2hemf//92AuKtIQca/sTCVCkaiyPAAIRCpRYy2dYo96oWv8pun03+ONWndXWyOCuAAmQu97UhgACCCDgMwG9D5jeiX6fNaaciM2tO5LlWfUNtcYaa9REiME9ARIg96ypCQEEEEDApwL/seL6z4oV7kd31lky9BvfcL9eahROg+dNgAACCCCAAAKBEyABClyX02AEEEAAAQQQYBeYAe8BPR3cq47iWjwGvEEIEQEEEEAgYgGv/q9GHGiQV9BOOmSN73qAcJ5Vpx4gyIAAAgggkACBvn3tQr287lmtFUFzAprm9yJJgPzeQyfi+3/WY70HB+j9z9SpJECGvEcIEwEEDBQ45RQ76CPWXy+SEL3Sml7r7agdRbD+kAAFq79pLQIIIICADwX+7+rVIk1NrkdWbP3IDerAQdBB7XnajQACCCCAQIAFSIAC3Pk0HQEEEEAAgaAKkAAFtedpNwIIIIAAAgEW4BigAHc+TUcAAQQQCLaAXupEE4F+YTI4SYPzGOZq3S6mB343dDs3sTPi1YaERNnY2Cjbt2+XlJQUGTNmjGRk6O3qGBBAAAEEEEAgHgKaBOhZYOHe++xYPCo9UUa29ZhljSRAnVDr6+tl1qxZMnLkSPn0009l+fLlsmDBAjsZ6rSoKy+dG+TFq7J4lxevuCgHAQQQQCBYAmsarBTk5Zddb3SKdQbad1yv9WSFvt0CtGzZMhk7dqzceeeddrRlZWWydetWGTdu3MnoXXqmmwhLrDGeScugCGL34toQEYTHoggggAACCBgnkNJqDX6Met68eTJx4kS5+OKL7fCefvppexeYbhVyhgcffFDefPNN+2V6erqsaTfPWSZej15mipoA6WbHL+LVmAjKybOW1c2UByNYJ16L6hH6mnhWW6MXm0iLrXrVvsoa3R50f7xeH7bSGnXztNuDtr3eGvUKsb0Nrf/9UFrv+UWXxVLuvlNSfjany/TeJhQWFkpqaqpUVmrr3R2ys7MlLy9PDh8+LMeORbmxf+PGqIPWq67zWY+aL+oV9f2unzO9GKHbg37O9fNuwmc93jZ69evMeBfarrzGrCzpO3lyuykdn3r5f71jJJ1eVVRUSG5ubttUfV5eXt72Wp/o7rG0tC/vVqWPjZMmdZgfzxfHreOP9FikeAwaa0tL+P/WtF49+qnQo1y12arfzbo1mVUfzc1brH+EfY4flz7xgI+wjFar3frucrPt7UPUtudZbe88aHJwPMT0zsvF9NqqI8uqQ/fP9zY0//udkElidk6O9LWSmUgHfb9r32siFM2g7x/1idZI33v9+8dwY4Jrr40m7C/XceGz7nyPhfrt6/ZnvT2U1591/cGln3Xnf0ok39Ht2xHN8+4+69GUFfE6EXzWteye3j8R122t0GJ9VpubE7Ofo7c+9G0C1DlJUKAc6wu1/TBlypT2L+XAgQMdXvv1RUFBgRw54sVvDb+KdIxr0KBBUltbK3V1dR1n8MoW0B8D6tPbh9s1rqrQ7+V6q//qPdiKU1JSIg3WMQ01NTWuEZhUUV/r3lO6davJg6sOm+BUVFRkf7aqqrzY9ut/Id1KqoN+xvw+ZFlbgHoaNOn15VBcXGxvhnaC003SgwcPdl7yiAACCCCAAAIIRC3g2wRo/PjxsnbtWjvL1GMBNm/eLKNGjYq6oayIAAIIIIAAAgg4Ar7dBTZhwgTZtGmTTJ8+3T4gctq0aVJaWurEzSMCCCCAAAIIIBC1gG8TID2Qcf78+fZ+fD32R18zIIAAAggggAAC8RDwfVYR09kY8RCiDAQQQAABBBBIOgHfHgOUdNI0CAEEEEAAAQR8I0AC5JuuIBAEEEAAAQQQcEuABMgtaepBAAEEEEAAAd8IkAD5pisIBAEEEEAAAQTcEiABckuaehBAAAEEEEDANwK+PwssEilTTpXX23qYEmsk/vFads+ePaKX6+9865N4lW96OXqPK70fmHNPHq/b02rd2uX4/76iSxgpw8+UVA8uX/HBBx+IXgJf30MMXQX0HmD63uE7qKuNTvnoo4/szxdnIIf2cb53THj/OPd1C90S675m1ofBl3eD7y5gpie/wIgRI2Tu3LkyY8aM5G8sLYy7wKWXXipXXnml3HvvvXEvmwKTX0Avvqv3I3z00UeTv7EBbyG7wAL+BqD5CCCAAAIIBFGABCiIvU6bEUAAAQQQCLhAUh0DFPC+TJrm33HHHdz4Nml60/2G/PCHP5QzzjjD/YqpMSkEvve970m/fv2Soi00omcBjgHq2Ye5CCCAAAIIIJCEAuwCS8JOpUkIIIAAAggg0LMACVDPPsz1SODIkSOyfv16OXDggEcRUK2JApWVlfLaa6/J3r17TQyfmD0WOHbsmGzZskV27twpnCDtcWe4UD0JkAvIVBGZwN/+9je5/fbb5T//+Y888MADsnDhwsgKYOlACuzYsUNmzpwp+/btk3vuuUdWrVoVSAcaHZ1ARUWFTJs2TTZv3ixr1qyRm266SRobG6MrjLWMEOAYICO6KThBtrS0yA033CAPP/ywlJaWSl1dnehBic8//7wUWBfcY0CgOwG9btScOXPkm9/8phw8eFBuueUWWblypWRmZna3CtMRaBN4/PHH7Ytnzpo1y552//33ywUXXCDf+c532pbhSXIJcBZYcvWn8a3RK3c+++yzbVfxbWhokKNHj4omRgwIdCegV1cvLy+Xc845x15k4MCB0qdPH/nkk0/sRLq79ZiOgCNQVlbW4erq1dXVUl9f78zmMQkF2AWWhJ1qepOcWxjoLR8ee+wx+6q+xcXFpjeL+BMocOjQITtpdi7Tr1Xl5eXJ4cOHE1grRSeTgG4pzMjIsJu0YcMGO6G+6qqrkqmJtKWTAFuAOoHw0l0BPc7n5ZdftivNzc1tu/2F7nt/8MEH7QMRf/GLX7gbFLUZJ6BbDjtvJdStQtnZ2ca1hYC9FVi9erW88MILsmDBAq4H5G1XJLx2EqCEE1NBTwL6i0t/qevgXHxMj/vR+zidcsop8rOf/Ux6u6FdT+UzLxgCRUVFUltbax+0qjdC1UG3/gwePDgYALQyLgJ6rKGeRajHA+luVIbkFiABSu7+9X3rTj31VPnBD37QIc5f/vKXMnz4cPnpT3/aYTovEOhOQO9MPXbsWNFf79dff71s3LjRPmieA+e7E2N6Z4FXX31VXn/9dXnqqaeEO8F31knO15wFlpz9amyrdu/eLbfeeqsdf/vjORYtWtR2gKuxjSPwhAp8+OGH9unvusUwNTVV9CyeM888M6F1UnjyCEydOtU+e7D9986UKVNEb83DkJwCJEDJ2a+0CoHAClRVVUl+fn5g20/DEUAgPAESoPCcWAoBBBBAAAEEkkiA0+CTqDNpCgIIIIAAAgiEJ0ACFJ4TSyGAAAIIIIBAEgmQACVRZ9IUBBBAAAEEEAhPgAQoPCeWQgABBBBAAIEkEiABSqLOpCkIIIAAAgggEJ4ACVB4TiyFAAI+F/jiiy8iijDS5SMqnIURQMD3AiRAvu8iAkQAgd4E9Cq+V155ZW+Ltc2PdPm2FXmCAAJJI0AClDRdSUMQCK7Au+++a98LLFyBSJcPt1yWQwABcwRIgMzpKyJFINACO3bskJkzZ8rll18uP/rRj+Ttt9+2Pf7xj3/IypUrpby83J5+5MgRe/qaNWtk1qxZ9vJ6v7m1a9f2uHygcWk8AgEUIAEKYKfTZARMEzh48KBccsklkp2dbSc5er+miy66SPbs2SMDBgwQvalunz595IILLpDMzEx54okn5MYbb5QzzjhDZsyYIXV1dTJp0iTZtm1byOVN8yBeBBCIXYC7wcduSAkIIJBgAd1lpUnMAw88YCcwN9xwg4wYMUJaW1vtR70T/P79++3kSEM5dOiQ/OEPf2i7sa4mQyUlJbJlyxa57bbb7DvHt18+weFTPAII+FCABMiHnUJICCDQUUATnK997WsybNgwmThxolx11VWiu7WKioo6LnjilSZKBw4ckL/+9a/2VqKdO3dKfX29NDQ0hFyeiQggEDwBdoEFr89pMQLGCfTr188+5uc3v/mNvSVo9uzZ9u6tN954I2RbFi5cKKWlpaLL69agqVOn2luOQi7MRAQQCKQACVAgu51GI2CWwL59+0QPatbE55VXXrGTGt0FtmjRIrshekyQM+iWnnvvvVcefvhh+5gfTYauu+46e53jx493Wd5Zj0cEEAiWAAlQsPqb1iJgrMDNN98sK1askJaWFtGLGOrZXrpbTIfCwkJ7l5cmSjrorrGKigrRhEePHbr99tulsbGxbRdY++Wbm5vtdfiDAALBEiABClZ/01oEjBQ488wz5ZFHHpF58+ZJXl6evXvr7LPPtl9rgy6++GJJS0uT4cOHix7v8/vf/17+8pe/2Lu9Bg0aJLoLbdq0aaKn0ndefvv27fY0/iCAQLAEUqyzKFqD1WRaiwACJgvolp38/Hz7lPjO7aiqqrLnOdP12kCaAKWnhz7fo/Pyzno8IoBA8guQACV/H9NCBBBAAAEEEOgkwC6wTiC8RAABBBBAAIHkFyABSv4+poUIIIAAAggg0EmABKgTCC8RQAABBBBAIPkFSICSv49pIQIIIIAAAgh0EiAB6gTCSwQQQAABBBBIfgESoOTvY1qIAAIIIIAAAp0ESIA6gfASAQQQQAABBJJfgAQo+fuYFiKAAAIIIIBAJ4H/DwuPVCDtOBaCAAAAAElFTkSuQmCC" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -421,9 +421,9 @@ <h3>One categorical (&gt;2 level) - GoF</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -466,9 +466,9 @@ <h3>Two categorical (&gt;2 level) variables</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -511,9 +511,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -556,9 +556,9 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -603,9 +603,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -648,9 +648,9 @@ <h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -693,9 +693,9 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -738,9 +738,9 @@ <h3>Two numerical vars - correlation</h3>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -787,6 +787,9 @@ <h3>One numerical (one mean)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -804,8 +807,11 @@ <h3>One numerical (one mean)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> x_bar) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -823,7 +829,7 @@ <h3>One numerical (one mean)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="one-numerical-one-mean---standardized" class="section level3">
 <h3>One numerical (one mean - standardized)</h3>
@@ -850,6 +856,9 @@ <h3>One numerical (one mean - standardized)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -867,8 +876,11 @@ <h3>One numerical (one mean - standardized)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -886,7 +898,7 @@ <h3>One numerical (one mean - standardized)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="one-categorical-one-proportion-1" class="section level3">
 <h3>One categorical (one proportion)</h3>
@@ -913,6 +925,9 @@ <h3>One categorical (one proportion)</h3>
 <span class="st"> </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st"> </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -930,8 +945,11 @@ <h3>One categorical (one proportion)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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AEEEPCaAAGQ10aE9iCAAAIIIICA6wIEQK4TUwECCCCAAAIIeE2AAMhrI0J7EEAAAQQQQMB1AQIg14mpAAEEEEAAAQS8JkAA5LURoT0IIIAAAggg4LoAAZDrxFSAAAIIIIAAAl4TIADy2ojQHgQQQAABBBBwXYAAyHViKkAAAQQQQAABrwkQAHltRGgPAggggAACCLguQADkOjEVIIAAAggggIDXBAiAvDYitAcBBBBAAAEEXBcgAHKdmAoQQAABBBBAwGsCBEBeGxHagwACCCCAAAKuCxAAuU5MBQgggAACCCDgNQECIK+NCO1BAAEEEEAAAdcFCIBcJ6YCBBBAAAEEEPCaAAGQ10aE9iCAAAIIIICA6wIEQK4TUwECCCCAAAIIeE2AAMhrI0J7EEAAAQQQQMB1AQIg14mpAAEEEEAAAQS8JkAA5LURoT0IIIAAAggg4LpAvus1xFSwePFiOemkkyQ/v73q3bt3y5o1a6KmGjNmjHnd2Ngoq1atkpycHBk1apQUFBRETccLBBBAAAEEEECgJwJpDYCefPJJmTt3rpxwwgkdAdDKlSvlnnvukaFDh3a0XwOg+vp6mTp1qhx22GGyceNGeeKJJ2TOnDkmGOqYkCcIIIAAAggggEAPBNISALW0tMjMmTNl+/btnZr48ccfy8SJE+Wiiy6Kem/+/PkyevRomT59usmfNm2arFixQsJbh6Im5gUCCCCAAAIIINANgbQEQK2trSZwOf30083ur8j2aQCkQc28efNk2LBhcswxx5itPOvWrZMJEyZ0TDpy5EhZu3ZtVADU1NQkGlxp0t1k+siGFJR+WiOWeLiSvNVppu5M22lmj2YEsU8epY7bLPUP4hi42Cf9PPtt3eS39sZdVh3KVIts80hLAFRUVGS28sQbJw2ANGkQ9Oijj4pu+Zk9e7Zs3rxZysrKOmbR5xs2bOh4rU+uvfZa0WOKNOkxRe+99555HvQ/VVVVgehiSZ019rVxurKPlVcYJz9eVu94meQhkKJAvxTn9+rsTq06/md1sC26k0VFhRKUdVN0z7LjVZ8+fUQfQUp79uxJ2p20BEDJWvDQQw9J3759JTc3V84880w566yzTKCTl5cnuuUonHRLT0lJSfil+f+9731Pxo0bZ57r/Dt37ox6P2gvevXqZbpUV1cXiK41NTbF78cuK9vOkllsTadFhOIX4+tcPd6/fbh93Q3fNl4D872rH992o6Ph+lnRHxX62XIixQQ/WmSztY72yzq4sLBQQqFQxx4EJ0j8XEZ5ebk0NDSInngUpNTVFi07XzOueegurPXr10tlZaWpQxfKgQMHmq0//fv3l+rq6o669fmgQYM6XuuT2OOBNm3aFPV+0F4UFxebTZR6gHgQUktr++7LTn1psHLyOuV2ztClVz+vCYrpPIOPcjSoIwDK3IDpctWcueodr1k/KxpUu7jq0B+sflo3aQAUtC/8ni43GgA1Nzf7avzs9FX3PiVLGb0OkJ7Wrmd2LV++3LTx/fffNwdKH3XUUTJ27FhZtGiRiUq3bdsmy5YtkxEjRiTrC+8hgAACCCCAAAK2BDK6BUg3T+lZXg8++KB5bN26VWbMmGGO5xk/frwsXbpUJk+ebHaPTZo0SYYMGWKrU0yEAAIIIIAAAggkE0h7APTaa69Ftefoo4+WBx54QGpqasxBz+F9dnpQ86xZs6S2ttYc+xO+cGLUzLxAAAEEEEAAAQR6IJD2AChRG3UfZLwUtKPS4/WRPAQQQAABBBBIr0BGjwFKb1epDQEEEEAAAQQQaBcgAGJJQAABBBBAAIGsEyAAyrohp8MIIIAAAgggQADEMoAAAggggAACWSdAAJR1Q06HEUAAAQQQQIAAiGUAAQQQQAABBLJOgAAo64acDiOAAAIIIIAAARDLAAIIIIAAAghknQABUNYNOR1GAAEEEEAAAQIglgEEEEAAAQQQyDoBAqCsG3I6jAACCCCAAAIEQCwDCCCAAAIIIJB1AgRAWTfkdBgBBBBAAAEECIBYBhBAAAEEEEAg6wQIgLJuyOkwAggggAACCBAAsQwggAACCCCAQNYJEABl3ZDTYQQQQAABBBAgAGIZQAABBBBAAIGsEyAAyrohp8MIIIAAAgggQADEMoAAAggggAACWSdAAJR1Q06HEUAAAQQQQIAAiGUAAQQQQAABBLJOgAAo64acDiOAAAIIIIAAARDLAAIIIIAAAghknUB+kHrcu3fvIHWnU1/y89uHKyj9LKgp6NRHk1Fq/U3wVtQMhdarHOsRisoNxotAfTL9NCTWOqTVam9ZBtusy3Ozw/Xr50k/Ky6uIvPz8sUv66aCggJpa2sT/U9qFygqKpKcHF1IgpN0jJOlQK1mGxsbk/XV9+8VFhaaBTQo/Wxt1W+aOKnJyrMT1ORZ0+m0CYqJU7J/svRzW+Kf5ganpX1FdLn6l/WozkCvTrTq1MDe6VWZ9knX9k6XaxUZTqFQSPy0btIvx6YmXYGQNHBtaWnx1fjZGbW8PF3wE6dABUDNzU7/bEoMl4l3wtFsUPqpK8y4qcXKTR64t8+ms+u0+ghaYud0Zkd083LZJRvS3oay6vNF9rGqdXpVFv6oOV1uhFCoLSR+WTfp1nRd//ilvRHMrj3VH6RB88jNTb4iTf6ua9QUjAACCCCAAAIIZE6AAChz9tSMAAIIIIAAAhkSIADKEDzVIoAAAggggEDmBAiAMmdPzQgggAACCCCQIQECoAzBUy0CCCCAAAIIZE6AAChz9tSMAAIIIIAAAhkSIADKEDzVIoAAAggggEDmBGwHQKeccors3r27U0sffvhhufDCCzvlk4EAAggggAACCHhVIOmFEFesWCGLFy82bX/99dfltttuk+Li4o6+6IWTnnnmGRk2bFhHHk8QQAABBBBAAAGvCyQNgIYOHSrXXnutuTy2XiHy+eefl8hLS+t9VAYPHiw/+9nPvN5P2ocAAggggAACCHQIJA2A+vXrJ0uXLjUTT5w4UebNm+ebm9119JAnCCCAAAIIIIBAjEDSAChy2oULF5otQZ9++mmnG6bpjdT23XffyMl5jgACCCCAAAIIeFbAdgD05JNPyiWXXCI7d+7s1JkLLrhA/vrXv3bKJwMBXwv0tlqvd+bORLJ9ekImGkedCCCAgP8FbAdAV1xxhZx99tly+eWXS2VlZVTPdQsQCYHACRRYPdJ4//UM9OyrVp2HZqBeqkQAAQSyRMBWALRr1y7ZsmWL/OY3v5F99tknS2joJgKWwOciu9oWpJ2i7N/nWQFQTtrrpUIEEEAgWwRsbWgvKyuTIUOGyKpVq7LFhX4igAACCCCAQIAFbG0B0v7ffPPNcs0118iGDRtMMJSfv3dW3Sp02GGHBZiJriGAAAIIIIBAkAT2RjFd9EqPAaqpqZFp06Z1mpKDoDuRkIEAAggggAACHhawHQB98cUX0tbWFrcrkRdHjDsBmQgggAACCCCAgIcEbAdA1dXVCQOgkpISqaio8FC3aAoCCCCAAAIIIJBYwHYANHz4cLMLLF5R7AKLp0IeAggggAACCHhVwHYApDdD1ZufhlNtba289dZbcu+995rT48P5/EcAAQQQQAABBLwuYDsA+upX9cps0en444+XPXv2yK233ip/+MMfot/kFQIIIIAAAggg4FEBW9cBStZ2vQfYv//972ST8B4CCCCAAAIIIOApAdtbgN544w1paWnpaLw+37x5s9xyyy1y4oknduTzBAEEEEAAAQQQ8LqA7QBowoQJcQ+C1t1gs2bNst3PxYsXy0knnSSRF1Lctm2bvPnmmzJ48GA59NC9N0BqbGw0V5/OycmRUaNGSUGB3pyJhAACCCCAAAIIpCZgexfYp59+au4Er3eDDz+am5vltddek4EDB9pqhd5R/qabboo6mHr16tVy8cUXy0cffSTXX3+9PP3006as+vp6mTJliixZskQee+wx816i6xDZqpyJEEAAAQQQQACBLwVsbwHS+4Fp4DNv3jwTrFRVVcmIESPM1pzCwsKkoLq7bObMmbJ9+/ZO082dO9fcZuPII4+Ub3/723LJJZfIGWecIfPnz5fRo0fL9OnTzTx6BeoVK1bImDFjOpVBBgIIIIAAAggg0B0B2wHQu+++K7obTK8IfcQRR8jWrVtl06ZNcuqpp5qtNsXFxQnr1dPnNXA5/fTTTcAUnlADI723mJanSbcklZaWyueffy7r1q0z9YWnHTlypKxduzYqAHr44YdF26VJd6n96le/Ck8eyP/hXYB9+/YNRP+KdhXF70eZlW1nb6cuvfqIf4Hy+GV3J9dOG7pTHtMiYEdAf086/RHXz0mOC+VG9EfXT35ZN+ndC3SPgl7El9QuoBbh75igmEQetxyvT7YDoMsuu0y+9rWvyd133y3777+/WXjefvttOffcc+Wuu+6SG264IV75Jq+oqEgmTpzY6f0tW7ZIr169RI/xCafy8nLRq07rAda61Smc9LkGS5FJp9u4caPJ0gU66LfkCDsFpZ85uXvHPXJcJc96pY+uUngHrlsBUILmddUs3kcgJQFd7uws/92pRD8rTparZcV87nKsCvyybsrNzTXfYeF1ancogzqtWvhl/OyOQeS1C+PNYysA0mv9rFq1ymxt0eBHk2LpLrBrr71WnnvuuaQBULyKNU+xYxuoEZtuTYp9T/Njo3WtOzLpFqkgJ73diLpr4BeE1NDQEL8bO6xsO18Afazp6q3H3pMT45fX01zu7tJTOeZLRaDRmrkmlQLizKufFd3Y0fkohDgT28iKCX50jqbmpriHOdgoLe2T6HdJKBQSPdGGJKKHtOj3fF1dXaA4dONLshT+DZ1sGglHywoUmxSsq81MsfOEX/fr18+ARy6E+uW+3377Sf/+/aO+6MP54Xn5jwACCCCAAAII9FTAVgCkW2T0Wj+6m2vlypVm06H+el+0aJH87ne/k1NOOaVH9etxO3qg88KFC838r776qrmpqm7pGDt2rClf69HT5JctW2a2OPWoImZCAAEEEEAAAQQiBGztAtPpf//738s555wjxx57rAwYMEB2794teqr6mWeeKdddd11Ekd17esUVV3Sc/q5bmvRsMU3jx4+XpUuXyuTJk80WqEmTJsmQIUO6VzhTI4AAAggggAACcQRsB0DDhg0zFyt86aWX5IMPPjDH6Rx11FFy3HHHxSk2cZZeNygyHXjggeaUdz3FPvIMAt06pBdY1Juu6v7ayAsnRs7PcwQQQAABBBBAoLsCtgMgLVgPKNJT2fU6PZriHRNk3ujBn8jgJ3L2Pn306D0SAggggAACCCDgnICtY4C0Oj3DSq/Y/Mgjj5ja9eBn3SV155139vggaOe6QUkIIIAAAggggIB9AdsB0DXXXCOffPKJfP3rXzel624pvYqzHgQdPojZfrVMiQACCCCAAAIIZE7A1i6wpqYmefHFF+W9994zp6hrc/WAZT1AWY/R0dtj6AURSQgggAACCCCAgB8EbG0B0ovv6Y1P9RYVsWnXrl1R1+uJfZ/XCCCAAAIIIICA1wRsBUB6f5Bx48bJjBkz5LPPPuvog14dWo8B0vuBkRBAAAEEEEAAAb8I2AqAtDN641G9KOEBBxwger8uPQZo1KhR5gKJsbek8EvnaScCCCCAAAIIZKeArWOAlKayslL0Gj4ffvih6E1Q9Rigww8/XL7yla9kpxy9RgABBBBAAAHfCtgOgLSHGvQMHz7cPHzbYxqOAAIIIIAAAlkvYHsXWNZLAYAAAggggAACgREgAArMUNIRBBBAAAEEELArQABkV4rpEEAAAQQQQCAwAgRAgRlKOoIAAggggAACdgUIgOxKMR0CCCCAAAIIBEaAACgwQ0lHEEAAAQQQQMCuAAGQXSmmQwABBBBAAIHACBAABWYo6QgCCCCAAAII2BUgALIrxXQIIIAAAgggEBgBAqDADCUdQQABBBBAAAG7AgRAdqWYDgEEEEAAAQQCI0AAFJihpCMIIIAAAgggYFeAAMiuFNMhgAACCCCAQGAECIACM5R0BAEEEEAAAQTsChAA2ZViOgQQQAABBBAIjEB+YHpidSQ3NzviuaD0MycnJ/7ip9l2hlKnsztt/JrIRcB7Am4s01qmJjufq/Ypu/1XP89+WTeF2xn+3+3OBnAGP42fU/yBCoD69OnjlIsny8nPzxddSIPSz4KdBfGde1vZCd6KmkGn0RV6W1Sucy8C9elwjoWSXBbQ5drpVZmWqUGQ0+VGUOTn5ftm3ZSXl2daXlhYGNGD7H5aVFQkYZegSLS2tibtSqBW8TU1NUk76/c3KyoqTAAUlH42NTXFH5JaK7t9/RT//XCurszrrUdLOMPh/xUOl0dxCNgR0I+F06sy/ayUuFBuRH+aW5rFL+umkpISCYVC0tjYGNGD7H2qHg0NDVJXVxcoBA3qkiUXN4gmq5b3EEAAAQQQQACBzAkEagtQ5hipGQEEEHBAoMwqQ3+0Or31Udf0+nO3q3J1j8Eu60FCIAsECICyYJDpIgII+ERAd1NVW4/1GWjvPladg6wHAVAG8KkyEwIEQJlQp04EEEAgkcCbVgyye0Gid13LL/vkJCsA6uda+RSMgNcEOAbIayNCexBAAAEEEEDAdQECINeJqQABBBBAAAEEvCZAAOS1EaE9CCCAAAIIIOC6AAGQ68RUgAACCCCAAAJeEyAA8tqI0B4EEEAAAQQQcF2AAMh1YipAAAEEEEAAAa8JEAB5bURoDwIIIIAAAgi4LkAA5DoxFSCAAAIIIICA1wQIgLw2IrQHAQQQQAABBFwXIABynZgKEEAAAQQQQMBrAgRAXhsR2oMAAggggAACrgsQALlOTAUIIIAAAggg4DUBAiCvjQjtQQABBBBAAAHXBQiAXCemAgQQQAABBBDwmgABkNdGhPYggAACCCCAgOsCBECuE1MBAggggAACCHhNgADIayNCexBAAAEEEEDAdQECINeJqQABBBBAAAEEvCZAAOS1EaE9CCCAAAIIIOC6AAGQ68RUgAACCCCAAAJeEyAA8tqI0B4EEEAAAQQQcF0g3/Uauqhg9+7dsmbNmqipxowZY143NjbKqlWrJCcnR0aNGiUFBQVR0/ECAQQQQAABBBDoiUDGA6CVK1fKPffcI0OHDu1ovwZA9fX1MnXqVDnssMNk48aN8sQTT8icOXNMMNQxIU8QQAABBBBAAIEeCGQ8APr4449l4sSJctFFF0U1f/78+TJ69GiZPn26yZ82bZqsWLFCwluHoibmBQIIIIAAAggg0A0BTwRAGtTMmzdPhg0bJsccc4zZyrNu3TqZMGFCR1dGjhwpa9eujQqANHiqrq420+Tm5spBBx3UMX0Qn2gfNRUWFgaie3m5efH7oXs67SyZOrtO69aRbG6VG7/X5CLgDYEerl50/eSXdVN+fr6EQiFpa2vzhrkHWpGXl+eb8bPLFf7OTDS9na+ZRPM6kq9BjCYNgh599FHRLT+zZ8+WzZs3S1lZWUcd+nzDhg0dr/XJXXfdJYsXLzZ5ukC/9957Ue8H9UW/fv0C0bWinUUiNXG6UmHl2V0Jl8SZnywEEOi5gJ3VS45VfEzsUGgdoxmUdVPP8fw7Z69evUQfQUp79uxJ2p2MB0APPfSQ9O3bVzRSO/PMM+Wss84ygY5Go62trR2Nb2lpkZKS6G+7n//853L11VebafRA6a1bt3ZMH8QnGgRqP2tq4kUN/utxQ31D/EZvt7LtLJn6WdUi9i4m8cvrae7e+LunJTAfAv4TsLMajQl+tJNNTU2+WQcXFRWZrT/aZpJI//79pa6uzhx7GyQP3TBSWlqasEt2vmYSzpzqG7rwrV+/XiorK01Ruvl04MCBZuuPDkh495a+qc8HDRoUVeW+++4b9XrTpk1Rr4P2Iry5VoPBIKRQWyh+N+wGNDq7TusWR5yVfPwGk4tAgAR6+HkKWbuT/LJu0jOKdReYX9qbjqUriB66ISVZyuhRDroQ6pldy5cvN218//33Zfv27XLUUUfJ2LFjZdGiRdLQ0CDbtm2TZcuWyYgRI5L1hfeCKKDBe+8ED91Nluz9RPPZzc/oz4MgDiZ9QgABBLwjkNFVvO7O0bO8HnzwQfPQXVgzZswQ3Ww1fvx4Wbp0qUyePNnsHps0aZIMGTLEO3K0xH0BPc6g3HrUW4/mONU1WnlubqXJ6M+DOP0lCwEEEEDAMYGMBkDai6OPPloeeOABc1xL+BgXzdcgaNasWVJbW2uO/dHXpCwVeP4/sktWp73zZePOT3udVIgAAgggkB4Bz0QV5eX6U79z6tOnT+dMchBAAAEEEEAAgRQE2MifAh6zIoAAAggggIA/BQiA/DlutBoBBBBAAAEEUhAgAEoBj1kRQAABBBBAwJ8CBED+HDdajQACCCCAAAIpCBAApYDHrAgggAACCCDgTwECIH+OG61GAAEEEEAAgRQECIBSwGNWBBBAAAEEEPCnAAGQP8eNViOAAAIIIIBACgIEQCngMSsCCCCAAAII+FOAAMif40arEUAAAQQQQCAFAQKgFPCYFQEEEEAAAQT8KUAA5M9xo9UIIIAAAgggkIIAAVAKeMyKAAIIIIAAAv4UIADy57jRagQQQAABBBBIQYAAKAU8ZkUAAQQQQAABfwoQAPlz3Gg1AggggAACCKQgkJ/CvMyKAAIIIBAYgT7tPfnyX3e7lZ+fL3369HDmLyurq6uTUCjU3aqZHoEeCRAA9YiNmRBAAIGACVQViuRYfWrsWb80cGls7NnMOTk5UllZKS0tLVJfX9+zBjAXAt0UIADqJhiTI4AAAoEU0ODH2viya8mCLrtXvu84K1iKPoLinbffke/ecF+X88aboLS0VJ566ql4b5GHgGsC0Uuwa9VQMAIIIIAAAggg4B0BAiDvjAUtQQABBBBAAIE0CRAApQmaahBAAAEEEEDAOwIEQN4ZC1qCAAIIIIAAAmkS4CDoNEH7uRo9Q8ONlGNOOUlQslbpTrUJKiQbAQQQQCCbBAIVAFVUVAR67AoKCkz/0tnP3NxcKSy0To91IZXUfSxSG6fgfaw8d6qMUxlZCCDgFQE9G6y4uNj15uTl5UlbW5tofaR2gZKSEtfW9Zky1ssqJEuBCoB27NiRrK++f08DH90ak85+9urVS3RlMWfOHMf91h9cIlJV1Lnc5z6zTsdts/LLRc4PdlDbufPkIJC9Anv27EnLdYD0yz6V6xYFbYSqqqqMu16IMkipqCjO90tEBwMVAEX0i6cOCugvpZdfftnBEtuLKikeIUVVB3Uqt1bekTZpkjKxAiQ5o9P7ZCCAAAIIIJCqAAdBpyrI/AgggAACCCDgOwECIN8NGQ1GAAEEEEAAgVQFCIBSFWR+BBBAAAEEEPCdAAGQ74aMBiOAAAIIIIBAqgIEQKkKMj8CCCCAAAII+E6AAMh3Q0aDEUAAAQQQQCBVAQKgVAWZHwEEEEAAAQR8J0AA5Lsho8EIIIAAAgggkKoAAVCqgsyPAAIIIIAAAr4TIADy3ZDRYAQQQAABBBBIVYAAKFVB5kcAAQQQQAAB3wkQAPluyGgwAggggAACCKQqQACUqiDzI4AAAggggIDvBAiAfDdkNBgBBBBAAAEEUhUgAEpVkPkRQAABBBBAwHcCBEC+GzIajAACCCCAAAKpCuSnWgDzp0cgN3dvrBr53O3ac3Jy3K6C8hFAAAEEEEi7AAFQ2sm7X2FFRYUUFxd3zDhw4MCO5+l40tLSko5qqAMBBBBAAIG0CRAApY06tYreeustWbRoUWqF9GDuCy64QA466KAezMksCCCAAAIIeFeAAMi7YxPVsk2bNslrr70WlZeOF+PGjSMASgc0dSCAAAIIpFWAACit3FSGAAIIIJBIoKCgQNra2hK97Vh+uJ7IYxybm5ultbXVsTooyPsCBEDeHyNaiAACCARaoKioyPSvV69eoo9MpIaGBtmxY0cmqqbODAkQAGUInmoRQAABBNoFwme23nPPPfLqq6+mneW6666TI488Mu31UmFmBQiAMutP7QgggAACXwrU19fLrl270u7Bma5pJ/dEhXsvLuOJ5tAIBBBAAAEEEEDAfQFPbwFqbGyUVatWiR6oNmrUKNED10gIIIAAAgg4KaDHIOluuMjrrTlZfldl5efnC1uhulJy/n3PBkC6KXTq1Kly2GGHycaNG+WJJ56QOXPmmGDIeYauS9QgLPKMga7nYAoEEEAAAT8IDB48WAoLC83DD+11uo165p1+v4WPxXK6/GTlhUKhZG+7+p5nA6D58+fL6NGjZfr06QZg2rRpsmLFChkzZoyrIPEKz8vLkwEDBmQsAOKXQbxRIQ8BBBBwRkC//NesWSO//OUvnSmwG6VMmjRJzj//fLn88stl69at3ZjTmUkff/xx0S1Qffr0MQ9nSrVfiu7pqa6utj+Dg1PmWJGf+xdd6EGDb7zxRpkwYYKccMIJZu4HHnjA7ALTrULhdNNNN3VcHFAH8Nlnnw2/5fh/LT9TSQMgvUZFJg4OLC8vN5uFv/jiC8e7P3/rO7Js1/pO5YYOLBPJtw5P0yPUBliPGuvR0Gky9zP6W1XoXUB2ul9Vpxp6Wzl6NvA265GJS5No3+utR531SHeqtCrUsde+pzvpHWfKrYeuj5vTXblVX78v603/ccDt/db+2/io5/xnp8TeJXB46T7y//bt2Q9U3fKgPzJrampET0dPd+rfv7+5BlAmToPX0/579+4t27Zty8h1iLTv+iM/U8nNH/gaXCW7rELmvtW70N68ebOUlVlfhF8mfb5hw4bwS/Nfd4+FB07/a2fdSrqZLlO7wLRePf5JVxCa0n2xLl1AKyv1W8nZVLSr/dofsaXeV3GalBeUmOzc1lwJlVqbSEtjp7K+I62VppubT3ParFW8tV5oq8zMbwTT93L7m4d1GdGxcuI3Ta4VgYSKrLrjD1HnwXAwx3zOLPJU3XWd0JPPinHvY9/dwa5bQYW1zFmHOqba99g26WdFH1192eS05Niqe8p/H5HWmN/Ouvylsp7QsSotLTWP2PY7/Tq8Lg9/VvS/+qTS/lTaqH3XH5uZStp/XZe6uT5N1Dets6vlMtG8XeV39fn3bAAUu/JSoJKS9i/FcKfPO++88FPzX28XEeSkN0XVD26mNhc6bZvol572rzmv629e3WSrx4q59eFxur9ulqcrb71Jrm4ldPOHgJt9cLps/bxk4he90/1wojz9rOj6U7cyOJFiYh9TZFNTk2PlO9HGZGWohX7x8llpV6qqqpK6ujrzSObmt/fCF9hM1G7d0OzJpJvlIr/o9fl+++3nybbSKAQQQAABBBDwl4BnA6CxY8eau5/rVgL91bJs2TIZMWKEv3RpLQIIIIAAAgh4UsCzu8DGjx8vS5culcmTJ5t9s3qk/JAhQzyJSKMQQAABBBBAwF8Cng2A9KyrWbNmSW1trdl3ncmzsPw1pLQWAQQQQAABBLoS8GwAFG64HrxHQgABBBBAAAEEnBTw7DFATnaSshBAAAEEEEAAgUgBAqBIDZ4jgAACCCCAQFYIEABlxTDTSQQQQAABBBCIFCAAitTgOQIIIIAAAghkhQABUFYMM51EAAEEEEAAgUgBz58FFtnYrp4H/VT5zz//3NznKZP3jOlqDLrz/qDiCqkNNXWapTi/UPLzul409VL2eguIoI97J6A4GXrPm7Vr15r7CRUWFsaZIvuy1IRlo33c9WKyelHZfv30bqupp8N7VVn36I2+R94BRX195a23FWL5aF8WPvzwQ3O5Gb0XW5BS+F6hifrk2bvBJ2pwNudffvnlZiX2pz/9KZsZ6HscAb0P3oknnij33XefnHzyyXGmICubBW6//XZ57rnn5JVXXslmBvqeQODII4+UH/3oR3LppZcmmCKY2ewCC+a40isEEEAAAQQQSCJAAJQEh7cQQAABBBBAIJgCXR9oEcx++7JXZ555puhxDSQEYgX0iulXX321HHzwwbFv8RoBOf7446WqqgoJBOIK/PCHP5Rjjjkm7ntBzuQYoCCPLn1DAAEEEEAAgbgC7AKLy0ImAggggAACCARZgF1gHhldPU31zTfflMGDB8uhhx7aZauWLFliNlmGbxbb2Ngoq1atEj21c9SoUVJQUNBlGUzgHwE9TXX9+vUycuRI6d+/f9KG67SawsvR7t27Zc2aNVHzjBkzJuo1L/wrYOez39Uy0J3ly79S2dlyO98tn376qWzcuLEDSC+XMGzYMOlquemYwadP8n5pJZ+2PTDNXr16tUyfPl369u0rDzzwgBQXF8vw4cMT9u/VV1+VX/ziF3LaaaeZeerr6+Xiiy+Wuro6Wbp0qWhw9M1vftMEQwkL4Q3fCNx5553yzDPPSFNTk9x7771y3HHHmev9xOvAzp07Rffn9+7dW4466igziS4Tc+fOlS1btsgHH3xgHqecckq82cnzmYDdz36yZaA7y5fPeLK+uXa/W+644w55/fXX5ZNPPjHrh5aWFjniiCPM90mg1x1tpIwL/OAHP2h7++23TTs2b97c9q1vfavN+lUXt11bt25tu+iii8w01sJqprGuC9RmrcQ6pr/sssva/vWvf3W85ol/Bf73v/+1nXPOOW3Wwe+mE3/5y1/abr311oQd+ulPf9o2ZcqUNl0mwskKqtsefvjh8Ev+B0jA7mc/0TLQ3eUrQHRZ0RW73y2TJk1qs7YwdzJJtNx0mtCnGRwDlOHfCBppb9iwwUTb2pSBAweKXo1Tr/ocm6xlTG677TZzwSrdShRO69atM7tGwq91N4leFZjkf4H//ve/ZtnQK15rSja2zz77rFRUVJhdoJE9//jjj80WoXnz5snKlSvN1cQj3+e5fwXsfvYTLQPdWb78q5SdLbf73bJnzx6prq4W68e1PPbYY+b7KCyWaLkJv+/3/wRAGR5B3S3Rq1evqN1VeqsLXSBj04IFC2TQoEGdTle0thpJWVlZx+T6fPv27R2veeJfAb3Cc+StTxKNrQbRTz31lAmOY3urK7Hly5dLUVGRPProo/KTn/wkdhJe+1TA7mc/0TJgd/nyKU9WN9vud8t//vMf0ePI9MeRXmZFD8d4/vnnjV2i5SYosBwEneGR1HuVxF7bRyP3yC082kRrU7UsWrRI7r///k4tji1D5y8pKek0HRn+E7Aztjret9xyi/z4xz+OO+4PPfSQOVZMtyLptaTOOuss8ytv//339x8ILY4SsLN86AyJlgG780dVygtfCMSOrTY63neLHm/69NNPm63HOs3QoUPN8nLGGWckXG6Csu4gANIRz2DSo+314GWNwPUXuibd+rPffvtFterll182ZwHpF5gmPfjxkksuMV98elZQ5BYjfa5bikj+FxgwYIC8++67HR3Rsd133307XusT61gwef/99+Waa64x+c3NzWaLop79cdVVV5nlprKy0rynN0rV3ay65SAoKzHTsSz9Y+ezrwfP6xmE8ZYBO8tXltL6vtt2v1v0xIldu3Z1BEAHHHCAfPHFF+a+k4mWm6CsO9gFluHFXO9GPHr0aFm4cKFpiZ7hpcdx6EOTfrnpXZw12NEg6O9//7t56FVd//jHP8qxxx4rY8eONVuHdDr90lu2bJmMGDHCzM8ffwvoJQ30FPbPPvvM/HrT43x0zDVpEKwrKP3F9s9//rNj2bjgggvEOvjRbBHSyyHMmTPH7ALTeTRQ0t2j4TPENI/kX4Fkn/3wuiPZMpBs+fKvCi1XgWTfLeF1h05XU1Njfjxpnh5nqjfN/cY3vmF+kAd93cGVoHUJyHDSL7Hrr79edJOl7qaYOXOmHHLIIaZVeqq7Hvisd+uNTPolN3v2bDnwwAPNF+OvfvUr80Wp81tH9Iu+TwqGgK6Q7r77bvMLXsf75ptvNis3ve7TTTfd1BE8h3urd4TXA+mts8FMll5f6sEHHxTdMqQHOs6YMcOcSh+env/+FdBdGok++5HrjmTLQKLly78qtDwskOi7JXbd8cgjj8hLL71kvkv0OMNZs2bJPvvsY65NF+R1BwFQeEnxwH/dFKnXAuppqq2tNceAaORPCpaABi+6m1Sv79PTpL/0dOWmF8skBUvA7mc/0TLgxPIVLNFg9cbOd0soFDIXPtR1RGxKtNzETue31wRAfhsx2osAAggggAACKQtwDFDKhBSAAAIIIIAAAn4TIADy24jRXgQQQAABBBBIWYAAKGVCCkAAAQQQQAABvwkQAPltxGgvAggggAACCKQsQACUMiEFIIAAAggggIDfBAiA/DZitBcBBOIK6NVsu5O6O313ymZaBBDwvgABkPfHiBYigEAXAi+88IKceuqpXUy19+3uTr93Tp4hgEBQBAiAgjKS9AOBLBbQ24XoPfXspu5Ob7dcpkMAAf8IEAD5Z6xoKQJZLbB69Wq5+OKL5eSTT5ZLL71U3njjDePxyiuvyJNPPmnucK/5O3bsMPl637SpU6ea6fXeaIsWLUo6fVbj0nkEslCAACgLB50uI+A3Ab079YknnijFxcUm+NHbeRx33HHywQcfmHsW6d2p9f5neqNYveP9vffeKxdeeKEcdNBBctFFF8mePXvk9NNPl5UrV8ad3m8etBcBBFIX4KZRqRtSAgIIuCygu6w0iNEbf+pNGr/zne/I8OHDzd2r9f/o0aNl3bp1JjjSpmzZskXuuOMOueyyy0zLNBgaMGCALF++XK688spO07vcfIpHAAEPChAAeXBQaBICCEQLaIBz8MEHy7Bhw2TChAmidzrX3Vr9+vWLnvDLVxoobdq0SZ566imzleidd96R+vp6aWhoiDs9mQggkH0C7ALLvjGnxwj4TqB3797mmJ9f//rXZkvQFVdcYXZvLVmyJG5f5s6dK0OGDBGdXrcGnX/++WbLUdyJyUQAgawUIADKymGn0wj4S+Cjjz4SPahZA5/nn3/eBDW66+uee+4xHdFjgsJJt/TccMMNcvvtt5tjfjQYOvfcc808oVCo0/Th+fiPAALZJUAAlF3jTW8R8K3AlClTZMGCBdLa2ip6EUM920t3i2mqrKw0u7w0UNKku8Y2b94sGvDosUNXXXWVNDY2duwCi5y+paXFzMMfBBDILgECoOwab3qLgC8FDjnkEJk9e7bceOONUl5ebnZvHX744ea1duiEE06QvLw8OfTQQ0WP9/ntb38rjz/+uNntVVVVJboLbdKkSaKn0sdOv2rVKpPHHwQQyC6BnDYrZVeX6S0CCPhZQLfs9O3b15wSH9uPnTt3mvfC+Rs2bBANgPLz45/vETt9eD7+I4BA8AUIgII/xvQQAQQQQAABBGIE2AUWA8JLBBBAAAEEEAi+AAFQ8MeYHiKAAAIIIIBAjAABUAwILxFAAAEEEEAg+AIEQMEfY3qIAAIIIIAAAjECBEAxILxEAAEEEEAAgeALEAAFf4zpIQIIIIAAAgjECBAAxYDwEgEEEEAAAQSCL0AAFPwxpocIIIAAAgggECPw/wEd9KEASGyMxgAAAABJRU5ErkJggg==" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> p_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -949,7 +967,7 @@ <h3>One categorical (one proportion)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="one-categorical-variable-standardized-proportion-z-1" class="section level3">
 <h3>One categorical variable (standardized proportion <span class="math inline">\(z\)</span>)</h3>
@@ -980,6 +998,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -997,8 +1018,11 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1016,7 +1040,7 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="one-numerical-variable-one-categorical-2-levels-t-1" class="section level3">
 <h3>One numerical variable, one categorical (2 levels) (t)</h3>
@@ -1043,6 +1067,9 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1060,8 +1087,11 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1079,7 +1109,7 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-diff-in-proportions" class="section level3">
 <h3>Two categorical variables (diff in proportions)</h3>
@@ -1106,6 +1136,9 @@ <h3>Two categorical variables (diff in proportions)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1123,8 +1156,11 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1142,7 +1178,7 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-z" class="section level3">
 <h3>Two categorical variables (z)</h3>
@@ -1169,6 +1205,9 @@ <h3>Two categorical variables (z)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1186,8 +1225,11 @@ <h3>Two categorical variables (z)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> z_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1205,7 +1247,7 @@ <h3>Two categorical variables (z)</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---slr-1" class="section level3">
 <h3>Two numerical vars - SLR</h3>
@@ -1232,6 +1274,9 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1249,8 +1294,11 @@ <h3>Two numerical vars - SLR</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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AIIIIAAAj4XIAHyeQNSfQQQQAABBBBIX4AEKH0zxkAAAQQQQAABnwuQAPm8Aak+AggggAACCKQvQAKUvhljIIAAAggggIDPBUiAfN6AVB8BBBBAAAEE0hcgAUrfjDEQQAABBBBAwOcCJEA+b0CqjwACCCCAAALpC5AApW/GGAgggAACCCDgc4HCXNd/yZIlcuKJJ0ph4Vez3r17t7z99tvdqjFx4kTzubm5WdasWSN5eXkyfvx4iUQi3YbjAwIIIIAAAggg0BeBnCZAixYtkrlz58rxxx8fS4BWr14t8+bNk5EjR8bqrwlQY2OjXHzxxTJ69Gj58ssv5fHHH5c5c+aYZCg2IG8QQAABBBBAAIE+COQkAWpra5MbbrhBtm/f3qOKH374oZx55ply4YUXduu3cOFCmTBhgsyaNct0nzFjhqxcuVKsvUPdBuYDAggggAACCCCQhkBOEqD29naTuJx22mnm8Fd8/TQB0qRmwYIFMmrUKDn66KPNXp7169fLlClTYoOOGzdO1q1b1y0Beu655+Sjjz4yw+Tn5/dIomIju/DGOlxXXl7uwtwzP8toXdR2ogVdhzL1cKb6W4c1bQf0YUeNieKegFvLUxCXZW3FvPw828YsKyuT8miZbb+gdLS+yyUlJVJUVBSUsJLGEY1GpaCgQHTbG/SiseppMnbb2s7OzpTh5yQB0gVO9/LYFU2AtGgS9PDDD4vu+bnzzjtl06ZNUllZGRtF32/YsCH2Wd+88sorsnTpUtNNE47LL7+8W383P2iDaCktLXWzGhmbd2HEflHRL5luqDRetzZYGQsyYUJWGyZ05mMOBNxcnnSZtjaYOQg1Z7NIFlNxV1JQWhSM9VRvmMXFxb0NEoj++v3RbWJvCUAQgrXW03bb2qamppQh2m/VUo6S2Z4PPvigDBgwwKxwzjjjDDnrrLNMopOYvephNM3e48utt94a/1E2btzY7bObH/RXVUVFhWzZssXNamRs3g0NDbbTauk6UV068kWz8JaWFtth/NpRY6K4I6Arbr0IItdF1zGtra2i65uglfY2+70B27dtk86I/fc7KAaaDAwePFh27txp2jcocSWLQ/eG6PdHl+WgF7MHsyteu22t7nzR/smKq/v4dYP56aefxn5t6QZn2LBhZu+PLqw7duyI1VvfjxgxIvaZNwgggAACCCCAQF8FXE2ANCvXK7teffVVU/93333XnCg9ZswYmTRpkixevFh0F9a2rl8oK1askLFjx/Y1TsZDAAEEEEAAAQRiAq4eAtNjd3qV1/z5883f1q1b5dprrzXnkkyePFmWL18u06ZNM3uIpk6dKtXV1bGK8wYBBBBAAAEEEOirQM4ToGXLlnWr61FHHSX333+/1NTUmJOerROa9ITa2bNnS11dnTn3J2gn2HZD4AMCCCCAAAII5FQg5wlQsuiqqqpse+mJxBQEEEAAAQQQQCCTAq6eA5TJQJgWAggggAACCCDgVIAEyKkUwyGAAAIIIIBAYARIgALTlASCAAIIIIAAAk4FSICcSjEcAggggAACCARGgAQoME1JIAgggAACCCDgVMAzV4E5rTDDhUtAb4tg3Roh15G7Nd9cx8n8EEAAgTAKkACFsdV9FLM+vJBExEcNRlURQAABnwiQAPmkocJaTU1+9BEpdg+6y7bJsccem+1ZMH0EEEAAAZcESIBcgme2zgX0qcb19fXOR8jQkB0dHRmaEpNBAAEEEPCaACdBe61FqA8CCCCAAAIIZF2ABCjrxMwAAQQQQAABBLwmQALktRahPggggAACCCCQdQESoKwTMwMEEEAAAQQQ8JoACZDXWoT6IIAAAggggEDWBUiAsk7MDBBAAAEEEEDAawIkQF5rEeqDAAIIIIAAAlkXIAHKOjEzQAABBBBAAAGvCZAAea1FqA8CCCCAAAIIZF2ABCjrxMwAAQQQQAABBLwmQALktRahPggggAACCCCQdQESoKwTMwMEEEAAAQQQ8JoACZDXWoT6IIAAAggggEDWBUiAsk7MDBBAAAEEEEDAawIkQF5rEeqDAAIIIIAAAlkXIAHKOjEzQAABBBBAAAGvCRR6rUL9qU9eXl5/Rs/ouFZdrNeMTtyFieVJatugxOkCLbNEwFUB/e4G/fsbH1/8e1fhszzzMLSrElrtab2mwxqoBKiqqiqd2LM6bGHhV7ReqlN/Ao7uLrIdPRKNSDQSNQthNBq1HYaOCKQroCszt5Yn/e7m5wdv53h+gX1MFZUVUhUtT7eJfDW8tXEsLy+Xzs5OX9W9L5XVZTgSiYQmVm1fu21tW1tbSr5AJUC7du1KGWwue5aVlUlFRYV4qU79ib+5ucl29NaWVsnrbDEbq5aWFtth+tPRSiT7Mw3G9Z+AbqSysTz1JlFSUiK60uxtxdnbdLzYv6O9w7ZatTW1UhhJvaGwHdFHHTUZKC4ult27d0tra6uPat63qmqi19zcHIpYdVur8dpta4uK7H+4W6r2PwmsvrwigAACCCCAAAIBFCABCmCjEhICCCCAAAIIpBYgAUrtQ18EEEAAAQQQCKAACVAAG5WQEEAAAQQQQCC1AAlQah/6IoAAAggggEAABUiAAtiohIQAAggggAACqQVIgFL70BcBBBBAAAEEAihAAhTARiUkBBBAAAEEEEgtQAKU2oe+CCCAAAIIIBBAARKgADYqISGAAAIIIIBAagESoNQ+9EUAAQQQQACBAAqQAAWwUQkJAQQQQAABBFILkACl9qEvAggggAACCARQgAQogI1KSAgggAACCCCQWoAEKLUPfRFAAAEEEEAggAIkQAFsVEJCAAEEEEAAgdQCJECpfeiLAAIIIIAAAgEUcJwAnXTSSbJ79+4eBA899JBccMEFPbrTAQEEEEAAAQQQ8KpAYaqKrVy5UpYsWWIGeeWVV+S2226T4uLi2Cjt7e3y9NNPy6hRo2LdeIMAAggggAACCHhdIGUCNHLkSLnqqqukublZWltb5bnnnpOCgoJYTJFIRPbff3+57rrrYt14gwACCCCAAAIIeF0gZQK0xx57yPLly00MZ555pixYsEDKy8u9HhP1QwABBBBAAAEEUgqkTIDix3zmmWfMnqDPPvvMvMb306Rozz33jO/EewQQQAABBBBAwLMCjhOgRYsWySWXXCK7du3qEcwPfvAD+dvf/tajOx0QQAABBBBAAAEvCjhOgGbOnClnn322XH755TJo0KBusXBYrBsHHxBAAAEEEEDA4wKOEqDa2lrZsmWL3H777TJ06FCPh0T1EEAAAQQQQACB1AKO7gNUWVkp1dXVsmbNmtRToy8CCCCAAAIIIOADAUd7gDSOm2++Wa688krZsGGDSYYKC78ZVfcKjR492gfhUkUEEEAAAQQQQEDkmyymFw09B6impkZmzJjRY8h0ToLWGyueeOKJEp9Abdu2TV577TVzT6GDDz44Nn29/5DudcrLy5Px48eL3neIggACCCCAAAII9FfA0SEwncnmzZulsbHR9u/RRx91VA+9kuymm24SvYO0Vd544w2ZPn26fPDBB3L11VfLU089ZXrpvC666CJZunSpPPLII6ZfZ2enNRqvCCCAAAIIIIBAnwUc7wHasWOHJEtASkpKZODAgUkr0dbWJjfccINs3769xzBz5841h9eOPPJIOe+888yl9qeffrosXLhQJkyYILNmzTLj6J4nfTTHxIkTe0yDDggggAACCCCAQDoCjhOgQw45xBwCs5t4b4fAdI+PJi6nnXaaOfxlTUMTIz2n6IgjjjCdhg0bJqWlpfLFF1/I+vXrZcqUKdagMm7cOFm3bl23BOidd94RPXymRQ+THXbYYbHh3X5jHeIrKipyuyoZmX9Bgf2ikpefL/ldf+qvr5ksOk1KeAUyvTw5ldT5ujVvp3Xsy3DJvk/RoqgURYKxnkrmYj3CSU+jCGLbJsat8YYlVt3W6rJtt63tra3tt2qJml2f9WGo8Yeu6urq5PXXX5d7773XXB5vM0qsk1ZMH6WRWPTS+rKyMlN5q19VVZXo3qZNmzaJXn1mFX2vyVJ8ue+++2IPa1UETYi8VhLvmeS1+jmtT0ntNw/BjR9H27Y4+lU/ayUT35/3CPRFQFdo8Q9e7ss0+jqOrkusHzB9nYYXx0v2/RxQNUAGFVd4scoZr5NuXyjBFLDb1jY0NKQM1nECZLd35X/+539EZ/DrX/9aHnjggZQzsuupX8j4pEqH0b1CuuJL7Kfd9VBbfLnxxhvlmmuuMZ10hakJlVeK7snS5G7r1q1eqVK/6lGfZEFqamqSzvY8iUaj0tLS0q952I2c2OZ2w9AteAJ6uF3PA8x10XWPrmv0L2ilrd0+Jj01oTOSe+tc+mpCqxtI/XEdxLZNtNTtj66PwxKrxmsdDYq30L1g2i9ZcZwAJZuAPgPs6aefTtY7ZXd92Gp9fb15tpi1+0oX0BEjRsjgwYPNwmpNQLvvs88+1kfzquPrn1U2btxovXX9taOjw9QhMcFzvWJ9rEBn51fx9Bi9a0OlGyvrr0d/OiCQpoAmIfqDxq2rPnW3ebLzHdMMxVuDJ7mGRNdR7fnfXJjirUpnpjbWoRBdLwdlnZxKRpffsMSaalvb255cxwnQqlWrumWTmlnqYapbbrlFTjjhhFRtkbSfVk5PdNYHrep5RC+//LI5mVpPqJ40aZIsXrzYvO7evVtWrFght912W9Jp0QMBBIIhYP1i0+99rovuea6oqDDniVgr1lzXgfkhgEBuBBwnQHpCst4HKLHoYbDZs2cndnb8We8vZF3+rlm6Xi2mZfLkybJ8+XKZNm2aWRlNnTrV3IDR8YQZEAEEfCugh1b1R0+ui55rqOs0CgIIBF/AcQL02Wef9dgtrOe49LaLKZFw2bJl3Trtt99+5pJ3fcr8gAEDYv10uppY6cnWeh5IuvOJTYg3CCCAAAIIIIBAgoDjBEh/GWmSsmDBAnPTwuHDh8vYsWPNZe16Amx/S3zyEz8t3R1NQQABBBBAAAEEMingOAF66623zH159I7Qet8evbpJTzo+5ZRTzN2b3bpkNZMYTAsBBBBAAAEEwiHg+M51l112mRx77LHy+eefy9q1a83NCvU+QO+995789re/DYcWUSKAAAIIIIBAIAQcJUB6rx99KKle8bX33nubwPUyVT0EdtVVV8lLL70UCAyCQAABBBBAAIFwCDhKgKz7YtjdVVHv4xOGmy2FY3EgSgQQQAABBMIh4CgB0vN79F4/etfl1atXm6vB9DJVvU/PPffcIyeddFI4tIgSAQQQQAABBAIh4CgB0kj/8Ic/iJ4Afcwxx4g+tFRvK64PNz366KPl5z//eSAwCAIBBBBAAAEEwiHg+CqwUaNGyWuvvSYvvviiOfFZ9wqNGTNGjjvuuHBIESUCCCCAAAIIBEbAcQKkEevzunSvz+mnn24A7M4JCowMgSCAAAIIIIBAYAUcHwLTe/5Mnz5d/vKXvxgMPfm5urpa7r77bk6CDuziQWAIIIAAAggEU8BxAnTllVfKJ598It/+9reNhD6eYu7cueYkaH2YKQUBBBBAAAEEEPCLgKNDYC0tLfLCCy/IO++8IyNGjDCx6aXx+qBSfVaXPh7jnHPO8UvM1BMBBBBAAAEEQi7gaA+Q3vSwtbXV3P050au2tlZ27NiR2JnPCCCAAAIIIICAZwUcJUCRSES++93vyrXXXmsehWFFo3eH1nOA9HlgFAQQQAABBBBAwC8CjhIgDeahhx4SvfnhvvvuK1VVVaLnAI0fP97cIFEfh0FBAAEEEEAAAQT8IuDoHCANRm98uGzZMnn//fflzTffFD0H6PDDD5dDDz3UL7FSTwQQQAABBBBAwAg4ToB0aE16DjnkEPOHHwIIIIAAAggg4FcBx4fA/Bog9UYAAQQQQAABBBIFSIASRfiMAAIIIIAAAoEXIAEKfBMTIAIIIIAAAggkCpAAJYrwGQEEEEAAAQQCL0ACFPgmJkAEEEAAAQQQSBQgAUoU4TMCCCCAAAIIBF6ABCjwTUyACCCAAAIIIJAoQAKUKMJnBBBAAAEEEAi8AAlQ4JuYABFAAAEEEEAgUYAEKFGEzwgggAACCCAQeIG0HoXhdY2BAwd6pooFBQWmLl6qU39wiuuLbUePRCMSjUTNY1Ki0ajtMHREwG8CkUhEOjs7/VbtlPXNL7D/vasPtx5YVJ5yXL/3zMvLMyFUVFQErl3t2ka3P7o+DtoynCxWbV+7bW1bW5vdKLFugUqAdu7cGQvM7TdlZWWiXzYv1ak/Jk3NTbajt7a0Sl5ni/mytbS02A7Tn46FhYFaRPtDwbg5FGhtbZWOjo4czjH7s+pot4+npqZGCiKt2a+Ai3PQhLaoqEjq6upE2zbopby8XJqbm0MRq25rNV67ba22eapi/5Mg1Rj0QwABBBBAAAEEfC5AAuTzBqT6CCCAAAIIIJC+AAlQ+maMgQACCCCAAAI+FyAB8nkDUn0EEEAAAQQQSF+ABCh9M8ZAAAEEEEAAAZ8LkAD5vAGpPgIIIIAAAgikL0AClL4ZYyCAAAIIIICAzwVIgHzegFQfAQQQQAABBNIXIAFK34wxEEAAAQQQQMDnAiRAPm9Aqo8AAggggAAC6QuQAKVvxhgIIIAAAggg4HMBEiCfNyDVRwABBBBAAIH0BUiA0jdjDAQQQAABBBDwuQAJkM8bkOojgAACCCCAQPoCJEDpmzEGAggggAACCPhcoNDn9af6CCCAQMYEotGodHZ2SlFRkXnN2ITTmFBbW5voHwUBBLIrQAKUXV+mjgACPhKIRCKSl5cnn376qTQ2Nua85vvvv78UFrJazjk8MwylAN+0UDY7QSOAQCqBL7/8Unbu3JlqkKz022uvvUiAsiLLRBHoKcA5QD1N6IIAAggggAACARcgAQp4AxMeAggggAACCPQU4BBYTxO6xAno+Qjl5eVSVF8c1/Wbt5FoRKKRqOTn54ueQEpBAAEEEEDADwIkQH5oJRfrqFfDFBcXy65du2xrsXv3bmkuyM4VK5pUDRgwwHa+dEQAAQQQQKA/AiRA/dELybjt7e3yr38tleJvH9wj4jffXCvtrZ09umeiQ2lpqZxwwgmZmBTTQAABBBBAoJsA5wB14+ADAggggAACCIRBgAQoDK1MjAgggAACCCDQTYAEqBsHHxBAAAEEEEAgDAIkQGFoZWJEAAEEEEAAgW4Crp8ErVcRvf32290qNXHiRPO5ublZ1qxZY25NP378eNHb1FMQQAABBBBAAIH+CrieAK1evVrmzZsnI0eOjMWiCZA+h+fiiy+W0aNHi96W/vHHH5c5c+aYZCg2IG8QQAABBBBAAIE+CLieAH344Ydy5plnyoUXXtit+gsXLpQJEybIrFmzTPcZM2bIypUrxdo71G1gPiCAAAIIIIAAAmkIeCIB0qRmwYIFMmrUKDn66KPNXp7169fLlClTYqGMGzdO1q1b1y0Bqq+vl9bWVjOMPsFZ/7xSrLpYr16pF/VAAAEE4gW8tu6Mr1um3sevh+PfZ2r6XpxOGNpV3a32tF7TaQtPJEBaYU2CHn74YdE9P3feeads2rRJKisrY7Ho+w0bNsQ+65trrrlGlixZYrrpIxveeeedbv298GH48OFeqEa/6tDWlp07PferUoyMQEAFdF2mf5kuhbvtpzlkyBAZUlyR6dl5cnqDBw/2ZL2oVP8F7La1DQ0NKSds/41IOUpmez744IPmcQf62IMzzjhDzjrrLJPoFBQUiN6B2Cq6ES4pKbE+mteLLrpITjnlFPNes7+dO3d26+/mB32EhNY32SMk3KxbOvPWOHjGVzpiDItA/wR0vZeNHx3x69P4GtbU1khhY7B/5Oj2RH9E19bWdtuuxDsE6b0+vkiPjiRr86DFmuxxTZoX6BMFkhVXE6CWlhb59NNPZdCgQaZ+uqEdNmyY2fujmfqOHTti9db3++yzT+yzvtHDZfFl48aN8R9dfa9fOC1NTU2u1qO/M9c4SID6q8j4CDgX6OzszMqGS6drV5qbmqWp/av1lV3/IHSzriDWbY512kQQ4koWg+5BDEusuo3SH+p221rtnqq4eh8gXSj1yq5XX33V1PHdd9+V7du3y5gxY2TSpEmyePFiE9S2bdtkxYoVMnbs2FSx0A8BBBBAAAEEEHAk4OoeIN09pVd5zZ8/3/xt3bpVrr32WnP8e/LkybJ8+XKZNm2a6OGxqVOnSnV1taOgGAgBBBBAAAEEEEgl4GoCpBU76qij5P7775eamhpzjNY6k1t34c2ePVvq6urMuTTZOCkwFQz9EEAAAQQQQCC4Aq4nQBZtVVWV9bbba0VFOK5O6BY0HxBAAAEEEEAgqwKungOU1ciYOAIIIIAAAgggkESABCgJDJ0RQAABBBBAILgCJEDBbVsiQwABBBBAAIEkAiRASWDojAACCCCAAALBFSABCm7bEhkCCCCAAAIIJBEgAUoCQ2cEEEAAAQQQCK4ACVBw25bIEEAAAQQQQCCJAAlQEhg6I4AAAggggEBwBUiAgtu2RIYAAggggAACSQRIgJLA0BkBBBBAAAEEgitAAhTctiUyBBBAAAEEEEgiQAKUBIbOCCCAAAIIIBBcARKg4LYtkSGAAAIIIIBAEgESoCQwdEYAAQQQQACB4AqQAAW3bYkMAQQQQAABBJIIFCbpTmcEEEAAgRwLFBYWSn5+vkQikYzPWadrV8orKqQyWmZ6tbW1SUNDg91gdEMgcAIkQIFrUgJCAAG/Cmji09HRIZqIZLp0dHbYTrKxK+GpbxUpLS2VyspKEiBbJToGUYAEKIitSkwIIOBbgY0bN8p//vOfjNe/YkihlA3oucq/8qqrpLOmQc4++2yZMWNGxufLBBHwqoD9PlGv1pZ6IYAAAggggAACGRAgAcoAIpNAAAEEEEAAAX8JkAD5q72oLQIIIIAAAghkQIAEKAOITAIBBBBAAAEE/CVAAuSv9qK2CCCAAAIIIJABARKgDCAyCQQQQAABBBDwlwAJkL/ai9oigAACCCCAQAYESIAygMgkEEAAAQQQQMBfAj3viuWv+nerbVVVVbfPbn7QW9pr8VKd+uJhxdGXcRkHAQT8J+D3dZaduPUYkPLycnOnbbthgtRN19vWXcWDFJddLBprXl6e7ba2vb3dbpRYt0AlQHV1dbHA3H6jt5XXBdBLdeqLicZRUlLSl1EZBwEEfCjg93WWHbmui4uLi6W+vj4rjxmxm6eb3crKyqSlpUVaW7uecRLwotsoTYLslltt91QlUAmQPkPHK6Wzs9NUxUt16ouNFUdfxmUcBBDwn4Df11l24lZMuj6z3tsNF5RuVpxhiVXbrS+xcg5QUJZ44kAAAQQQQAABxwIkQI6pGBABBBBAAAEEgiJAAhSUliQOBBBAAAEEEHAsEKhzgBxH7bMB9QQv/XOjuDVfN2JlnggggAAC4RFwZ6saHt+MRFpZWSlFRUUZmVZfJtLW1taX0RgHAQQQQAABzwqQAHm2ab6pmJ7R/+KLL8r8+fO/6Zijd1dffbWMGTMmR3NjNggggAACCORGgAQoN879nktzc7PU1tb2ezrpTiAM95FI14ThEUAAAQT8L8BJ0P5vQyJAAAEEEEAAgTQFSIDSBGNwBBBAAAEEEPC/AAmQ/9uQCBBAAAEEEEAgTQESoDTBGBwBBBBAAAEE/C9AAuT/NiQCBBBAAAEEEEhTgAQoTTAGRwABBBBAAAH/C5AA+b8NiQABBBBAAAEE0hTgPkBpgjE4AgggEESBoUOHit50deDAga6FV1dXJ9x53jX+0M2YBCh0TU7ACCCAQE+BYcOGSX5+vqxZs6Znzyx3KSgokGOPPVaamppIgLJszeS/ESAB+saCdwgggECoBTo6OuTmm2/OuUFpaak8+eSTOZ8vMwy3AOcAhbv9iR4BBBBAAIFQCpAAhbLZCRoBBBBAAIFwC5AAhbv9iR4BBBBAAIFQCnAOUCibnaARQAAB7wnoSdh6QnSmi05XS6rpt7e3Z3q2TM/jAiRAHm8gqocAAggEXSASiZhL8CsrK0X/slUGDRqUdNKNjY2ya9eupP3pETwBEqDgtSkRIYAAAr4SKCwslLy8PFm0aJGsXbs253WfOnWqHHDAATmfLzN0V4AEyF1/5o4AAggg8LXAxx9/LKtWrcq5x8knn5zzeTJD9wU4Cdr9NqAGCCCAAAIIIJBjARKgHIMzOwQQQAABBBBwX4AEyP02oAYIIIAAAgggkGMBT58D1NzcbJ5LoyfHjR8/XvRKAbdKWVmZOUnP6fyj0agZtLy83OkoSYezLuFMOgA9EEAAAQQQQCAtAc8mQHpJ4sUXXyyjR4+WL7/8Uh5//HGZM2dOWklIWhIpBtYrFPTSzIaGBnF6r4iWlhbRv0wUEqBMKDINBBBAwF5A17H6Q1vX9W4Unb8+hy1TRaen91Pq7OzsdZIat5Phep1QHwfQuDMZezrVcKe1HdRw4cKFMmHCBJk1a5YZesaMGbJy5UqZOHGig7EzO4guIFquu+46ee+99zI7cQdTe+qppxwMxSAIIIAAAn0ROPDAA6WoqEiGDBnSl9E9OY4etXBS9Ed9Nm4+6WTeOowe6dmxY4fTwTM6XF5X5td7ipjRWTqb2PXXXy9TpkyR448/3oxw//33m0NgulfIKjfddJMsW7bMfNTM/dlnn7V6ZfxVp6+N1NramvFp9zbBPfbYw8y3tra2t0Ez3r+qqkqKi4tl/rqX5KVdH/WY/gFDj5RopKRH90x00MSzpKTE7Elra2vLxCTTmobGrV8P/YLmuujhXv1rampy5deRxq4rRjeWd523Fo0910W/53r42k13/TWcqb3H8X6bd30iO+s3xXcy7//vvpNlUKRUrO/65s2bewyT7Q66x0KTj5qaGlfaffDgwa4mAdn2TTV9txOg/q7bdTuhy4/d0Rldd6dKBD27B2jTpk3d7giqh6A2bNjQrR318JiVueprNjdUulKqqKjoNv9UH7RB9K+/jWvNQzeGqe5iag2XjVeNobjYPsn5P8MOkD2Lq0w72C2A/a2PGrq1e9Ta85f4G8Fa5rIRb7yXm7HHz1vjzXas8XEnc48fJlvvNQHS9s5lvPGxZDP2v7bWyP/aJEADBgyQQUVfnauo33W31jNqXlpaav7iTTLxXl21bTW+xO+zTl+7JeuXifn3No3471tvwzrpr9PTmOxiTRxfh3Xjh45VD12/q31fi24bNQa77X9v32PPJkCJK10F0r0B8eXcc8+N/ygbN27s9tnND5p1asK0bds2N6uRsXk3NjbYTmvnzp1deypaZeDAgaLvw1A0Vi1hijcssQ4fPlx2794t9fX1gVuUG5sabWMyhx8iud/bZluZLHXUjaTu5dFHXbi5sc9SeD0mqxffaEIQhlh1W6vJrd0REj2smap49jJ4XVjjjwvq+xEjRqSKhX4IIIAAAggggIAjAc8mQJMmTZLFixeb48G6F2XFihUyduxYR0ExEAIIIIAAAgggkErAs4fAJk+eLMuXL5dp06aZ43v6sLrq6upUsdAPAQQQQAABBBBwJODZBEiP6c2ePVvq6urMuT/6mYIAAggggAACCGRCwPNZRTpXXmUChGkggAACCCCAQPAFPHsOUPDpiRABBBBAAAEE3BIgAXJLnvkigAACCCCAgGsCJECu0TNjBBBAAAEEEHBLgATILXnmiwACCCCAAAKuCZAAuUbPjBFAAAEEEEDALQHPXwWWDoyXLpXXRwd8+eWXog8yDULZq3iAHNY2vEcoZZEicxtyfeaKl/x7VDSDHb744gszNX0+XRhKmNr2/fffN7fd0GdSBa3srd/h9m++w/p8LP0r+fo7HLR44+PRh8uuW7dO9Lln+liMoBd9Bpg+HysM6+RU21rruY3J2tuzT4NPVmG/dP/jH/8o99xzj7z11lt+qTL1dChw6aWXmiEfeOABh2MwmF8EDj/8cJk1a5b85Cc/8UuVqacDgTfffFPOP/98WbRokRx22GEOxmAQvwjcf//9on+vv/562lXmEFjaZIyAAAIIIIAAAn4XIAHyewtSfwQQQAABBBBIW6Dg/3WVtMdihF4F9PjrXnvtJUcddVSvwzKAvwT0uPK3vvUtOeigg/xVcWrbq4C27fjx42X48G/Olel1JAbwvICe66Tn7B1zzDFSVlbm+fpSQecCuq3de++9Zdy4cc5H+npIzgFKm4wREEAAAQQQQMDvAhwC83sLUn8EEEAAAQQQSFsgUJfBpx19H0dobm6WNWvWmEtIdXd5sssqa2trZdWqVTJ69GjZc889Y3P77LPPzCXyVge9VH7UqFHWR15dFti9e7esXbtWjjvuuKQ12bZtm7z22muy//77y8EHHxwbzumyERuBNzkVeO+996S4uNi0W7IZ66Xwn376qdmlPnjwYDOYLhNvv/12t1EmTpzY7TMf3BVw0rZawyVLlsiJJ57Y7RLxZN9ndyNi7paAXZtZ/axXu/V2b9tazgGy9By+NjY2yvTp06W+vl6WL18uS5culZNPPtkkQ/GTeOaZZ+SWW24RfZr9n//8Z9H7iowcOdIMctddd8krr7win3zyieiXtq2tTY444oj40XnvkkBTU5PccMMN8sEHH8hJJ51kW4s33njDXCqt9xTRyy91g3rIIYeI02XDdqJ0zLrAf//7X9NuBxxwQNIfHHfffbc8/fTToveNuffee00SXFVVZb7rc+fOlS1btpjvrH5vky0fWQ+EGfQQcNK2OpJeBn/77bfLj370o1gClOz73GMmdHBFwK7NEiuSbL3d27aWPUCJkr18XrhwoUyYMMGsSHXQGTNmyMqVKyXx1+Bjjz0men65bhj1/hP6p786ioqK5MMPPzRfwn333beXudE7lwIff/yxXHfddaIbPP1LVnRDePPNN8uRRx4p5513nlxyySVy+umni9NlI9l06Z49gb///e/mh0iqdtUfJMuWLZMnnnjC3EROv8OPPvqo/PKXvzTf2TPPPFMuvPDC7FWSKfdJwEnb6o9M/WGzffv2HvNI9n2ORqM9hqVD7gRStVl8LVKtt3vb1nIOULykg/fr16/vdra5nnmudxiNL3pnSv2laF0lpIe4dE+Q3kG4oaFBduzYIVu3bpVHHnlENmzYED8q710U0Lb51a9+JdOmTUtaC/1SaptZe+yGDRtm9u5p2zpZNpJOmB5ZFSgpKZE//elPZs+PXhFkV3RFqu2qV5Voif9u64q0vLxcFixYIKtXrxa90y7FGwJO2lbvZq4/UnWvXnxJ9X2OH473uRdI1maJNUm23nayrSUBStTs5fOmTZvM5ZTWYHppZeKvioEDB8qBBx4o//rXv8xguotVjzFv3LhRPvroI9HzRHQlqg2sd5197rnnrMnx6qKA3iFW7wScqmhiq5fRxm9Eda+CJrVOlo1U06Zf9gSmTJkSeyxNsuRFv5/xe4jiv9uaAL366qtmD+7DDz8sv/jFL7JXWaacloCTttU977oHL/HREKm+z2lVgoEzLpCszRJnlGy97WRbyyGwRM1ePut9QjRxsYr+gtBfIIlFD43NmzdP5s+fb06APvTQQ83KVe8f89RTT4kmSVr0vKAHH3zQHEJJnAafvSeQ2P5aQ10G9DygxH7Jlg3vRUWNVCBV++l3VM/50r1DZ5xxhpx11llmT6Def4TiX4HENtdIrO+zf6Oi5iqgp5/0tq1lD1Cay4peFaK/9q2i70eMGGF9jL2OHTvWJDb6vCg9xrxr1y5zY0R91UNkVtHzgDZv3iwdHR1WJ149LKCHM/UEeN2LZxVrGXC6bFjj8eotgSFDhvT4buvVm3pCtF4VZh0a03ND9NCn7vGj+Fsg1ffZ35FReyfbWhKgNJeTSZMmyeLFi0XPOtfDWitWrBBNdrToZ/3Tctlll8k777xjDpfp1WK6cdS9PjU1NXLllVeaK4Z0V/w//vEP+c53vhNbuZqR+ec5AT1BVttcd6HrSfB6lZ+Wl19+2bSrtm2qZcNzAVEhI6BX7mlyo0VvaaGXun/++edmL8Czzz5r7hyst7mYM2eOOQSmw7377rvmsPeYMWP0I8WjAvHr42RVTPV9TjYO3d0XcNK2Tra1HAJLsy0nT55sLonVE2X1F+HUqVOlurraTEUvd9eVpZ7Xo4fA7rjjjtihEb0CQYueG3TOOeeYBEl3tep5BrNnzzb9+Oddgcsvv1xuu+02c+XXzJkz5eqrrza7V3UZsNo21bLh3cjCXTP9kXLTTTeZhFa/i/rDRa/qGzRokOy3335ywQUXmPO99Duth7P1Ty9guPbaa3ucTxJuSe9FH78+TlW7ZN/nVOPQz10BJ23rZFvLozD62I51dXXm3J/Ek+oSJ6c3Z9KrRxKLHvLSfrrSpfhTQHex6nkhicXpspE4Hp+9IdDa2moOcdp9b/VXpX5n40+C90atqUV/BZJ9n/s7XcZ3VyDVtpYEyN22Ye4IIIAAAggg4IIA5wC5gM4sEUAAAQQQQMBdARIgd/2ZOwIIIIAAAgi4IEAC5AI6s0QAAQQQQAABdwVIgNz1Z+4IIIAAAggg4IIACZAL6MwSAQQQQAABBNwVIAFy15+5I4BAhgRqa2vTmlK6w6c1cQZGAAHPC5AAeb6JqCACCPQm8Pzzz8spp5zS22Cx/ukOHxuRNwggEBgBEqDANCWBIBBeAX2EhT6jzWlJd3in02U4BBDwjwAJkH/aipoiEGqBN954Q6ZPny7f+9735NJLL5VVq1YZj3//+9+yaNEi83R27W49bFif5XXxxReb4X/84x+bZ/jpCMmGDzUuwSMQQgESoBA2OiEj4DeBzZs3ywknnGCeradJjj6K4rjjjpP33ntPhg4dKnvvvbeUlpaah5fq09rvvfde8xyvAw44QC688EJpaGiQ0047TVavXm07vN88qC8CCPRfgIeh9t+QKSCAQJYF9JCVJjE33nijSWDOP/98OeSQQ6Szs9O8TpgwQdavX2/2DGlVtmzZInfddZd5uKl+1oeaDhkyxDzR/YorrpDE4XUYCgIIhEuABChc7U20CPhSQBMWfbrzqFGjZMqUKXLqqaeKHtbaY489bOPRRGnjxo3y5JNPmr1Ea9eulcbGRmlqarIdno4IIBA+AQ6Bha/NiRgB3wnok9n1nJ9bb73V7AmaOXOm6OGtpUuX2sYyd+5cqa6uNsPr3qDvf//7Zs+R7cB0RACBUAqQAIWy2QkaAX8JfPDBB6InNWvi89xzz5lDXHoIbN68eSYQPSfIKrqn55prrpE77rjDnPOjydA555xjxuno6OgxvDUerwggEC4BEqBwtTfRIuBbgYsuukieeOIJaW9vF72JoV7tpYfFtAwaNMgc8tJESYseGtu0aZNowqPnDv30pz+V5ubm2CGw+OHb2trMOPxDAIFwCZAAhau9iRYBXwocdNBBcuedd8r1118vVVVV5vDW4Ycfbj5rQMcff7wUFBTIwQcfLHq+z29+8xt57LHHzGGv4cOHix5Cmzp1quil9InDr1mzxnTjHwIIhEsgr+sqis5whUy0CCDgZwHdszNgwABzSXxiHLt27TL9rO4bNmwQTYAKC+2v90gc3hqPVwQQCL4ACVDw25gIEUAAAQQQQCBBgENgCSB8RAABBBBAAIHgC5AABb+NiRABBBBAAAEEEgRIgBJA+IgAAggggAACwRcgAQp+GxMhAggggAACCCQIkAAlgPARAQQQQAABBIIvQAIU/DYmQgQQQAABBBBIECABSgDhIwIIIIAAAggEX4AEKPhtTIQIIIAAAgggkCDw/wGT7B1MWS1YbwAAAABJRU5ErkJggg==" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> slope_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1268,7 +1316,7 @@ <h3>Two numerical vars - SLR</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>) </code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---correlation-1" class="section level3">
 <h3>Two numerical vars - correlation</h3>
@@ -1295,6 +1343,9 @@ <h3>Two numerical vars - correlation</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1312,9 +1363,12 @@ <h3>Two numerical vars - correlation</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, 
                             <span class="dt">point_estimate =</span> correlation_hat) )</code></pre></div>
+<pre><code>## Warning: 'get_ci' is deprecated.
+## Use 'get_confidence_interval' instead.
+## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1332,7 +1386,7 @@ <h3>Two numerical vars - correlation</h3>
 </table>
 </div>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)  </code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---t" class="section level3">
 <h3>Two numerical vars - t</h3>
diff --git a/inst/doc/two_sample_t.R b/inst/doc/two_sample_t.R
index 1c46e550..80ce97a0 100644
--- a/inst/doc/two_sample_t.R
+++ b/inst/doc/two_sample_t.R
@@ -47,7 +47,7 @@ t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 t_null_distn %>% 
-  get_pvalue(obs_stat = obs_t, direction = "two_sided")
+  get_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 fli_small %>%
diff --git a/inst/doc/two_sample_t.Rmd b/inst/doc/two_sample_t.Rmd
index 9a2295f4..9fbe54ab 100755
--- a/inst/doc/two_sample_t.Rmd
+++ b/inst/doc/two_sample_t.Rmd
@@ -100,7 +100,7 @@ t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
 
 ```{r}
 t_null_distn %>% 
-  get_pvalue(obs_stat = obs_t, direction = "two_sided")
+  get_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
index 605cdffa..7a8ecc8c 100644
--- a/inst/doc/two_sample_t.html
+++ b/inst/doc/two_sample_t.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-08-06" />
+<meta name="date" content="2018-09-03" />
 
 <title>Two sample t test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Two sample <span class="math inline">\(t\)</span> test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-08-06</em></h4>
+<h4 class="date"><em>2018-09-03</em></h4>
 
 
 
@@ -168,12 +168,12 @@ <h2>Randomization approach to t-statistic</h2>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">get_pvalue</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
 <thead>
@@ -214,7 +214,7 @@ <h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on t
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>, <span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. `infer` currently does not check these for you.</code></pre>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="compute-theoretical-p-value" class="section level2">
 <h2>Compute theoretical p-value</h2>
diff --git a/man/deprecated.Rd b/man/deprecated.Rd
new file mode 100644
index 00000000..d4692abc
--- /dev/null
+++ b/man/deprecated.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/deprecated.R
+\name{deprecated}
+\alias{deprecated}
+\alias{conf_int}
+\alias{get_ci}
+\alias{p_value}
+\alias{get_pvalue}
+\title{Deprecated functions}
+\usage{
+conf_int(x, level = 0.95, type = "percentile", point_estimate = NULL)
+
+get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)
+
+p_value(x, obs_stat, direction)
+
+get_pvalue(x, obs_stat, direction)
+}
+\arguments{
+\item{x}{See the non-deprecated function.}
+
+\item{level}{See the non-deprecated function.}
+
+\item{type}{See the non-deprecated function.}
+
+\item{point_estimate}{See the non-deprecated function.}
+
+\item{obs_stat}{See the non-deprecated function.}
+
+\item{direction}{See the non-deprecated function.}
+}
+\description{
+These functions should no longer be used. They will be removed in a
+future release of \code{infer}.
+}
+\seealso{
+\code{\link{get_p_value}}, \code{\link{get_confidence_interval}}
+}
diff --git a/man/get_ci.Rd b/man/get_confidence_interval.Rd
similarity index 71%
rename from man/get_ci.Rd
rename to man/get_confidence_interval.Rd
index 82df2640..7ec6bae3 100644
--- a/man/get_ci.Rd
+++ b/man/get_confidence_interval.Rd
@@ -1,15 +1,9 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/conf_int.R
-\name{get_ci}
-\alias{get_ci}
-\alias{conf_int}
+\name{get_confidence_interval}
 \alias{get_confidence_interval}
 \title{Compute confidence interval}
 \usage{
-conf_int(x, level = 0.95, type = "percentile", point_estimate = NULL)
-
-get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)
-
 get_confidence_interval(x, level = 0.95, type = "percentile",
   point_estimate = NULL)
 }
@@ -34,19 +28,33 @@ the confidence interval.
 }
 \description{
 Only simulation-based methods are (currently only) supported.
-\code{get_confidence_interval()} and \code{get_ci()} are both aliases of \code{conf_int()}.
 }
+\section{Aliases}{
+
+\code{conf_int()} and \code{get_ci()} are deprecated aliases of \code{get_confidence_interval()}.
+}
+
 \examples{
+# Prepare the dataset
 mtcars_df <- mtcars \%>\%
   dplyr::mutate(am = factor(am))
+
+# Calculate the difference in means in the dataset
 d_hat <- mtcars_df \%>\%
   specify(mpg ~ am) \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
+
+# Same calculation on 100 bootstrap replicates
 bootstrap_distn <- mtcars_df \%>\%
   specify(mpg ~ am) \%>\%
-  generate(reps = 100) \%>\%
+  generate(reps = 100, type = "bootstrap") \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
-bootstrap_distn \%>\% conf_int(level = 0.9)
-bootstrap_distn \%>\% conf_int(type = "se", point_estimate = d_hat)
 
+# Use level to set the confidence level
+bootstrap_distn \%>\%
+  get_confidence_interval(level = 0.9)
+
+# To calculate std error, set the type and point estimate
+bootstrap_distn \%>\%
+  get_confidence_interval(type = "se", point_estimate = d_hat)
 }
diff --git a/man/get_pvalue.Rd b/man/get_p_value.Rd
similarity index 65%
rename from man/get_pvalue.Rd
rename to man/get_p_value.Rd
index da59b651..9df0e25c 100644
--- a/man/get_pvalue.Rd
+++ b/man/get_p_value.Rd
@@ -1,13 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/p_value.R
-\name{get_pvalue}
-\alias{get_pvalue}
-\alias{p_value}
+\name{get_p_value}
+\alias{get_p_value}
 \title{Compute p-value}
 \usage{
-p_value(x, obs_stat, direction)
-
-get_pvalue(x, obs_stat, direction)
+get_p_value(x, obs_stat, direction)
 }
 \arguments{
 \item{x}{Data frame of calculated statistics or containing attributes of
@@ -23,21 +20,32 @@ extreme than this).}
 A 1x1 data frame with value between 0 and 1.
 }
 \description{
-Only simulation-based methods are (currently only) supported. \code{get_pvalue()}
-is an alias of \code{p_value}.
+Only simulation-based methods are (currently only) supported.
+}
+\section{Aliases}{
+
+\code{p_value} and \code{get_pvalue()} are deprecated aliases of \code{get_p_value()}.
 }
+
 \examples{
+# Prepare the dataset
 mtcars_df <- mtcars \%>\%
   dplyr::mutate(am = factor(am))
+
+# Calculate the difference in means in the dataset
 d_hat <- mtcars_df \%>\%
   specify(mpg ~ am) \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
+
+# Same calculation on 100 permutation replicates
 null_distn <- mtcars_df \%>\%
   specify(mpg ~ am) \%>\%
   hypothesize(null = "independence") \%>\%
   generate(reps = 100) \%>\%
   calculate(stat = "diff in means", order = c("1", "0"))
-null_distn \%>\%
-  p_value(obs_stat = d_hat, direction = "right")
 
+# What proportion of replicates had a difference
+# in means more extreme than in the dataset?
+null_distn \%>\%
+  get_p_value(obs_stat = d_hat, direction = "right")
 }
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
index 5cfe0060..4a8e9727 100644
--- a/tests/testthat/test-conf_int.R
+++ b/tests/testthat/test-conf_int.R
@@ -8,13 +8,13 @@ set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("basics work", {
-  expect_silent(test_df %>% conf_int())
-  expect_error(test_df  %>% conf_int(type = "other"))
-  expect_error(test_df  %>% conf_int(level = 1.2))
-  expect_error(test_df  %>% conf_int(point_estimate = "help"))
-  
-  expect_silent(iris_calc %>% get_ci(type = "se", point_estimate = 4))
-  expect_silent(iris_calc %>% get_ci(type = "se", point_estimate = obs_diff))
-  expect_error(iris_calc  %>% get_ci(type = "se", point_estimate = "error"))
-  expect_error(iris_calc  %>% get_ci(type = "se"))
+  expect_silent(test_df %>% get_confidence_interval())
+  expect_error(test_df  %>% get_confidence_interval(type = "other"))
+  expect_error(test_df  %>% get_confidence_interval(level = 1.2))
+  expect_error(test_df  %>% get_confidence_interval(point_estimate = "help"))
+
+  expect_silent(iris_calc %>% get_confidence_interval(type = "se", point_estimate = 4))
+  expect_silent(iris_calc %>% get_confidence_interval(type = "se", point_estimate = obs_diff))
+  expect_error(iris_calc  %>% get_confidence_interval(type = "se", point_estimate = "error"))
+  expect_error(iris_calc  %>% get_confidence_interval(type = "se"))
 })
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-p_value.R
index c3b758b3..1c5c063f 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-p_value.R
@@ -1,46 +1,46 @@
-context("p_value")
+context("get_p_value")
 
 set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
 
 test_that("direction is appropriate", {
-  expect_error(test_df %>% p_value(obs_stat = 0.5, direction = "righ"))
+  expect_error(test_df %>% get_p_value(obs_stat = 0.5, direction = "righ"))
 })
 
-test_that("p_value makes sense", {
+test_that("get_p_value makes sense", {
   expect_lt(
     iris_calc %>%
-      p_value(obs_stat = 0.1, direction = "right") %>%
+      get_p_value(obs_stat = 0.1, direction = "right") %>%
       dplyr::pull(),
     expected = 0.1
   )
   expect_gt(
     iris_calc %>%
-      p_value(obs_stat = -0.1, direction = "greater") %>%
+      get_p_value(obs_stat = -0.1, direction = "greater") %>%
       dplyr::pull(),
     expected = 0.9
   )
   expect_equal(
     iris_calc %>%
-      p_value(obs_stat = median(iris_calc$stat), direction = "both") %>%
+      get_p_value(obs_stat = median(iris_calc$stat), direction = "both") %>%
       dplyr::pull(),
     expected = 1
   )
   expect_lt(
     iris_calc %>%
-      p_value(obs_stat = -0.2, direction = "left") %>%
+      get_p_value(obs_stat = -0.2, direction = "left") %>%
       dplyr::pull(),
     expected = 0.02
   )
   expect_gt(
     iris_calc %>%
-      p_value(obs_stat = -0.2, direction = "right") %>%
+      get_p_value(obs_stat = -0.2, direction = "right") %>%
       dplyr::pull(),
     expected = 0.98
   )
   expect_equal(
     iris_calc %>%
-      get_pvalue(
+      get_p_value(
         obs_stat = median(iris_calc$stat) + 1, direction = "two_sided"
       ) %>%
       dplyr::pull(),
diff --git a/vignettes/chisq_test.Rmd b/vignettes/chisq_test.Rmd
index f2dd959a..d56b5201 100644
--- a/vignettes/chisq_test.Rmd
+++ b/vignettes/chisq_test.Rmd
@@ -99,7 +99,7 @@ chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
 
 ```{r}
 chisq_null_distn %>% 
-  get_pvalue(obs_stat = obs_chisq, direction = "greater")
+  get_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 
diff --git a/vignettes/observed_stat_examples.Rmd b/vignettes/observed_stat_examples.Rmd
index cec5e8f1..3a2f3dc7 100644
--- a/vignettes/observed_stat_examples.Rmd
+++ b/vignettes/observed_stat_examples.Rmd
@@ -72,7 +72,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_bar, direction = "two_sided")
+  get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
 
 ### One numerical variable (standardized mean $t$)
@@ -93,7 +93,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_bar, direction = "two_sided")
+  get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
 
 
@@ -116,7 +116,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = x_tilde, direction = "two_sided")
+  get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
 
 ### One categorical (one proportion)
@@ -138,7 +138,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = p_hat, direction = "two_sided")
+  get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
 
 Logical variables will be coerced to factors:
@@ -176,7 +176,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### Two categorical (2 level) variables (z)
@@ -198,7 +198,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = z_hat, direction = "two_sided")
+  get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
 
 Note the similarities in this plot and the previous one.
@@ -227,7 +227,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
 
 ### Two categorical (>2 level) variables
@@ -249,7 +249,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = Chisq_hat, direction = "greater")
+  get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
 
 ### One numerical variable, one categorical (2 levels) (diff in means)
@@ -271,7 +271,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -293,7 +293,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_hat, direction = "two_sided")
+  get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
 
 Note the similarities in this plot and the previous one.
@@ -318,7 +318,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = d_hat, direction = "two_sided")
+  get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
 
 ### One numerical, one categorical (>2 levels) -  ANOVA
@@ -340,7 +340,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = F_hat, direction = "greater")
 null_distn %>%
-  get_pvalue(obs_stat = F_hat, direction = "greater")
+  get_p_value(obs_stat = F_hat, direction = "greater")
 ```
 
 ### Two numerical vars - SLR 
@@ -362,7 +362,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = slope_hat, direction = "two_sided")
+  get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
 
 ### Two numerical vars - correlation
@@ -384,7 +384,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = correlation_hat, direction = "two_sided")
+  get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
 
 
@@ -409,7 +409,7 @@ null_distn <- fli_small %>%
 null_distn %>% 
   visualize(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
-  get_pvalue(obs_stat = t_hat, direction = "two_sided")
+  get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
 
 
diff --git a/vignettes/two_sample_t.Rmd b/vignettes/two_sample_t.Rmd
index 9a2295f4..9fbe54ab 100755
--- a/vignettes/two_sample_t.Rmd
+++ b/vignettes/two_sample_t.Rmd
@@ -100,7 +100,7 @@ t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
 
 ```{r}
 t_null_distn %>% 
-  get_pvalue(obs_stat = obs_t, direction = "two_sided")
+  get_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 

From 39ee0ddc74d4395aee2819b16404224c444bb4a8 Mon Sep 17 00:00:00 2001
From: Richard Cotton <richierocks@gmail.com>
Date: Mon, 3 Sep 2018 11:53:41 -0400
Subject: [PATCH 36/78] don't deprecate get_pvalue(), get_ci()

---
 NEWS.md                        |  4 ++--
 R/conf_int.R                   | 12 +++++++++++-
 R/deprecated.R                 | 16 ----------------
 R/p_value.R                    |  9 ++++++++-
 man/deprecated.Rd              |  6 ------
 man/get_confidence_interval.Rd |  6 +++++-
 man/get_p_value.Rd             |  6 +++++-
 7 files changed, 31 insertions(+), 28 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 1539f44f..d3915e1f 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -9,8 +9,8 @@
 - Make transparancy in `visualize()` to not depend on method and data volume.
 - Make `visualize()` work for "One sample t" theoretical type with `method = "both"`.
 - Add `stat = "sum"` and `stat = "count"` options to `calculate()` (#50).
-- Deprecated `p_value()` and `get_pvalue()` (use `get_p_value()` instead) (#180).
-- Deprecated `cont_int()` and `get_ci()` (use `get_confidence_interval()` instead) (#180).
+- Deprecated `p_value()` (use `get_p_value()` instead) (#180).
+- Deprecated `cont_int()` (use `get_confidence_interval()` instead) (#180).
 
 # infer 0.3.1
 
diff --git a/R/conf_int.R b/R/conf_int.R
index 1889a88d..397891f5 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -16,7 +16,8 @@
 #' @return A 1 x 2 tibble with values corresponding to lower and upper values in
 #'   the confidence interval.
 #' @section Aliases:
-#' `conf_int()` and `get_ci()` are deprecated aliases of `get_confidence_interval()`.
+#' `get_ci()` is an alias of `get_confidence_interval()`.
+#' `conf_int` is a deprecated alias of `get_confidence_interval()`.
 #'
 #' @examples
 #' # Prepare the dataset
@@ -65,6 +66,15 @@ get_confidence_interval <- function(x, level = 0.95, type = "percentile",
   return(ci)
 }
 
+#' @rdname get_confidence_interval
+#' @export
+get_ci <- function(x, level = 0.95, type = "percentile",
+  point_estimate = NULL) {
+  get_confidence_interval(
+    x, level = level, type = type, point_estimate = point_estimate
+  )
+}
+
 check_ci_args <- function(x, level, type, point_estimate){
 
   if(!is.null(point_estimate)){
diff --git a/R/deprecated.R b/R/deprecated.R
index 8c4e7b67..6d64d129 100644
--- a/R/deprecated.R
+++ b/R/deprecated.R
@@ -23,16 +23,6 @@ conf_int <- function(x, level = 0.95, type = "percentile",
   )
 }
 
-#' @rdname deprecated
-#' @export
-get_ci <- function(x, level = 0.95, type = "percentile",
-  point_estimate = NULL) {
-  .Deprecated("get_confidence_interval")
-  get_confidence_interval(
-    x, level = level, type = type, point_estimate = point_estimate
-  )
-}
-
 
 #' @rdname deprecated
 #' @export
@@ -41,9 +31,3 @@ p_value <- function(x, obs_stat, direction) {
   get_p_value(x = x, obs_stat = obs_stat, direction = direction)
 }
 
-#' @rdname deprecated
-#' @export
-get_pvalue <- function(x, obs_stat, direction) {
-  .Deprecated("get_p_value")
-  get_p_value(x = x, obs_stat = obs_stat, direction = direction)
-}
diff --git a/R/p_value.R b/R/p_value.R
index 6be7147c..8147cb56 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -12,7 +12,8 @@
 #' @return A 1x1 data frame with value between 0 and 1.
 #'
 #' @section Aliases:
-#' `p_value` and `get_pvalue()` are deprecated aliases of `get_p_value()`.
+#' `get_pvalue()` is an alias of `get_p_value()`.
+#' `p_value` is a deprecated alias of `get_p_value()`.
 #'
 #' @examples
 #' # Prepare the dataset
@@ -70,6 +71,12 @@ get_p_value <- function(x, obs_stat, direction){
   return(pvalue)
 }
 
+#' @rdname get_p_value
+#' @export
+get_pvalue <- function(x, obs_stat, direction) {
+  get_p_value(x = x, obs_stat = obs_stat, direction = direction)
+}
+
 simulation_based_p_value <- function(x, obs_stat, direction){
 
   if(direction %in% c("less", "left")){
diff --git a/man/deprecated.Rd b/man/deprecated.Rd
index d4692abc..d733d8d2 100644
--- a/man/deprecated.Rd
+++ b/man/deprecated.Rd
@@ -3,18 +3,12 @@
 \name{deprecated}
 \alias{deprecated}
 \alias{conf_int}
-\alias{get_ci}
 \alias{p_value}
-\alias{get_pvalue}
 \title{Deprecated functions}
 \usage{
 conf_int(x, level = 0.95, type = "percentile", point_estimate = NULL)
 
-get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)
-
 p_value(x, obs_stat, direction)
-
-get_pvalue(x, obs_stat, direction)
 }
 \arguments{
 \item{x}{See the non-deprecated function.}
diff --git a/man/get_confidence_interval.Rd b/man/get_confidence_interval.Rd
index 7ec6bae3..db5096fe 100644
--- a/man/get_confidence_interval.Rd
+++ b/man/get_confidence_interval.Rd
@@ -2,10 +2,13 @@
 % Please edit documentation in R/conf_int.R
 \name{get_confidence_interval}
 \alias{get_confidence_interval}
+\alias{get_ci}
 \title{Compute confidence interval}
 \usage{
 get_confidence_interval(x, level = 0.95, type = "percentile",
   point_estimate = NULL)
+
+get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)
 }
 \arguments{
 \item{x}{Data frame of calculated statistics or containing attributes of
@@ -31,7 +34,8 @@ Only simulation-based methods are (currently only) supported.
 }
 \section{Aliases}{
 
-\code{conf_int()} and \code{get_ci()} are deprecated aliases of \code{get_confidence_interval()}.
+\code{get_ci()} is an alias of \code{get_confidence_interval()}.
+\code{conf_int} is a deprecated alias of \code{get_confidence_interval()}.
 }
 
 \examples{
diff --git a/man/get_p_value.Rd b/man/get_p_value.Rd
index 9df0e25c..5b935608 100644
--- a/man/get_p_value.Rd
+++ b/man/get_p_value.Rd
@@ -2,9 +2,12 @@
 % Please edit documentation in R/p_value.R
 \name{get_p_value}
 \alias{get_p_value}
+\alias{get_pvalue}
 \title{Compute p-value}
 \usage{
 get_p_value(x, obs_stat, direction)
+
+get_pvalue(x, obs_stat, direction)
 }
 \arguments{
 \item{x}{Data frame of calculated statistics or containing attributes of
@@ -24,7 +27,8 @@ Only simulation-based methods are (currently only) supported.
 }
 \section{Aliases}{
 
-\code{p_value} and \code{get_pvalue()} are deprecated aliases of \code{get_p_value()}.
+\code{get_pvalue()} is an alias of \code{get_p_value()}.
+\code{p_value} is a deprecated alias of \code{get_p_value()}.
 }
 
 \examples{

From d6f987cb78f24cb607c3742dff62f898fb82150e Mon Sep 17 00:00:00 2001
From: Richard Cotton <richierocks@gmail.com>
Date: Mon, 3 Sep 2018 12:10:57 -0400
Subject: [PATCH 37/78] refactor generate()

---
 NAMESPACE                       |   1 +
 R/generate.R                    | 112 +++++++++++++++++---------------
 man/generate.Rd                 |   8 ++-
 tests/testthat/test-calculate.R |  16 ++---
 tests/testthat/test-generate.R  | 110 ++++++++++++++++---------------
 5 files changed, 133 insertions(+), 114 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index a6bf6e27..cf359b30 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,6 +2,7 @@
 
 S3method(print,infer)
 export("%>%")
+export(GENERATION_TYPES)
 export(calculate)
 export(chisq_stat)
 export(chisq_test)
diff --git a/R/generate.R b/R/generate.R
index 61f3856a..fda851df 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -21,66 +21,74 @@
 #'
 #' @importFrom dplyr group_by
 #' @export
-generate <- function(x, reps = 1, type = attr(x, "type"), ...) {
-  auto_type <- attr(x, "type")
+generate <- function(x, reps = 1, type = NULL, ...) {
+  type <- sanitize_generation_type(type)
+  auto_type <- sanitize_generation_type(attr(x, "type"))
+  type <- if(!is.null(type)) { # User specifies type
+    compare_type_vs_auto_type(type, auto_type)
+  } else { # Use default
+    use_auto_type(auto_type)
+  }
 
-  if (!is.null(auto_type)) {
-    if (is.null(type)) {
-      stop_glue("Supply not `NULL` value of `type`.")
-    }
+  attr(x, "generate") <- TRUE
 
-    if (auto_type != type) {
-      stop_glue(
-        "You have specified `type = \"{type}\"`, but `type` is expected to be ",
-        "`\"{auto_type}\"`. Please try again with appropriate `type` value."
-      )
-    } else {
-      type <- auto_type
-    }
+  switch(
+    type,
+    bootstrap = bootstrap(x, reps, ...),
+    permute = {
+      check_permutation_attributes(x)
+      permute(x, reps, ...)
+    },
+    simulate = simulate(x, reps, ...)
+  )
+}
+
+#' @rdname generate
+#' @export
+GENERATION_TYPES <- c("bootstrap", "permute",  "simulate")
+
+sanitize_generation_type <- function(x) {
+  if(is.null(x)) return(x)
+  match.arg(x, GENERATION_TYPES)
+}
+
+compare_type_vs_auto_type <- function(type, auto_type) {
+  if(is.null(auto_type)) {
+    # No default; use whatever they specified.
+    return(type)
+  }
+  if (auto_type != type) {
+    # User is overriding the default, so warn of potential stupidity.
+    warning_glue(
+      "You have specified `type = \"{type}\"`, but `type` is expected",
+      "to be`\"{auto_type}\"`. This workflow is untested and",
+      "the results may not mean what you think they mean.",
+      .sep = " "
+    )
   }
+  type
+}
 
-  attr(x, "generate") <- TRUE
+use_auto_type <- function(auto_type) {
+  if(is.null(auto_type)) {
+    stop_glue(
+      "There is no default `type`;",
+      "please set it to one of {toString(shQuote(GENERATION_TYPES))}.",
+      .sep = " "
+    )
+  }
+  message_glue('Setting `type = "{auto_type}"`.')
+  auto_type
+}
 
-  if (
-    (type == "permute") &&
-    any(is_nuat(x, "response"), is_nuat(x, "explanatory"))
-  ) {
+check_permutation_attributes <- function(x, attr) {
+  if (any(is_nuat(x, "response"), is_nuat(x, "explanatory"))) {
     stop_glue(
-      "Please `specify()` an explanatory and a response variable when ",
-      "permuting."
+      "Please `specify()` an explanatory and a response variable",
+      "when permuting.",
+      .sep = " "
     )
   }
-## Can't get to these anymore with tests
-#   if (
-#     (type == "simulate") &&
-#     (attr(x, "null") != "point") &&
-#     !(length(grep("p.", names(attr(x, "params")))) >= 1)
-#   ) {
-#     stop_glue("Simulation requires a `point` null hypothesis on proportions.")
-#   }
-#   if (
-#     (type == "bootstrap") &&
-#     !(attr(attr(x, "params"), "names") %in% c("mu", "med", "sigma")) &&
-#     !is_nuat(x, "null")
-#   ) {
-#     stop_glue(
-#       "Bootstrapping is inappropriate in this setting. ",
-#       "Consider using `type = permute` or `type = simulate`."
-#     )
-#  }
-
-  if (type == "bootstrap") {
-    bootstrap(x, reps, ...)
-  } else if (type == "permute") {
-    permute(x, reps, ...)
-  } else if (type == "simulate") {
-    simulate(x, reps, ...)
-  } # else if (!(type %in% c("bootstrap", "permute", "simulate"))) {
-    # stop_glue(
-    #   "Choose one of the available options for `type`: ",
-    #   '`"bootstrap"`, `"permute"`, or `"simulate"`'
-    # )
-  # }
 }
 
 bootstrap <- function(x, reps = 1, ...) {
diff --git a/man/generate.Rd b/man/generate.Rd
index f8fae0f1..6df55386 100755
--- a/man/generate.Rd
+++ b/man/generate.Rd
@@ -1,10 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/generate.R
+\docType{data}
 \name{generate}
 \alias{generate}
+\alias{GENERATION_TYPES}
 \title{Generate resamples, permutations, or simulations}
+\format{An object of class \code{character} of length 3.}
 \usage{
-generate(x, reps = 1, type = attr(x, "type"), ...)
+generate(x, reps = 1, type = NULL, ...)
+
+GENERATION_TYPES
 }
 \arguments{
 \item{x}{A data frame that can be coerced into a \link[tibble:tibble]{tibble}.}
@@ -32,3 +37,4 @@ mtcars \%>\%
   generate(reps = 100, type = "permute")
 
 }
+\keyword{datasets}
diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index b03a2453..dd2bacea 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -77,7 +77,7 @@ test_that("grouping (explanatory) variable is a factor (two var problems)", {
   expect_error(calculate(gen_iris2, stat = "diff in medians"))
   # Since shifts to "Slope with t"
   ## Not implemented
- # expect_silent(calculate(gen_iris2, stat = "t"))
+  # expect_silent(calculate(gen_iris2, stat = "t"))
 })
 
 test_that("grouping (explanatory) variable is numeric (two var problems)", {
@@ -356,7 +356,7 @@ test_that("specify() %>% calculate() works", {
 })
 
 test_that("One sample t hypothesis test is working", {
-  expect_silent(
+  expect_message(
     iris_tbl %>%
       specify(Petal.Width ~ NULL) %>%
       hypothesize(null = "point", mu = 1) %>%
@@ -398,7 +398,7 @@ test_that("generate not done before calculate", {
 })
 
 test_that("One sample t bootstrap is working", {
-  expect_silent(
+  expect_message(
     iris_tbl %>%
       specify(Petal.Width ~ NULL) %>%
       generate(reps = 10) %>%
@@ -409,14 +409,14 @@ test_that("One sample t bootstrap is working", {
 test_that("calculate doesn't depend on order of `p` (#122)", {
   calc_chisq <- function(p) {
     set.seed(111)
-    
+
     iris %>%
       specify(Species ~ NULL) %>%
       hypothesize(null = "point", p = p) %>%
       generate(reps = 10, type = "simulate") %>%
       calculate("Chisq")
   }
-  
+
   expect_equal(
     calc_chisq(c("versicolor" = 0.25, "setosa" = 0.5, "virginica" = 0.25)),
     calc_chisq(c("virginica" = 0.25, "versicolor" = 0.25, "setosa" = 0.5))
@@ -439,11 +439,11 @@ test_that("calc_impl.sum works", {
       `[[`(1),
     sum(iris_tbl$Petal.Width)
   )
-  
+
   gen_iris16 <- iris_tbl %>%
     specify(Petal.Width ~ NULL) %>%
     generate(10)
-  
+
   expect_equal(
     gen_iris16 %>% calculate(stat = "sum"),
     gen_iris16 %>% dplyr::summarise(stat = sum(Petal.Width))
@@ -467,7 +467,7 @@ test_that("calc_impl.count works", {
       `[[`(1),
     sum(iris_tbl$Sepal.Length.Group == ">5")
   )
-  
+
   expect_equal(
     gen_iris12 %>% calculate(stat = "count"),
     gen_iris12 %>% dplyr::summarise(stat = sum(Sepal.Length.Group == ">5"))
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index 70088b90..76cc80ff 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -37,9 +37,9 @@ hyp_anova <- mtcars_df %>%
   hypothesize(null = "independence")
 
 test_that("cohesion with type argument", {
-  expect_error(generate(hyp_prop, type = "bootstrap"))
+  expect_warning(generate(hyp_prop, type = "bootstrap"))
   expect_error(generate(hyp_diff_in_props, type = "bootstrap"))
-  expect_error(generate(hyp_chisq_gof, type = "bootstrap"))
+  expect_warning(generate(hyp_chisq_gof, type = "bootstrap"))
   expect_error(generate(hyp_chisq_ind, type = "bootstrap"))
   expect_silent(generate(hyp_mean, type = "bootstrap"))
   expect_silent(generate(hyp_median, type = "bootstrap"))
@@ -48,12 +48,12 @@ test_that("cohesion with type argument", {
   expect_error(generate(hyp_anova, type = "bootstrap"))
 
   expect_silent(generate(hyp_prop, type = "simulate"))
-  expect_error(generate(hyp_diff_in_props, type = "simulate"))
+  expect_warning(generate(hyp_diff_in_props, type = "simulate"))
   expect_silent(generate(hyp_chisq_gof, type = "simulate"))
-  expect_error(generate(hyp_chisq_ind, type = "simulate"))
+  expect_warning(generate(hyp_chisq_ind, type = "simulate"))
   expect_error(generate(hyp_mean, type = "simulate"))
-  expect_error(generate(hyp_diff_in_means, type = "simulate"))
-  expect_error(generate(hyp_anova, type = "simulate"))
+  expect_warning(generate(hyp_diff_in_means, type = "simulate"))
+  expect_warning(generate(hyp_anova, type = "simulate"))
 
   expect_error(generate(hyp_prop, type = "permute"))
   expect_silent(generate(hyp_diff_in_props, type = "permute"))
@@ -155,81 +155,81 @@ test_that("auto `type` works (generate)", {
   expect_equal(attr(slope_boot, "type"), "bootstrap")
 
   expect_error(mtcars_df %>%
-    specify(response = mpg) %>% # formula alt: mpg ~ NULL
-    hypothesize(null = "point", mu = 25) %>%
-    generate(reps = 100, type = "permute")
+      specify(response = mpg) %>% # formula alt: mpg ~ NULL
+      hypothesize(null = "point", mu = 25) %>%
+      generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars_df %>%
-    specify(response = mpg) %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(response = mpg) %>%
+      generate(reps = 100, type = "simulate")
   )
 
   expect_error(mtcars_df %>%
-    specify(response = mpg) %>% # formula alt: mpg ~ NULL
-    hypothesize(null = "point", med = 26) %>%
-    generate(reps = 100, type = "permute")
+      specify(response = mpg) %>% # formula alt: mpg ~ NULL
+      hypothesize(null = "point", med = 26) %>%
+      generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars_df %>%
-    specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-    hypothesize(null = "point", p = .25) %>%
-    generate(reps = 100, type = "bootstrap")
+  expect_warning(mtcars_df %>%
+      specify(response = am, success = "1") %>% # formula alt: am ~ NULL
+      hypothesize(null = "point", p = .25) %>%
+      generate(reps = 100, type = "bootstrap")
   )
 
   expect_error(mtcars_df %>%
-    specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
-    hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "bootstrap")
+      specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars_df %>%
-    specify(cyl ~ NULL) %>% # alt: response = cyl
-    hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-    generate(reps = 100, type = "bootstrap")
+  expect_warning(mtcars_df %>%
+      specify(cyl ~ NULL) %>% # alt: response = cyl
+      hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
+      generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars_df %>%
-    specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
-    hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "simulate")
   )
 
   expect_error(mtcars_df %>%
-    specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-    hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "bootstrap"))
+      specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "bootstrap"))
 
-  expect_error(mtcars_df %>%
-    specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
-    hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "simulate")
   )
 
   expect_error(mtcars_df %>%
-    specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
-    hypothesize(null = "independence") %>%
-    generate(reps = 100, type = "bootstrap")
+      specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
+      hypothesize(null = "independence") %>%
+      generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars_df %>%
-    specify(response = am, success = "1") %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(response = am, success = "1") %>%
+      generate(reps = 100, type = "simulate")
   )
 
   expect_error(mtcars_df %>%
-    specify(mpg ~ am) %>%
-    generate(reps = 100, type = "permute")
+      specify(mpg ~ am) %>%
+      generate(reps = 100, type = "permute")
   )
 
-  expect_error(mtcars_df %>%
-    specify(am ~ vs, success = "1") %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(am ~ vs, success = "1") %>%
+      generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars_df %>%
-    specify(mpg ~ hp) %>%
-    generate(reps = 100, type = "simulate")
+  expect_warning(mtcars_df %>%
+      specify(mpg ~ hp) %>%
+      generate(reps = 100, type = "simulate")
   )
 })
 
@@ -241,7 +241,7 @@ test_that("mismatches lead to error", {
       hypothesize(null = "independence", p = c("1" = 0.5)) %>%
       generate(reps = 100, type = "simulate")
   )
-  expect_error(
+  expect_warning(
     mtcars_df %>%
       specify(cyl ~ NULL) %>% # alt: response = cyl
       hypothesize(
@@ -254,5 +254,9 @@ test_that("mismatches lead to error", {
 })
 
 test_that("generate() handles `NULL` value of `type`", {
-  expect_error(generate(hyp_prop, type = NULL), "NULL.*type")
+  expect_message(
+    generate(hyp_prop, type = NULL),
+    'Setting `type = "simulate"`.',
+    fixed = TRUE
+  )
 })

From 2f437b34c986049c78de50e83e2e211d0da25313 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 09:48:24 -0700
Subject: [PATCH 38/78] Update p_value.R

---
 R/p_value.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/p_value.R b/R/p_value.R
index 8147cb56..5758f8a4 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -1,6 +1,6 @@
 #' Compute p-value
 #'
-#' Only simulation-based methods are (currently only) supported.
+#' Simulation-based methods are (currently only) supported.
 #'
 #' @param x Data frame of calculated statistics or containing attributes of
 #'   theoretical distribution values.

From 47881518a200dfb60debfbd32e9b385440627743 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 09:49:09 -0700
Subject: [PATCH 39/78] Update conf_int.R

---
 R/conf_int.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/conf_int.R b/R/conf_int.R
index 397891f5..7acecbc3 100644
--- a/R/conf_int.R
+++ b/R/conf_int.R
@@ -17,7 +17,7 @@
 #'   the confidence interval.
 #' @section Aliases:
 #' `get_ci()` is an alias of `get_confidence_interval()`.
-#' `conf_int` is a deprecated alias of `get_confidence_interval()`.
+#' `conf_int()` is a deprecated alias of `get_confidence_interval()`.
 #'
 #' @examples
 #' # Prepare the dataset

From 0dc65810328d5f24598dd5ef6e863379161b33cc Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 09:54:12 -0700
Subject: [PATCH 40/78] Update p_value.R

---
 R/p_value.R | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/R/p_value.R b/R/p_value.R
index 5758f8a4..6abbfa7f 100644
--- a/R/p_value.R
+++ b/R/p_value.R
@@ -122,11 +122,12 @@ two_sided_p_value <- function(x, obs_stat){
 #     param2 <- attr(x, "distr_param2")
 #
 #   if(theory_type == "Two sample t")
-#     return(pt(q = obs_stat,
-#                              df = param,
-#                              lower.tail = set_lower_tail(direction))
-#          )
-#
+#     return(
+#       pt(q = obs_stat,
+#          df = param,
+#          lower.tail = set_lower_tail(direction)
+#         )
+#     )
 # }
 
 #theory_t_pvalue <-

From 9746fb33b13feb9c6167a67f61621c10e639ff0f Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 3 Sep 2018 10:22:00 -0700
Subject: [PATCH 41/78] Update tests and file names

---
 R/{conf_int.R => get_confidence_interval.R}   |  0
 R/{p_value.R => get_p_value.R}                |  0
 man/get_confidence_interval.Rd                |  4 ++--
 man/get_p_value.Rd                            |  4 ++--
 tests/testthat/helper-data.R                  |  7 ++++++
 tests/testthat/test-aliases.R                 | 24 +++++++++++++++++++
 tests/testthat/test-conf_int.R                | 20 ----------------
 tests/testthat/test-get_confidence_interval.R | 20 ++++++++++++++++
 .../{test-p_value.R => test-get_p_value.R}    |  1 +
 9 files changed, 56 insertions(+), 24 deletions(-)
 rename R/{conf_int.R => get_confidence_interval.R} (100%)
 rename R/{p_value.R => get_p_value.R} (100%)
 create mode 100644 tests/testthat/test-aliases.R
 delete mode 100644 tests/testthat/test-conf_int.R
 create mode 100644 tests/testthat/test-get_confidence_interval.R
 rename tests/testthat/{test-p_value.R => test-get_p_value.R} (99%)

diff --git a/R/conf_int.R b/R/get_confidence_interval.R
similarity index 100%
rename from R/conf_int.R
rename to R/get_confidence_interval.R
diff --git a/R/p_value.R b/R/get_p_value.R
similarity index 100%
rename from R/p_value.R
rename to R/get_p_value.R
diff --git a/man/get_confidence_interval.Rd b/man/get_confidence_interval.Rd
index db5096fe..211204c8 100644
--- a/man/get_confidence_interval.Rd
+++ b/man/get_confidence_interval.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/conf_int.R
+% Please edit documentation in R/get_confidence_interval.R
 \name{get_confidence_interval}
 \alias{get_confidence_interval}
 \alias{get_ci}
@@ -35,7 +35,7 @@ Only simulation-based methods are (currently only) supported.
 \section{Aliases}{
 
 \code{get_ci()} is an alias of \code{get_confidence_interval()}.
-\code{conf_int} is a deprecated alias of \code{get_confidence_interval()}.
+\code{conf_int()} is a deprecated alias of \code{get_confidence_interval()}.
 }
 
 \examples{
diff --git a/man/get_p_value.Rd b/man/get_p_value.Rd
index 5b935608..11d9e49a 100644
--- a/man/get_p_value.Rd
+++ b/man/get_p_value.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/p_value.R
+% Please edit documentation in R/get_p_value.R
 \name{get_p_value}
 \alias{get_p_value}
 \alias{get_pvalue}
@@ -23,7 +23,7 @@ extreme than this).}
 A 1x1 data frame with value between 0 and 1.
 }
 \description{
-Only simulation-based methods are (currently only) supported.
+Simulation-based methods are (currently only) supported.
 }
 \section{Aliases}{
 
diff --git a/tests/testthat/helper-data.R b/tests/testthat/helper-data.R
index 12701584..69720a13 100644
--- a/tests/testthat/helper-data.R
+++ b/tests/testthat/helper-data.R
@@ -16,3 +16,10 @@ mtcars_df <- mtcars %>%
     cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear),
     carb = factor(carb)
   )
+
+obs_diff <- iris_tbl %>%
+  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
+  calculate(stat = "diff in props", order = c("large", "small"))
+
+set.seed(2018)
+test_df <- tibble::tibble(stat = rnorm(100))
diff --git a/tests/testthat/test-aliases.R b/tests/testthat/test-aliases.R
new file mode 100644
index 00000000..705308f8
--- /dev/null
+++ b/tests/testthat/test-aliases.R
@@ -0,0 +1,24 @@
+context("aliases")
+
+test_that("aliases work", {
+  expect_gt(
+    iris_calc %>%
+      get_pvalue(obs_stat = -0.2, direction = "right") %>%
+      dplyr::pull(),
+    expected = 0.98
+  )
+  
+  expect_silent(test_df %>% get_ci())
+})
+
+test_that("old aliases produce warning", {
+  expect_warning(
+    iris_calc %>%
+      p_value(obs_stat = -0.2, direction = "right") %>%
+      dplyr::pull(),
+    expected = 0.98
+  )
+  
+  expect_warning(test_df %>% conf_int())
+  
+})
\ No newline at end of file
diff --git a/tests/testthat/test-conf_int.R b/tests/testthat/test-conf_int.R
deleted file mode 100644
index 4a8e9727..00000000
--- a/tests/testthat/test-conf_int.R
+++ /dev/null
@@ -1,20 +0,0 @@
-context("conf_int")
-
-obs_diff <- iris_tbl %>%
-  specify(Sepal.Length.Group ~ Sepal.Width.Group, success = "<=5") %>%
-  calculate(stat = "diff in props", order = c("large", "small"))
-
-set.seed(2018)
-test_df <- tibble::tibble(stat = rnorm(100))
-
-test_that("basics work", {
-  expect_silent(test_df %>% get_confidence_interval())
-  expect_error(test_df  %>% get_confidence_interval(type = "other"))
-  expect_error(test_df  %>% get_confidence_interval(level = 1.2))
-  expect_error(test_df  %>% get_confidence_interval(point_estimate = "help"))
-
-  expect_silent(iris_calc %>% get_confidence_interval(type = "se", point_estimate = 4))
-  expect_silent(iris_calc %>% get_confidence_interval(type = "se", point_estimate = obs_diff))
-  expect_error(iris_calc  %>% get_confidence_interval(type = "se", point_estimate = "error"))
-  expect_error(iris_calc  %>% get_confidence_interval(type = "se"))
-})
diff --git a/tests/testthat/test-get_confidence_interval.R b/tests/testthat/test-get_confidence_interval.R
new file mode 100644
index 00000000..918defe5
--- /dev/null
+++ b/tests/testthat/test-get_confidence_interval.R
@@ -0,0 +1,20 @@
+context("conf_int")
+
+test_that("basics work", {
+  expect_silent(test_df %>% get_confidence_interval())
+  expect_error(test_df %>% get_confidence_interval(type = "other"))
+  expect_error(test_df %>% get_confidence_interval(level = 1.2))
+  expect_error(test_df %>% get_confidence_interval(point_estimate = "help"))
+
+  expect_silent(iris_calc %>% 
+                  get_confidence_interval(type = "se", 
+                                          point_estimate = 4))
+  expect_silent(iris_calc %>% 
+                  get_confidence_interval(type = "se", 
+                                          point_estimate = obs_diff))
+  expect_error(iris_calc %>% 
+                 get_confidence_interval(type = "se", 
+                                         point_estimate = "error"))
+  expect_error(iris_calc %>% 
+                 get_confidence_interval(type = "se"))
+})
diff --git a/tests/testthat/test-p_value.R b/tests/testthat/test-get_p_value.R
similarity index 99%
rename from tests/testthat/test-p_value.R
rename to tests/testthat/test-get_p_value.R
index 1c5c063f..03b3a96a 100644
--- a/tests/testthat/test-p_value.R
+++ b/tests/testthat/test-get_p_value.R
@@ -47,3 +47,4 @@ test_that("get_p_value makes sense", {
     expected = 0
   )
 })
+

From 70eeb686d6441dec0d1a012f264605fb0bfb51ab Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 10:57:23 -0700
Subject: [PATCH 42/78] Update test-calculate.R

---
 tests/testthat/test-calculate.R | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tests/testthat/test-calculate.R b/tests/testthat/test-calculate.R
index dd2bacea..3aa32c6a 100644
--- a/tests/testthat/test-calculate.R
+++ b/tests/testthat/test-calculate.R
@@ -1,7 +1,5 @@
 context("calculate")
 
-iris_df <- tibble::as_tibble(iris)
-
 # calculate arguments
 test_that("x is a tibble", {
   vec <- 1:10

From cf4b3bf70e2f0447af44c629bbcce02b0c469436 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 10:58:35 -0700
Subject: [PATCH 43/78] Update helper-data.R

---
 tests/testthat/helper-data.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/testthat/helper-data.R b/tests/testthat/helper-data.R
index 12701584..e92b8d18 100644
--- a/tests/testthat/helper-data.R
+++ b/tests/testthat/helper-data.R
@@ -1,3 +1,5 @@
+iris_df <- tibble::as_tibble(iris)
+
 iris_tbl <- iris %>%
   tibble::as_tibble() %>%
   dplyr::mutate(

From 794f038cdf6dcb04fcfccdd3be09c2442bbb1bdc Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 3 Sep 2018 11:21:04 -0700
Subject: [PATCH 44/78] Comment out extra check that cannot currently get to

---
 R/generate.R | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/R/generate.R b/R/generate.R
index fda851df..d6cb0f4c 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -70,13 +70,15 @@ compare_type_vs_auto_type <- function(type, auto_type) {
 }
 
 use_auto_type <- function(auto_type) {
-  if(is.null(auto_type)) {
-    stop_glue(
-      "There is no default `type`;",
-      "please set it to one of {toString(shQuote(GENERATION_TYPES))}.",
-      .sep = " "
-    )
-  }
+  ## Commented out since no way to currently get to this
+  ## All variable types specified have an auto_type set 
+  # if(is.null(auto_type)) {
+  #   stop_glue(
+  #     "There is no default `type`;",
+  #     "please set it to one of {toString(shQuote(GENERATION_TYPES))}.",
+  #     .sep = " "
+  #   )
+  # }
   message_glue('Setting `type = "{auto_type}"`.')
   auto_type
 }

From c955327307cc526a83e3dab545bab2001dc9ebd8 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 11:35:54 -0700
Subject: [PATCH 45/78] Update specify.R

---
 R/specify.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/specify.R b/R/specify.R
index 61703547..46cc0070 100755
--- a/R/specify.R
+++ b/R/specify.R
@@ -45,8 +45,8 @@ specify <- function(x, formula, response = NULL,
   if (methods::hasArg(formula)) {
 
     tryCatch(
-       formula_arg_is_formula <- rlang::is_formula(formula)
-      , error = function(e) {
+       formula_arg_is_formula <- rlang::is_formula(formula), 
+      error = function(e) {
         stop_glue("The argument you passed in for the formula does not exist.
                   * Were you trying to pass in an unquoted column name?
                   * Did you forget to name one or more arguments?")

From 89c9275563ce167fcbec74ecec341eb0aa55e81c Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Mon, 3 Sep 2018 12:10:38 -0700
Subject: [PATCH 46/78] Update DESCRIPTION

---
 DESCRIPTION | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b2fea636..cd371d59 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -7,6 +7,7 @@ Authors@R: c(
     person("Chester", "Ismay", email = "chester.ismay@gmail.com", role = "aut"),
     person("Ben", "Baumer", email = "ben.baumer@gmail.com", role = "aut"),
     person("Mine", "Cetinkaya-Rundel", email = "mine@stat.duke.edu", role = "aut"),
+    person("Evgeni", "Chasnovski", email = "evgeni.chasnovski@gmail.com", role = "ctb"),
     person("Ted", "Laderas", email = "tedladeras@gmail.com", role = "ctb"),
     person("Nick", "Solomon", email = "nick.solomon@datacamp.com", role = "ctb"),
     person("Johanna", "Hardin", email = "Jo.Hardin@pomona.edu", role = "ctb"),
@@ -14,7 +15,8 @@ Authors@R: c(
     person("Neal", "Fultz", email = "nfultz@gmail.com", role = "ctb"),
     person("Doug", "Friedman", email = "doug.nhp@gmail.com", role = "ctb"),
     person("Richie", "Cotton", email = "richie@datacamp.com", role = "ctb"),
-    person("Evgeni", "Chasnovski", email = "evgeni.chasnovski@gmail.com", role = "ctb"))
+    person("Brian", "Fannon", email = "captain@pirategrunt.com", role = "ctb")
+)
 Description: The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework. 
 License: CC0
 Encoding: UTF-8

From 77a0875ead019512c69d9333947758b5348496fd Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 3 Sep 2018 12:26:43 -0700
Subject: [PATCH 47/78] Add Brian Fannon

---
 DESCRIPTION | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index cd371d59..c3301693 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -15,8 +15,7 @@ Authors@R: c(
     person("Neal", "Fultz", email = "nfultz@gmail.com", role = "ctb"),
     person("Doug", "Friedman", email = "doug.nhp@gmail.com", role = "ctb"),
     person("Richie", "Cotton", email = "richie@datacamp.com", role = "ctb"),
-    person("Brian", "Fannon", email = "captain@pirategrunt.com", role = "ctb")
-)
+    person("Brian", "Fannon", email = "captain@pirategrunt.com", role = "ctb"))
 Description: The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework. 
 License: CC0
 Encoding: UTF-8

From a16590b39ce8a66fccd21aee5c672898f335c7fc Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Mon, 3 Sep 2018 13:01:55 -0700
Subject: [PATCH 48/78] Fix broken tests (warnings <-> errors)

---
 DESCRIPTION                    |  3 +-
 R/generate.R                   | 24 ++++++++-------
 tests/testthat/test-generate.R | 54 +++++++++++++++++++++-------------
 3 files changed, 49 insertions(+), 32 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b2fea636..c3301693 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -7,6 +7,7 @@ Authors@R: c(
     person("Chester", "Ismay", email = "chester.ismay@gmail.com", role = "aut"),
     person("Ben", "Baumer", email = "ben.baumer@gmail.com", role = "aut"),
     person("Mine", "Cetinkaya-Rundel", email = "mine@stat.duke.edu", role = "aut"),
+    person("Evgeni", "Chasnovski", email = "evgeni.chasnovski@gmail.com", role = "ctb"),
     person("Ted", "Laderas", email = "tedladeras@gmail.com", role = "ctb"),
     person("Nick", "Solomon", email = "nick.solomon@datacamp.com", role = "ctb"),
     person("Johanna", "Hardin", email = "Jo.Hardin@pomona.edu", role = "ctb"),
@@ -14,7 +15,7 @@ Authors@R: c(
     person("Neal", "Fultz", email = "nfultz@gmail.com", role = "ctb"),
     person("Doug", "Friedman", email = "doug.nhp@gmail.com", role = "ctb"),
     person("Richie", "Cotton", email = "richie@datacamp.com", role = "ctb"),
-    person("Evgeni", "Chasnovski", email = "evgeni.chasnovski@gmail.com", role = "ctb"))
+    person("Brian", "Fannon", email = "captain@pirategrunt.com", role = "ctb"))
 Description: The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework. 
 License: CC0
 Encoding: UTF-8
diff --git a/R/generate.R b/R/generate.R
index d6cb0f4c..5ee1e324 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -60,8 +60,8 @@ compare_type_vs_auto_type <- function(type, auto_type) {
   if (auto_type != type) {
     # User is overriding the default, so warn of potential stupidity.
     warning_glue(
-      "You have specified `type = \"{type}\"`, but `type` is expected",
-      "to be`\"{auto_type}\"`. This workflow is untested and",
+      "You have given `type = \"{type}\"`, but `type` is expected",
+      "to be `\"{auto_type}\"`. This workflow is untested and",
       "the results may not mean what you think they mean.",
       .sep = " "
     )
@@ -79,7 +79,7 @@ use_auto_type <- function(auto_type) {
   #     .sep = " "
   #   )
   # }
-  message_glue('Setting `type = "{auto_type}"`.')
+  message_glue('Setting `type = "{auto_type}"` in `generate()`.')
   auto_type
 }
 
@@ -98,10 +98,11 @@ bootstrap <- function(x, reps = 1, ...) {
   if (!is_nuat(x, "null")) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
-    if (attr(attr(x, "params"), "names") == "mu") {
-      col <- as.character(attr(x, "response"))
+    if (!is.null(attr(attr(x, "params"), "names"))){
+      if (attr(attr(x, "params"), "names") == "mu") {
+        col <- as.character(attr(x, "response"))
 #      if (attr(x, "theory_type") != "One sample t") {
-      x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
+        x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
 #      }
 
       # Standardize after centering above
@@ -116,11 +117,11 @@ bootstrap <- function(x, reps = 1, ...) {
     }
 
     # Similarly for median
-    else if (attr(attr(x, "params"), "names") == "med") {
-      col <- as.character(attr(x, "response"))
-      x[[col]] <- x[[col]] -
-        stats::median(x[[col]], na.rm = TRUE) + attr(x, "params")
-    }
+      else if (attr(attr(x, "params"), "names") == "med") {
+        col <- as.character(attr(x, "response"))
+        x[[col]] <- x[[col]] -
+          stats::median(x[[col]], na.rm = TRUE) + attr(x, "params")
+      }
 
     # Implement confidence interval for bootstrapped proportions?
     # Implement z transformation?
@@ -132,6 +133,7 @@ bootstrap <- function(x, reps = 1, ...) {
     #   x[[col]] <- x[[col]] -
     #     stats::sd(x[[col]], na.rm = TRUE) + attr(x, "params")
     # }
+    }
   }
 
   # Set variables for use in calculate()
diff --git a/tests/testthat/test-generate.R b/tests/testthat/test-generate.R
index 76cc80ff..04f70b31 100644
--- a/tests/testthat/test-generate.R
+++ b/tests/testthat/test-generate.R
@@ -38,28 +38,36 @@ hyp_anova <- mtcars_df %>%
 
 test_that("cohesion with type argument", {
   expect_warning(generate(hyp_prop, type = "bootstrap"))
-  expect_error(generate(hyp_diff_in_props, type = "bootstrap"))
+  expect_warning(generate(hyp_diff_in_props, type = "bootstrap"))
   expect_warning(generate(hyp_chisq_gof, type = "bootstrap"))
-  expect_error(generate(hyp_chisq_ind, type = "bootstrap"))
+  expect_warning(generate(hyp_chisq_ind, type = "bootstrap"))
   expect_silent(generate(hyp_mean, type = "bootstrap"))
   expect_silent(generate(hyp_median, type = "bootstrap"))
   expect_silent(generate(hyp_sd, type = "bootstrap"))
-  expect_error(generate(hyp_diff_in_means, type = "bootstrap"))
-  expect_error(generate(hyp_anova, type = "bootstrap"))
+  expect_warning(generate(hyp_diff_in_means, type = "bootstrap"))
+  expect_warning(generate(hyp_anova, type = "bootstrap"))
 
   expect_silent(generate(hyp_prop, type = "simulate"))
   expect_warning(generate(hyp_diff_in_props, type = "simulate"))
   expect_silent(generate(hyp_chisq_gof, type = "simulate"))
   expect_warning(generate(hyp_chisq_ind, type = "simulate"))
-  expect_error(generate(hyp_mean, type = "simulate"))
+  expect_error(
+    expect_warning(generate(hyp_mean, type = "simulate"))
+  )
   expect_warning(generate(hyp_diff_in_means, type = "simulate"))
   expect_warning(generate(hyp_anova, type = "simulate"))
 
-  expect_error(generate(hyp_prop, type = "permute"))
+  expect_error(
+    expect_warning(generate(hyp_prop, type = "permute"))
+  )
   expect_silent(generate(hyp_diff_in_props, type = "permute"))
-  expect_error(generate(hyp_chisq_gof, type = "permute"))
+  expect_error(
+    expect_warning(generate(hyp_chisq_gof, type = "permute"))
+  )
   expect_silent(generate(hyp_chisq_ind, type = "permute"))
-  expect_error(generate(hyp_mean, type = "permute"))
+  expect_error(
+    expect_warning(generate(hyp_mean, type = "permute"))
+  )
   expect_silent(generate(hyp_diff_in_means, type = "permute"))
   expect_silent(generate(hyp_anova, type = "permute"))
 })
@@ -154,10 +162,12 @@ test_that("auto `type` works (generate)", {
   expect_equal(attr(two_props_boot, "type"), "bootstrap")
   expect_equal(attr(slope_boot, "type"), "bootstrap")
 
-  expect_error(mtcars_df %>%
+  expect_error(
+    expect_warning(mtcars_df %>%
       specify(response = mpg) %>% # formula alt: mpg ~ NULL
       hypothesize(null = "point", mu = 25) %>%
       generate(reps = 100, type = "permute")
+    )
   )
 
   expect_warning(mtcars_df %>%
@@ -165,10 +175,12 @@ test_that("auto `type` works (generate)", {
       generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars_df %>%
-      specify(response = mpg) %>% # formula alt: mpg ~ NULL
-      hypothesize(null = "point", med = 26) %>%
-      generate(reps = 100, type = "permute")
+  expect_warning(
+    expect_error(mtcars_df %>%
+        specify(response = mpg) %>% # formula alt: mpg ~ NULL
+        hypothesize(null = "point", med = 26) %>%
+        generate(reps = 100, type = "permute")
+    )
   )
 
   expect_warning(mtcars_df %>%
@@ -177,7 +189,7 @@ test_that("auto `type` works (generate)", {
       generate(reps = 100, type = "bootstrap")
   )
 
-  expect_error(mtcars_df %>%
+  expect_warning(mtcars_df %>%
       specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap")
@@ -195,7 +207,7 @@ test_that("auto `type` works (generate)", {
       generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars_df %>%
+  expect_warning(mtcars_df %>%
       specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap"))
@@ -206,7 +218,7 @@ test_that("auto `type` works (generate)", {
       generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars_df %>%
+  expect_warning(mtcars_df %>%
       specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "bootstrap")
@@ -217,9 +229,11 @@ test_that("auto `type` works (generate)", {
       generate(reps = 100, type = "simulate")
   )
 
-  expect_error(mtcars_df %>%
-      specify(mpg ~ am) %>%
-      generate(reps = 100, type = "permute")
+  expect_error(
+    expect_warning(mtcars_df %>%
+        specify(mpg ~ am) %>%
+        generate(reps = 100, type = "permute")
+    )
   )
 
   expect_warning(mtcars_df %>%
@@ -256,7 +270,7 @@ test_that("mismatches lead to error", {
 test_that("generate() handles `NULL` value of `type`", {
   expect_message(
     generate(hyp_prop, type = NULL),
-    'Setting `type = "simulate"`.',
+    'Setting `type = "simulate"` in `generate()`.',
     fixed = TRUE
   )
 })

From e8d28096b84fc0fc890e5f7d8fcc77eb63ceaded Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Sat, 8 Sep 2018 18:42:14 -0700
Subject: [PATCH 49/78] Add error message if theoretical p-value requested (not
 yet impl'd)

---
 R/get_p_value.R                   | 24 ++++++++++++++++--------
 man/get_p_value.Rd                |  3 +--
 tests/testthat/helper-data.R      |  1 +
 tests/testthat/test-get_p_value.R | 19 +++++++++++++++++++
 4 files changed, 37 insertions(+), 10 deletions(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index 6abbfa7f..785765ff 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -2,8 +2,7 @@
 #'
 #' Simulation-based methods are (currently only) supported.
 #'
-#' @param x Data frame of calculated statistics or containing attributes of
-#'   theoretical distribution values.
+#' @param x Data frame of calculated statistics as returned by \code{\link{`generate()`}}
 #' @param obs_stat A numeric value or a 1x1 data frame (as extreme or more
 #'   extreme than this).
 #' @param direction A character string. Options are `"less"`, `"greater"`, or
@@ -44,15 +43,20 @@ NULL
 get_p_value <- function(x, obs_stat, direction){
 
   check_type(x, is.data.frame)
+  if(!is_generated(x)) {
+    stop_glue(
+      "Theoretical p-values are not yet supported.",
+      "`x` should be the result of calling `generate()`.",
+      .sep = " "
+    )
+  }
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
 
-  is_simulation_based <- !is.null(attr(x, "generate")) &&
-    attr(x, "generate")
-
-  if(is_simulation_based)
-    pvalue <- simulation_based_p_value(x = x, obs_stat = obs_stat,
-      direction = direction)
+  pvalue <- simulation_based_p_value(
+    x = x, 
+    obs_stat = obs_stat,
+    direction = direction)
 
   ## Theoretical-based p-value
   # Could be more specific
@@ -115,6 +119,10 @@ two_sided_p_value <- function(x, obs_stat){
     return(tibble::tibble(p_value = basic_p_value))
 }
 
+is_generated <- function(x) {
+  !is.null(attr(x, "generate")) && attr(x, "generate")
+}
+
 # which_distribution <- function(x, theory_type, obs_stat, direction){
 #
 #   param <- attr(x, "distr_param")
diff --git a/man/get_p_value.Rd b/man/get_p_value.Rd
index 11d9e49a..65a1d4fd 100644
--- a/man/get_p_value.Rd
+++ b/man/get_p_value.Rd
@@ -10,8 +10,7 @@ get_p_value(x, obs_stat, direction)
 get_pvalue(x, obs_stat, direction)
 }
 \arguments{
-\item{x}{Data frame of calculated statistics or containing attributes of
-theoretical distribution values.}
+\item{x}{Data frame of calculated statistics as returned by \code{\link{`generate()`}}}
 
 \item{obs_stat}{A numeric value or a 1x1 data frame (as extreme or more
 extreme than this).}
diff --git a/tests/testthat/helper-data.R b/tests/testthat/helper-data.R
index 24d4c4b8..245816b6 100644
--- a/tests/testthat/helper-data.R
+++ b/tests/testthat/helper-data.R
@@ -25,3 +25,4 @@ obs_diff <- iris_tbl %>%
 
 set.seed(2018)
 test_df <- tibble::tibble(stat = rnorm(100))
+
diff --git a/tests/testthat/test-get_p_value.R b/tests/testthat/test-get_p_value.R
index 03b3a96a..71d6cb33 100644
--- a/tests/testthat/test-get_p_value.R
+++ b/tests/testthat/test-get_p_value.R
@@ -46,5 +46,24 @@ test_that("get_p_value makes sense", {
       dplyr::pull(),
     expected = 0
   )
+  expect_error(
+    iris_calc %>%
+      get_p_value(
+        obs_stat = median(iris_calc$stat) + 1, direction = "wrong"
+      )
+  )
 })
 
+test_that("theoretical p-value not supported error", {
+  obs_F <- iris_tbl %>% 
+    specify(Sepal.Width ~ Species) %>%
+    calculate(stat = "F") 
+  expect_error(
+    iris_tbl %>% 
+      specify(Sepal.Width ~ Species) %>%
+      hypothesize(null = "independence") %>% 
+      calculate(stat = "F") %>% 
+      get_p_value(obs_stat = obs_F, direction = "right")
+  )
+  
+})

From 5a5ea93f4564979a89245972a8293f3824e8dc52 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Sat, 8 Sep 2018 22:05:57 -0700
Subject: [PATCH 50/78] Switch to markdown roxygen

---
 R/get_p_value.R    | 4 ++--
 man/get_p_value.Rd | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index 785765ff..8a831897 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -2,11 +2,11 @@
 #'
 #' Simulation-based methods are (currently only) supported.
 #'
-#' @param x Data frame of calculated statistics as returned by \code{\link{`generate()`}}
+#' @param x Data frame of calculated statistics as returned by [generate()]
 #' @param obs_stat A numeric value or a 1x1 data frame (as extreme or more
 #'   extreme than this).
 #' @param direction A character string. Options are `"less"`, `"greater"`, or
-#'   `"two_sided"`. Can also specify `"left"`, `"right"`, or `"both"`.
+#'   `"two_sided"`. Can also use `"left"`, `"right"`, or `"both"`.
 #'
 #' @return A 1x1 data frame with value between 0 and 1.
 #'
diff --git a/man/get_p_value.Rd b/man/get_p_value.Rd
index 65a1d4fd..83e5cac1 100644
--- a/man/get_p_value.Rd
+++ b/man/get_p_value.Rd
@@ -10,13 +10,13 @@ get_p_value(x, obs_stat, direction)
 get_pvalue(x, obs_stat, direction)
 }
 \arguments{
-\item{x}{Data frame of calculated statistics as returned by \code{\link{`generate()`}}}
+\item{x}{Data frame of calculated statistics as returned by \code{\link[=generate]{generate()}}}
 
 \item{obs_stat}{A numeric value or a 1x1 data frame (as extreme or more
 extreme than this).}
 
 \item{direction}{A character string. Options are \code{"less"}, \code{"greater"}, or
-\code{"two_sided"}. Can also specify \code{"left"}, \code{"right"}, or \code{"both"}.}
+\code{"two_sided"}. Can also use \code{"left"}, \code{"right"}, or \code{"both"}.}
 }
 \value{
 A 1x1 data frame with value between 0 and 1.

From 8c521e0f0477d4191d33239345180acc462c45c8 Mon Sep 17 00:00:00 2001
From: ismayc <chester.ismay@gmail.com>
Date: Sat, 8 Sep 2018 22:18:58 -0700
Subject: [PATCH 51/78] Added better assertion for theoretical so that p-values
 w/o {infer} work

---
 R/get_p_value.R                   | 6 +++++-
 tests/testthat/test-get_p_value.R | 4 ++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index 8a831897..f081f98a 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -43,7 +43,7 @@ NULL
 get_p_value <- function(x, obs_stat, direction){
 
   check_type(x, is.data.frame)
-  if(!is_generated(x)) {
+  if(!is_generated(x) & is_hypothesized(x)) {
     stop_glue(
       "Theoretical p-values are not yet supported.",
       "`x` should be the result of calling `generate()`.",
@@ -123,6 +123,10 @@ is_generated <- function(x) {
   !is.null(attr(x, "generate")) && attr(x, "generate")
 }
 
+is_hypothesized <- function(x){
+  !is.null(attr(x, "null"))
+}
+
 # which_distribution <- function(x, theory_type, obs_stat, direction){
 #
 #   param <- attr(x, "distr_param")
diff --git a/tests/testthat/test-get_p_value.R b/tests/testthat/test-get_p_value.R
index 71d6cb33..b1d78aec 100644
--- a/tests/testthat/test-get_p_value.R
+++ b/tests/testthat/test-get_p_value.R
@@ -8,6 +8,10 @@ test_that("direction is appropriate", {
 })
 
 test_that("get_p_value makes sense", {
+  expect_silent(
+    test_df %>% 
+      get_p_value(obs_stat = 0.7, direction = "right")
+    )
   expect_lt(
     iris_calc %>%
       get_p_value(obs_stat = 0.1, direction = "right") %>%

From b09d853002c05310626ed8975eec887b9b3d7ac9 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Sat, 8 Sep 2018 22:37:33 -0700
Subject: [PATCH 52/78] Update .travis.yml

---
 .travis.yml | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 867a3310..d37dc8cc 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -33,11 +33,8 @@ deploy:
   keep-history: true
   local-dir: docs
   github_token: $GITHUBTRAVIS
-#  target-branch: gh-pages-dev
-  target-branch: gh-pages
-  on:
-#   branch: develop
-    branch: master
+  target-branch: gh-pages-dev
+#  target-branch: gh-pages
 
 after_success:
   - Rscript -e 'covr::codecov()'

From 30f813c69fe2c3e56b23e7097603673b6146f753 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Thu, 13 Sep 2018 17:41:55 +0300
Subject: [PATCH 53/78] Start refactoring of `visualize()` (#178).

Also:
- Adds `shade_p_value()`, `shade_pvalue()`, `shade_confidence_interval()`, and `shade_ci()`.
---
 NAMESPACE                        |   4 +
 R/infer.R                        |   6 +-
 R/visualize.R                    | 766 ++++++++++++++++---------------
 man/shade_confidence_interval.Rd |  51 ++
 man/shade_p_value.Rd             |  55 +++
 man/visualize.Rd                 |  31 +-
 tests/testthat/test-visualize.R  |  82 +++-
 7 files changed, 595 insertions(+), 400 deletions(-)
 create mode 100644 man/shade_confidence_interval.Rd
 create mode 100644 man/shade_p_value.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 4f8da22c..bb52bb40 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -15,6 +15,10 @@ export(get_pvalue)
 export(hypothesize)
 export(p_value)
 export(rep_sample_n)
+export(shade_ci)
+export(shade_confidence_interval)
+export(shade_p_value)
+export(shade_pvalue)
 export(specify)
 export(t_stat)
 export(t_test)
diff --git a/R/infer.R b/R/infer.R
index 69614a05..5978a096 100755
--- a/R/infer.R
+++ b/R/infer.R
@@ -18,9 +18,9 @@ if (getRversion() >= "2.15.1") {
   utils::globalVariables(
     c(
       "prop", "stat", "value", "x", "..density..", "statistic", ".",
-      "parameter", "p.value", "xmin", "xmax", "density", "denom", "diff_prop",
-      "group_num", "n1", "n2", "num_suc", "p_hat", "total_suc", "explan",
-      "probs", "conf.low", "conf.high"
+      "parameter", "p.value", "xmin", "x_min", "xmax", "x_max", "density",
+      "denom", "diff_prop", "group_num", "n1", "n2", "num_suc", "p_hat",
+      "total_suc", "explan", "probs", "conf.low", "conf.high"
     )
   )
 }
diff --git a/R/visualize.R b/R/visualize.R
index f2847ad3..91983036 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -11,28 +11,42 @@
 #' @param dens_color A character or hex string specifying the color of the
 #'   theoretical density curve.
 #' @param obs_stat A numeric value or 1x1 data frame corresponding to what the
-#'   observed statistic is.
+#'   observed statistic is. **Deprecated (see Details)**.
 #' @param obs_stat_color A character or hex string specifying the color of the
-#'   observed statistic as a vertical line on the plot.
+#'   observed statistic as a vertical line on the plot. **Deprecated (see
+#'   Details)**.
 #' @param pvalue_fill A character or hex string specifying the color to shade
 #'   the p-value. In previous versions of the package this was the `shade_color`
-#'   argument.
+#'   argument. **Deprecated (see Details)**.
 #' @param direction A string specifying in which direction the shading should
 #'   occur. Options are `"less"`, `"greater"`, or `"two_sided"` for p-value. Can
 #'   also give `"left"`, `"right"`, or `"both"` for p-value. For confidence
 #'   intervals, use `"between"` and give the endpoint values in `endpoints`.
+#'   **Deprecated (see Details)**.
 #' @param endpoints A 2 element vector or a 1 x 2 data frame containing the
 #'   lower and upper values to be plotted. Most useful for visualizing
-#'   conference intervals.
+#'   conference intervals. **Deprecated (see Details)**.
 #' @param endpoints_color A character or hex string specifying the color of the
-#'   observed statistic as a vertical line on the plot.
+#'   observed statistic as a vertical line on the plot. **Deprecated (see
+#'   Details)**.
 #' @param ci_fill A character or hex string specifying the color to shade the
-#'   confidence interval.
-#' @param ... Other arguments passed along to ggplot2.
+#'   confidence interval. **Deprecated (see Details)**.
+#' @param ... Other arguments passed along to \\{ggplot2\\} functions.
 #'
+#' @details In order to make visualization workflow more straightforward and
+#' explicit `visualize()` now only should be used to plot statistics directly.
+#' That is why arguments not related to this task are deprecated and will be
+#' removed in a future release of \\{infer\\}.
+#' 
+#' To add to plot information related to p-value use [shade_p_value()]. To add
+#' to plot information related to confidence interval use
+#' [shade_confidence_interval()].
+#' 
 #' @return A ggplot object showing the simulation-based distribution as a
 #'   histogram or bar graph. Also used to show the theoretical curves.
 #'
+#' @seealso [shade_p_value()], [shade_confidence_interval()].
+#'
 #' @examples
 #' # Permutations to create a simulation-based null distribution for
 #' # one numerical response and one categorical predictor
@@ -79,6 +93,43 @@ visualize <- function(data, bins = 15, method = "simulation",
                       endpoints_color = "mediumaquamarine",
                       ci_fill = "turquoise",
                       ...) {
+  check_visualize_args(
+    data, bins, method, dens_color, obs_stat, obs_stat_color,
+    pvalue_fill, direction, endpoints, endpoints_color, ci_fill
+  )
+  warn_depricated_args(obs_stat, endpoints)
+  endpoints <- impute_enpoints(endpoints)
+  obs_stat <- impute_obs_stat(obs_stat, direction, endpoints)
+  
+  # Add `method` to `data` attributes to enable later possibility of
+  # complicated computation of p-value regions (in case `direction = "both"`)
+  # in `shade_p_value()`.
+  attr(data, "viz_method") <- method
+  
+  infer_plot <- ggplot(data) +
+    simulation_layer(data, bins, ...) +
+    theoretical_layer(data, dens_color, ...) +
+    title_labels_layer(data) +
+    shade_p_value(
+      obs_stat, direction, obs_stat_color, pvalue_fill, ...
+    )
+  
+  if (!is.null(direction) && (direction == "between")) {
+    infer_plot <- infer_plot +
+      shade_ci(endpoints, endpoints_color, ci_fill, ...)
+  }
+  
+  infer_plot
+}
+
+#' @rdname visualize
+#' @export
+visualise <- visualize
+
+check_visualize_args <- function(data, bins, method, dens_color,
+                                 obs_stat, obs_stat_color,
+                                 pvalue_fill, direction,
+                                 endpoints, endpoints_color, ci_fill) {
   check_type(data, is.data.frame)
   check_type(bins, is.numeric)
   check_type(method, is.character)
@@ -96,417 +147,369 @@ visualize <- function(data, bins = 15, method = "simulation",
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
     )
   }
-  if (is.vector(endpoints) && (length(endpoints) != 2)) {
-    warning_glue(
-      "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector. ",
-      "Using the first two entries as the `endpoints`."
-    )
-    endpoints <- endpoints[1:2]
-  }
-  if (is.data.frame(endpoints)) {
-    endpoints <- unlist(endpoints)
-  }
-  obs_stat <- check_obs_stat(obs_stat)
-  if (
-    !is.null(direction) &&
-    (is.null(obs_stat) + is.null(endpoints) != 1)
-  ) {
+  
+  if (!(method %in% c("simulation", "theoretical", "both"))) {
     stop_glue(
-      "Shading requires either `endpoints` values for a confidence interval ",
-      "or the observed statistic `obs_stat` to be provided."
+      'Provide `method` with one of three options: `"theoretical"`, `"both"`, ',
+      'or `"simulation"`. `"simulation"` is the default.'
     )
   }
-
-  if (method == "simulation") {
-    infer_plot <- visualize_simulation(
-      data = data,
-      bins = bins,
-      dens_color = dens_color,
-      obs_stat = obs_stat,
-      obs_stat_color = obs_stat_color,
-      direction = direction,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill,
-      ...
-    )
-  } else if (method == "theoretical") {
-    infer_plot <- visualize_theoretical(
-      data = data,
-      dens_color = dens_color,
-      obs_stat = obs_stat,
-      obs_stat_color = obs_stat_color,
-      direction = direction,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill,
-      ...
-    )
-  } else if (method == "both") {
+  
+  if (method == "both") {
     if (!("stat" %in% names(data))) {
       stop_glue(
         '`generate()` and `calculate()` are both required to be done prior ',
         'to `visualize(method = "both")`'
       )
     }
-
+    
     if (
       ("replicate" %in% names(data)) && (length(unique(data$replicate)) < 100)
     ) {
       warning_glue(
-        "With only {length(unique(data$stat))} replicates, it may be ",
+        "With only {length(unique(data$replicate))} replicates, it may be ",
         "difficult to see the relationship between simulation and theory."
       )
     }
-
-    infer_plot <- visualize_both(
-      data = data,
-      bins = bins,
-      dens_color = dens_color,
-      obs_stat = obs_stat,
-      obs_stat_color = obs_stat_color,
-      direction = direction,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill,
-      ...
-    )
-  } else {
-    stop_glue(
-      'Provide `method` with one of three options: `"theoretical"`, `"both"`, ',
-      'or `"simulation"`. `"simulation"` is the default.'
+  }
+  
+  if (!is.null(obs_stat) && !is.null(endpoints)) {
+    warning_glue(
+      "Values for both `endpoints` and `obs_stat` were given when only one ",
+      "should be set. Ignoring `obs_stat` values."
     )
   }
-
-  if (!is.null(obs_stat)) { # && !is.null(direction)
-    infer_plot <- infer_plot +
-      geom_vline(xintercept = obs_stat, size = 2, color = obs_stat_color, ...)
+  
+  theory_type <- short_theory_type(data)
+  if (theory_type %in% c("F", "Chi-Square")) {
+    warn_right_tail_test(direction, theory_type)
   }
+  
+  TRUE
+}
 
+warn_depricated_args <- function(obs_stat, endpoints) {
+  if (!is.null(obs_stat)) {
+    warning_glue(
+      "`visualize()` shouldn't be used to plot p-value. Arguments `obs_stat`, ",
+      "`obs_stat_color`, `pvalue_fill`, and `direction` are deprecated. ",
+      "Use `add_p_value()` instead."
+    )
+  }
+  
   if (!is.null(endpoints)) {
-    if (!is.null(obs_stat)) {
-      warning_glue(
-        "Values for both `endpoints` and `obs_stat` were given when only one ",
-        "should be set. Ignoring `obs_stat` values."
-      )
-    }
-    infer_plot <- infer_plot +
-      geom_vline(
-        xintercept = endpoints, size = 2, color = endpoints_color, ...
-      )
+    warning_glue(
+      "`visualize()` shouldn't be used to plot confidence interval. Arguments ",
+      "`endpoints`, `endpoints_color`, and `ci_fill` are deprecated. ",
+      "Use `add_ci()` instead."
+    )
   }
-
-  infer_plot
+  
+  TRUE
 }
 
-theory_plot <- function(d_fun, q_fun, args_list, stat_name, dens_color) {
-  x_range <- do.call(q_fun, c(p = list(c(0.001, 0.999)), args_list))
-  
-  ggplot(data.frame(x = x_range)) +
-    stat_function(
-      mapping = aes(x), fun = d_fun, args = args_list, color = dens_color
-    ) +
-    ggtitle(glue_null("Theoretical {stat_name} Null Distribution")) +
-    xlab("") + ylab("")
+impute_enpoints <- function(endpoints) {
+  if (is.vector(endpoints) && (length(endpoints) != 2)) {
+    warning_glue(
+      "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector. ",
+      "Using the first two entries as the `endpoints`."
+    )
+    endpoints <- endpoints[1:2]
+  }
+  if (is.data.frame(endpoints)) {
+    endpoints <- unlist(endpoints)
+  }
+  
+  endpoints
 }
 
-both_plot <- function(data, d_fun, args_list, stat_name, stat_label, dens_color,
-                      obs_stat, direction, bins, pvalue_fill, endpoints,
-                      ci_fill, ...) {
-  infer_plot <- shade_density_check(
-    data = data,
-    obs_stat = obs_stat,
-    direction = direction,
-    bins = bins,
-    endpoints = endpoints,
-    pvalue_fill = pvalue_fill,
-    ci_fill = ci_fill
-  )
+impute_obs_stat <- function(obs_stat, direction, endpoints) {
+  obs_stat <- check_obs_stat(obs_stat)
   
-  infer_plot +
-    stat_function(
-      fun = d_fun, args = args_list, color = dens_color
-    ) +
-    ggtitle(glue_null(
-      "Simulation-Based and Theoretical {stat_name} Null Distributions"
-    )) +
-    xlab(stat_label) + ylab("")
+  if (
+    !is.null(direction) &&
+    (is.null(obs_stat) + is.null(endpoints) != 1)
+  ) {
+    stop_glue(
+      "Shading requires either `endpoints` values for a confidence interval ",
+      "or the observed statistic `obs_stat` to be provided."
+    )
+  }
+  
+  obs_stat
 }
 
-shade_density_check <- function(data,
-                                obs_stat,
-                                direction,
-                                bins,
-                                density = TRUE,
-                                pvalue_fill,
-                                endpoints,
-                                ci_fill,
-                                ...) {
-  if (is.null(direction) || is.null(obs_stat)) {
-    if (density) {
-      gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
+simulation_layer <- function(data, bins, ...) {
+  method <- get_viz_method(data)
+  
+  if (method == "theoretical") {
+    return(list())
+  }
+  
+  if (method == "simulation") {
+    if (length(unique(data$stat)) >= 10) {
+      res <- list(
         geom_histogram(
-          bins = bins, color = "white", mapping = aes(y = ..density..), ...
+          mapping = aes(x = stat), bins = bins, color = "white", ...
         )
-    } # else {
-      # Not sure if needed? Can't get tests to find it
-      # gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-      #  geom_histogram(bins = bins, color = "white", ...)
-    # }
-  }
-
-  if (xor(!is.null(obs_stat), !is.null(endpoints))) {
-    if (!is.null(direction)) {
-      if (density) {
-        gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-          geom_histogram(
-            bins = bins, color = "white", mapping = aes(y = ..density..), ...
-          )
-      } else {
-        gg_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-          geom_histogram(bins = bins, color = "white", ...)
-      }
-
-      if (direction %in% c("less", "left", "greater", "right")) {
-        gg_plot <- gg_plot +
-          geom_tail(direction, obs_stat, pvalue_fill)
-      }
-      
-      if (direction %in% c("two_sided", "both")) {
-        gg_plot <- gg_plot +
-          geom_both_tails(
-            border_1 = obs_stat,
-            border_2 = mirror_obs_stat(data$stat, obs_stat),
-            fill = pvalue_fill
-          )
-      }
-
-      if (direction == "between") {
-        gg_plot <- gg_plot +
-          geom_rect(
-            data = data.frame(endpoints[1]),
-            fill = ci_fill, alpha = 0.6,
-            aes(xmin = endpoints[1], xmax = endpoints[2], ymin = 0, ymax = Inf),
-            inherit.aes = FALSE,
-            ...
-          )
-      }
+      )
+    } else {
+      res <- list(geom_bar(mapping = aes(x = stat), ...))
     }
+  } else if (method == "both") {
+    res <- list(
+      geom_histogram(
+        mapping = aes(x = stat, y = ..density..), bins = bins,
+        color = "white", ...
+      )
+    )
   }
-  gg_plot
+  
+  res
 }
 
-visualize_simulation <- function(data, bins,
-                                 method = "simulation",
-                                 dens_color,
-                                 obs_stat,
-                                 obs_stat_color,
-                                 direction,
-                                 pvalue_fill,
-                                 endpoints,
-                                 ci_fill,
-                                 ...) {
-  if (is.null(direction)) {
-    if (length(unique(data$stat)) >= 10) {
-      infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-        geom_histogram(bins = bins, color = "white", ...)
-    } else {
-      infer_plot <- ggplot(data = data, mapping = aes(x = stat)) +
-        geom_bar(...) +
-        xlab("stat")
-    }
-  } else {
-    infer_plot <- shade_density_check(
-      data = data,
-      obs_stat = obs_stat,
-      direction = direction,
-      bins = bins,
-      density = FALSE,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill
-    )
+theoretical_layer <- function(data, dens_color, ...) {
+  method <- get_viz_method(data)
+  
+  if (method == "simulation") {
+    return(list())
   }
-  infer_plot
+  
+  warn_theoretical_layer(data)
+  
+  theory_type <- short_theory_type(data)
+  
+  switch(
+    theory_type,
+    t = theory_curve(
+      method, dt, qt, list(df = attr(data, "distr_param")), dens_color
+    ),
+    `F` = theory_curve(
+      method, df, qf,
+      list(
+        df1 = attr(data, "distr_param"), df2 = attr(data, "distr_param2")
+      ),
+      dens_color = dens_color
+    ),
+    z = theory_curve(method, dnorm, qnorm, list(), dens_color),
+    `Chi-Square` = theory_curve(
+      method, dchisq, qchisq, list(df = attr(data, "distr_param")), dens_color
+    )
+  )
 }
 
-visualize_theoretical <- function(data,
-                                  dens_color,
-                                  obs_stat,
-                                  obs_stat_color,
-                                  direction,
-                                  pvalue_fill,
-                                  endpoints,
-                                  ci_fill,
-                                  ...) {
+warn_theoretical_layer <- function(data) {
+  method <- get_viz_method(data)
+  
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. {{infer}} currently does not check these for you."
   )
-
+  
   if (
     !is_nuat(data, "stat") &&
     !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))
   ) {
-    warning_glue(
-      "Your `calculate`d statistic and the theoretical distribution are on ",
-      "different scales. Displaying only the theoretical distribution."
-    )
+    if (method == "theoretical") {
+      warning_glue(
+        "Your `calculate`d statistic and the theoretical distribution are on ",
+        "different scales. Displaying only the theoretical distribution."
+      )
+    } else if (method == "both") {
+      stop_glue(
+        "Your `calculate`d statistic and the theoretical distribution are on ",
+        "different scales. Use a standardized `stat` instead."
+      )
+    }
   }
-  
-  theory_type <- short_theory_type(data)
-
-  if (theory_type == "t") {
-    infer_plot <- theory_plot(
-      d_fun = dt, q_fun = qt,
-      args_list = list(df = attr(data, "distr_param")),
-      stat_name = "t",
-      dens_color = dens_color
-    )
-  } else if (theory_type == "F") {
-    warn_right_tail_test(direction, "F")
+}
 
-    infer_plot <- theory_plot(
-      d_fun = df, q_fun = qf,
-      args_list = list(
-        df1 = attr(data, "distr_param"), df2 = attr(data, "distr_param2")
-      ),
-      stat_name = "F",
-      dens_color = dens_color
+theory_curve <- function(method, d_fun, q_fun, args_list, dens_color) {
+  if (method == "theoretical") {
+    x_range <- do.call(q_fun, c(p = list(c(0.001, 0.999)), args_list))
+    
+    res <- list(
+      stat_function(
+        data = data.frame(x = x_range), mapping = aes(x),
+        fun = d_fun, args = args_list, color = dens_color
+      )
     )
-  } else if (theory_type == "z") {
-    infer_plot <- theory_plot(
-      d_fun = dnorm, q_fun = qnorm,
-      args_list = list(),
-      stat_name = "z",
-      dens_color = dens_color
+  } else if (method == "both") {
+    res <- list(
+      stat_function(
+        mapping = aes(x = stat), fun = d_fun, args = args_list,
+        color = dens_color
+      )
     )
-  } else if (theory_type == "Chi-Square") {
-    warn_right_tail_test(direction, "Chi-Square")
+  }
+  
+  res
+}
 
-    infer_plot <- theory_plot(
-      d_fun = dchisq, q_fun = qchisq,
-      args_list = list(df = attr(data, "distr_param")),
-      stat_name = "Chi-Square",
-      dens_color = dens_color
-    )
-  } # else {
-    # stop_glue(
-    #   '"{attr(data, "theory_type")}" is not implemented (possibly yet).'
-    # )
-  # }
+title_labels_layer <- function(data) {
+  method <- get_viz_method(data)
+  theory_type <- short_theory_type(data)
+  
+  title_string <- switch(
+    method,
+    simulation = "Simulation-Based Null Distribution",
+    theoretical = "Theoretical {theory_type} Null Distribution",
+    both = "Simulation-Based and Theoretical {theory_type} Null Distributions"
+  )
+  
+  x_lab <- switch(method, simulation = "stat", "{theory_type} stat")
+  y_lab <- switch(method, simulation = "count", "density")
+  
+  list(
+    ggtitle(glue_null(title_string)),
+    xlab(glue_null(x_lab)),
+    ylab(glue_null(y_lab))
+  )
+}
 
-  # Plot tails
-  if (!is.null(obs_stat) && !is.null(direction)) {
+#' Add information about p-value region(s)
+#' 
+#' `shade_p_value()` plots p-value region(s) on top of the [visualize()] output.
+#' It should be used as \\{ggplot2\\} layer function (see examples).
+#' `shade_pvalue()` is its alias.
+#' 
+#' @param obs_stat A numeric value or 1x1 data frame corresponding to what the
+#'   observed statistic is.
+#' @param direction A string specifying in which direction the shading should
+#'   occur. Options are `"less"`, `"greater"`, or `"two_sided"`. Can
+#'   also give `"left"`, `"right"`, or `"both"`. If `NULL` then no shading is
+#'   actually done.
+#' @param color A character or hex string specifying the color of the observed
+#'   statistic as a vertical line on the plot.
+#' @param fill A character or hex string specifying the color to shade the
+#'   p-value region. If `NULL` then no shading is actually done.
+#' @param ... Other arguments passed along to \\{ggplot2\\} functions.
+#'
+#' @return A list of \\{ggplot2\\} objects to be added to the `visualize()`
+#'   output.
+#'   
+#' @seealso [shade_confidence_interval()] to add information about confidence
+#'   interval.
+#'
+#' @examples
+#' viz_plot <- mtcars %>%
+#'   dplyr::mutate(am = factor(am)) %>%
+#'   specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
+#'   hypothesize(null = "independence") %>%
+#'   generate(reps = 100, type = "permute") %>%
+#'   calculate(stat = "t", order = c("1", "0")) %>%
+#'   visualize(method = "both")
+#' 
+#' viz_plot + shade_p_value(1.5, direction = "right")
+#' viz_plot + shade_p_value(1.5, direction = "both")
+#' viz_plot + shade_p_value(1.5, direction = NULL)
+#' 
+#' @name shade_p_value
+NULL
+
+#' @rdname shade_p_value
+#' @export
+shade_p_value <- function(obs_stat, direction,
+                          color = "red2", fill = "pink", ...) {
+  res <- list()
+  if (is.null(obs_stat)) {
+    return(res)
+  }
+  
+  # Add shading
+  if (!is.null(direction) && !is.null(fill)) {
     if (direction %in% c("less", "left", "greater", "right")) {
-      infer_plot <- infer_plot +
-        geom_tail(direction, obs_stat, pvalue_fill, ...)
-    }
-    # Assuming two-tailed shading will only happen with theoretical
-    # distributions centered at 0
-    if (direction %in% c("two_sided", "both")) {
-      infer_plot <- infer_plot +
-        geom_both_tails(obs_stat, -obs_stat, pvalue_fill, ...)
+      tail_data <- one_tail_data(obs_stat, direction)
+      
+      res <- c(res, list(geom_tail(tail_data, fill, ...)))
+    } else if (direction %in% c("two_sided", "both")) {
+      tail_data <- two_tail_data(obs_stat)
+      
+      res <- c(res, list(geom_tail(tail_data, fill, ...)))
+    } else {
+      warning_glue(
+        '`direction` should be one of `"less"`, `"left"`, `"greater"`, ",
+        "`"right"`, `"two_sided"`, `"both"`.'
+      )
     }
   }
-
-  # To implement: plotting of theoretical confidence interval values
-
-  infer_plot
-}
-
-visualize_both <- function(data, bins,
-                           dens_color,
-                           obs_stat,
-                           obs_stat_color,
-                           direction,
-                           pvalue_fill,
-                           endpoints,
-                           ci_fill,
-                           ...) {
-  warning_glue(
-    "Check to make sure the conditions have been met for the theoretical ",
-    "method. `infer` currently does not check these for you."
+  
+  # Add vertical line at `obs_stat`
+  c(
+    res, list(geom_vline(xintercept = obs_stat, size = 2, color = color, ...))
   )
+}
 
-  if (!(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))) {
-    stop_glue(
-      "Your `calculate`d statistic and the theoretical distribution are on ",
-      "different scales. Use a standardized `stat` instead."
-    )
+#' @rdname shade_p_value
+#' @export
+shade_pvalue <- shade_p_value
+
+#' Add information about confidence interval
+#' 
+#' `shade_confidence_interval()` plots confidence interval region on top of the
+#' [visualize()] output. It should be used as \\{ggplot2\\} layer function (see
+#' examples). `shade_ci()` is its alias.
+#' 
+#' @param endpoints A 2 element vector or a 1 x 2 data frame containing the
+#'   lower and upper values to be plotted. Most useful for visualizing
+#'   conference intervals.
+#' @param color A character or hex string specifying the color of the
+#'   end points as a vertical lines on the plot.
+#' @param fill A character or hex string specifying the color to shade the
+#'   confidence interval. If `NULL` then no shading is actually done.
+#' @param ... Other arguments passed along to \\{ggplot2\\} functions.
+#' @return A list of \\{ggplot2\\} objects to be added to the `visualize()`
+#'   output.
+#'   
+#' @seealso [shade_p_value()] to add information about p-value region.
+#'
+#' @examples
+#' viz_plot <- mtcars %>%
+#'   dplyr::mutate(am = factor(am)) %>%
+#'   specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
+#'   hypothesize(null = "independence") %>%
+#'   generate(reps = 100, type = "permute") %>%
+#'   calculate(stat = "t", order = c("1", "0")) %>%
+#'   visualize(method = "both")
+#' 
+#' viz_plot + shade_confidence_interval(c(-1.5, 1.5))
+#' viz_plot + shade_confidence_interval(c(-1.5, 1.5), fill = NULL)
+#' 
+#' @name shade_confidence_interval
+NULL
+
+#' @rdname shade_confidence_interval
+#' @export
+shade_confidence_interval <- function(endpoints, color = "mediumaquamarine",
+                                      fill = "turquoise", ...) {
+  res <- list()
+  if (is.null(endpoints)) {
+    return(res)
   }
-
-  theory_type <- short_theory_type(data)
   
-  if (theory_type == "t") {
-    infer_plot <- both_plot(
-      data = data,
-      d_fun = dt,
-      args_list = list(df = attr(data, "distr_param")),
-      stat_name = "t", stat_label = "tstat",
-      dens_color = dens_color,
-      bins = bins,
-      direction = direction,
-      obs_stat = obs_stat,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill
-    )
-  } else if (theory_type == "F") {
-    warn_right_tail_test(direction, "F")
-    
-    infer_plot <- both_plot(
-      data = data,
-      d_fun = df,
-      args_list = list(
-        df1 = attr(data, "distr_param"), df2 = attr(data, "distr_param2")
-      ),
-      stat_name = "F", stat_label = "Fstat",
-      dens_color = dens_color,
-      bins = bins,
-      direction = direction,
-      obs_stat = obs_stat,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill
-    )
-  } else if (theory_type == "z") {
-    infer_plot <- both_plot(
-      data = data,
-      d_fun = dnorm,
-      args_list = list(),
-      stat_name = "z", stat_label = "zstat",
-      dens_color = dens_color,
-      bins = bins,
-      direction = direction,
-      obs_stat = obs_stat,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill
-    )
-  } else if (theory_type == "Chi-Square") {
-    warn_right_tail_test(direction, "Chi-Square")
-    
-    infer_plot <- both_plot(
-      data = data,
-      d_fun = dchisq,
-      args_list = list(df = attr(data, "distr_param")),
-      stat_name = "Chi-Square", stat_label = "chisqstat",
-      dens_color = dens_color,
-      bins = bins,
-      direction = direction,
-      obs_stat = obs_stat,
-      pvalue_fill = pvalue_fill,
-      endpoints = endpoints,
-      ci_fill = ci_fill
+  if (!is.null(fill)) {
+    res <- c(
+      res, list(
+        geom_rect(
+          data = data.frame(endpoints[1]),
+          fill = fill, alpha = 0.6,
+          aes(xmin = endpoints[1], xmax = endpoints[2], ymin = 0, ymax = Inf),
+          inherit.aes = FALSE,
+          ...
+        )
+      )
     )
-  } # else {
-    # stop_glue('"{attr(data, "theory_type")}" is not implemented yet.')
-  # }
-
-  infer_plot
+  }
+  
+  c(
+    res, list(geom_vline(xintercept = endpoints, size = 2, color = color, ...))
+  )
 }
 
+#' @rdname shade_confidence_interval
+#' @export
+shade_ci <- shade_confidence_interval
+
 get_percentile <- function(vector, observation) {
   stats::ecdf(vector)(observation)
 }
@@ -542,17 +545,11 @@ warn_right_tail_test <- function(direction, stat_name) {
   TRUE
 }
 
-geom_tail <- function(dir, border, fill, ...) {
-  if (dir %in% c("less", "left")) {
-    x_range <- c(-Inf, border)
-  } else if (dir %in% c("greater", "right")) {
-    x_range <- c(border, Inf)
-  }
-  
+geom_tail <- function(tail_data, fill, ...) {
   list(
     geom_rect(
-      data = data.frame(border),
-      aes(xmin = x_range[1], xmax = x_range[2], ymin = 0, ymax = Inf),
+      data = tail_data,
+      aes(xmin = x_min, xmax = x_max, ymin = 0, ymax = Inf),
       fill = fill, alpha = 0.6,
       inherit.aes = FALSE,
       ...
@@ -560,16 +557,33 @@ geom_tail <- function(dir, border, fill, ...) {
   )
 }
 
-geom_both_tails <- function(border_1, border_2, fill, ...) {
-  left_border <- min(border_1, border_2)
-  right_border <- max(border_1, border_2)
-  
-  c(
-    geom_tail("left", left_border, fill, ...),
-    geom_tail("right", right_border, fill, ...)
-  )
+one_tail_data <- function(obs_stat, direction) {
+  if (direction %in% c("less", "left")) {
+    data.frame(x_min = -Inf, x_max = obs_stat)
+  } else if (direction %in% c("greater", "right")) {
+    data.frame(x_min = obs_stat, x_max = Inf)
+  }
 }
 
-#' @rdname visualize
-#' @export
-visualise <- visualize
+two_tail_data <- function(obs_stat) {
+  # Take advantage of {ggplot2} functionality to accept function as `data`
+  # This is needed to make possible existence of `shade_p_value()` in case of
+  # `direction = "both"`, as it depends on actual `data` but adding it as
+  # argument to `shade_p_value()` is very bad.
+  function(data) {
+    if (get_viz_method(data) == "theoretical") {
+      second_border <- -obs_stat
+    } else {
+      second_border <- mirror_obs_stat(data$stat, obs_stat)
+    }
+    
+    data.frame(
+      x_min = c(-Inf, max(obs_stat, second_border)),
+      x_max = c(min(obs_stat, second_border), Inf)
+    )
+  }
+}
+
+get_viz_method <- function(data) {
+  attr(data, "viz_method")
+}
diff --git a/man/shade_confidence_interval.Rd b/man/shade_confidence_interval.Rd
new file mode 100644
index 00000000..02377da6
--- /dev/null
+++ b/man/shade_confidence_interval.Rd
@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualize.R
+\name{shade_confidence_interval}
+\alias{shade_confidence_interval}
+\alias{shade_ci}
+\title{Add information about confidence interval}
+\usage{
+shade_confidence_interval(endpoints, color = "mediumaquamarine",
+  fill = "turquoise", ...)
+
+shade_ci(endpoints, color = "mediumaquamarine", fill = "turquoise",
+  ...)
+}
+\arguments{
+\item{endpoints}{A 2 element vector or a 1 x 2 data frame containing the
+lower and upper values to be plotted. Most useful for visualizing
+conference intervals.}
+
+\item{color}{A character or hex string specifying the color of the
+end points as a vertical lines on the plot.}
+
+\item{fill}{A character or hex string specifying the color to shade the
+confidence interval. If \code{NULL} then no shading is actually done.}
+
+\item{...}{Other arguments passed along to \{ggplot2\} functions.}
+}
+\value{
+A list of \{ggplot2\} objects to be added to the \code{visualize()}
+output.
+}
+\description{
+\code{shade_confidence_interval()} plots confidence interval region on top of the
+\code{\link[=visualize]{visualize()}} output. It should be used as \{ggplot2\} layer function (see
+examples). \code{shade_ci()} is its alias.
+}
+\examples{
+viz_plot <- mtcars \%>\%
+  dplyr::mutate(am = factor(am)) \%>\%
+  specify(mpg ~ am) \%>\% # alt: response = mpg, explanatory = am
+  hypothesize(null = "independence") \%>\%
+  generate(reps = 100, type = "permute") \%>\%
+  calculate(stat = "t", order = c("1", "0")) \%>\%
+  visualize(method = "both")
+
+viz_plot + shade_confidence_interval(c(-1.5, 1.5))
+viz_plot + shade_confidence_interval(c(-1.5, 1.5), fill = NULL)
+
+}
+\seealso{
+\code{\link[=shade_p_value]{shade_p_value()}} to add information about p-value region.
+}
diff --git a/man/shade_p_value.Rd b/man/shade_p_value.Rd
new file mode 100644
index 00000000..48cffef4
--- /dev/null
+++ b/man/shade_p_value.Rd
@@ -0,0 +1,55 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualize.R
+\name{shade_p_value}
+\alias{shade_p_value}
+\alias{shade_pvalue}
+\title{Add information about p-value region(s)}
+\usage{
+shade_p_value(obs_stat, direction, color = "red2", fill = "pink", ...)
+
+shade_pvalue(obs_stat, direction, color = "red2", fill = "pink", ...)
+}
+\arguments{
+\item{obs_stat}{A numeric value or 1x1 data frame corresponding to what the
+observed statistic is.}
+
+\item{direction}{A string specifying in which direction the shading should
+occur. Options are \code{"less"}, \code{"greater"}, or \code{"two_sided"}. Can
+also give \code{"left"}, \code{"right"}, or \code{"both"}. If \code{NULL} then no shading is
+actually done.}
+
+\item{color}{A character or hex string specifying the color of the observed
+statistic as a vertical line on the plot.}
+
+\item{fill}{A character or hex string specifying the color to shade the
+p-value region. If \code{NULL} then no shading is actually done.}
+
+\item{...}{Other arguments passed along to \{ggplot2\} functions.}
+}
+\value{
+A list of \{ggplot2\} objects to be added to the \code{visualize()}
+output.
+}
+\description{
+\code{shade_p_value()} plots p-value region(s) on top of the \code{\link[=visualize]{visualize()}} output.
+It should be used as \{ggplot2\} layer function (see examples).
+\code{shade_pvalue()} is its alias.
+}
+\examples{
+viz_plot <- mtcars \%>\%
+  dplyr::mutate(am = factor(am)) \%>\%
+  specify(mpg ~ am) \%>\% # alt: response = mpg, explanatory = am
+  hypothesize(null = "independence") \%>\%
+  generate(reps = 100, type = "permute") \%>\%
+  calculate(stat = "t", order = c("1", "0")) \%>\%
+  visualize(method = "both")
+
+viz_plot + shade_p_value(1.5, direction = "right")
+viz_plot + shade_p_value(1.5, direction = "both")
+viz_plot + shade_p_value(1.5, direction = NULL)
+
+}
+\seealso{
+\code{\link[=shade_confidence_interval]{shade_confidence_interval()}} to add information about confidence
+interval.
+}
diff --git a/man/visualize.Rd b/man/visualize.Rd
index 314096b3..56697ee4 100755
--- a/man/visualize.Rd
+++ b/man/visualize.Rd
@@ -28,31 +28,34 @@ visualise(data, bins = 15, method = "simulation",
 theoretical density curve.}
 
 \item{obs_stat}{A numeric value or 1x1 data frame corresponding to what the
-observed statistic is.}
+observed statistic is. \strong{Deprecated (see Details)}.}
 
 \item{obs_stat_color}{A character or hex string specifying the color of the
-observed statistic as a vertical line on the plot.}
+observed statistic as a vertical line on the plot. \strong{Deprecated (see
+Details)}.}
 
 \item{pvalue_fill}{A character or hex string specifying the color to shade
 the p-value. In previous versions of the package this was the \code{shade_color}
-argument.}
+argument. \strong{Deprecated (see Details)}.}
 
 \item{direction}{A string specifying in which direction the shading should
 occur. Options are \code{"less"}, \code{"greater"}, or \code{"two_sided"} for p-value. Can
 also give \code{"left"}, \code{"right"}, or \code{"both"} for p-value. For confidence
-intervals, use \code{"between"} and give the endpoint values in \code{endpoints}.}
+intervals, use \code{"between"} and give the endpoint values in \code{endpoints}.
+\strong{Deprecated (see Details)}.}
 
 \item{endpoints}{A 2 element vector or a 1 x 2 data frame containing the
 lower and upper values to be plotted. Most useful for visualizing
-conference intervals.}
+conference intervals. \strong{Deprecated (see Details)}.}
 
 \item{endpoints_color}{A character or hex string specifying the color of the
-observed statistic as a vertical line on the plot.}
+observed statistic as a vertical line on the plot. \strong{Deprecated (see
+Details)}.}
 
 \item{ci_fill}{A character or hex string specifying the color to shade the
-confidence interval.}
+confidence interval. \strong{Deprecated (see Details)}.}
 
-\item{...}{Other arguments passed along to ggplot2.}
+\item{...}{Other arguments passed along to \{ggplot2\} functions.}
 }
 \value{
 A ggplot object showing the simulation-based distribution as a
@@ -62,6 +65,15 @@ histogram or bar graph. Also used to show the theoretical curves.
 Visualize the distribution of the simulation-based inferential statistics or
 the theoretical distribution (or both!).
 }
+\details{
+In order to make visualization workflow more straightforward and
+explicit \code{visualize()} now only should be used to plot statistics directly.
+That is why arguments, not related to this task, are deprecated and will be
+removed in a future release of \{infer\}.
+
+To add information related to p-value use \code{\link[=shade_p_value]{shade_p_value()}}. To add
+information related to confidence interval use \code{\link[=shade_confidence_interval]{shade_confidence_interval()}}.
+}
 \examples{
 # Permutations to create a simulation-based null distribution for
 # one numerical response and one categorical predictor
@@ -95,3 +107,6 @@ mtcars \%>\%
   visualize(method = "both")
 
 }
+\seealso{
+\code{\link[=shade_p_value]{shade_p_value()}}, \code{\link[=shade_confidence_interval]{shade_confidence_interval()}}.
+}
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 59b46537..bbe039a2 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -42,7 +42,6 @@ obs_F <- anova(
     aov(formula = Sepal.Width ~ Species, data = iris_tbl)
   )$`F value`[1]
 
-
 test_that("visualize basic tests", {
   expect_silent(visualize(Sepal.Width_resamp))
 
@@ -51,13 +50,14 @@ test_that("visualize basic tests", {
   expect_silent(visualise(Sepal.Width_resamp))
   
   expect_error(Sepal.Width_resamp %>% visualize(bins = "yep"))
-  expect_silent(
+  expect_warning(
     iris_tbl %>%
       specify(Sepal.Length ~ Sepal.Width) %>%
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "permute") %>%
       calculate(stat = "slope") %>%
-      visualize(obs_stat = obs_slope, direction = "right")
+      visualize(obs_stat = obs_slope, direction = "right"),
+    "deprecated"
   )
 
   # obs_stat not specified
@@ -70,13 +70,14 @@ test_that("visualize basic tests", {
       visualize(direction = "both")
   )
 
-  expect_silent(
+  expect_warning(
     iris_tbl %>%
       specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
       hypothesize(null = "independence") %>%
       generate(reps = 100, type = "permute") %>%
       calculate(stat = "diff in props", order = c(">5", "<=5")) %>%
-      visualize(direction = "both", obs_stat = obs_diff)
+      visualize(direction = "both", obs_stat = obs_diff),
+    "deprecated"
   )
 
   expect_warning(
@@ -239,13 +240,14 @@ test_that("visualize basic tests", {
       visualize(method = "theoretical")
   )
 
-  expect_silent(
+  expect_warning(
     iris_tbl %>%
       specify(Petal.Width ~ Sepal.Width.Group) %>%
       hypothesize(null = "independence") %>%
       generate(reps = 10, type = "permute") %>%
       calculate(stat = "diff in means", order = c("large", "small")) %>%
-      visualize(direction = "both",obs_stat = obs_diff_mean)
+      visualize(direction = "both",obs_stat = obs_diff_mean),
+    "deprecated"
   )
 
   # Produces warning first for not checking conditions but would also error
@@ -284,13 +286,14 @@ test_that("visualize basic tests", {
       )
   )
 
-  expect_silent(
+  expect_warning(
     iris_tbl %>%
       specify(Petal.Width ~ NULL) %>%
       hypothesize(null = "point", mu = 1.3) %>%
       generate(reps = 100, type = "bootstrap") %>%
       calculate(stat = "mean") %>%
-      visualize(direction = "left", obs_stat = mean(iris$Petal.Width))
+      visualize(direction = "left", obs_stat = mean(iris$Petal.Width)),
+    "deprecated"
   )
 })
 
@@ -302,13 +305,14 @@ test_that("obs_stat as a data.frame works", {
   mean_petal_width <- iris_tbl %>%
     specify(Petal.Width ~ NULL) %>%
     calculate(stat = "mean")
-  expect_silent(
+  expect_warning(
     iris_tbl %>%
       specify(Petal.Width ~ NULL) %>%
       hypothesize(null = "point", mu = 4) %>%
       generate(reps = 100, type = "bootstrap") %>%
       calculate(stat = "mean") %>%
-      visualize(obs_stat = mean_petal_width)
+      visualize(obs_stat = mean_petal_width),
+    "deprecated"
   )
   mean_df_test <- data.frame(x = c(4.1, 1), y = c(1, 2))
   expect_warning(
@@ -396,9 +400,61 @@ test_that("confidence interval plots are working", {
 
   expect_warning(iris_boot %>% visualize(endpoints = vec_error))
 
-  expect_silent(
-    iris_boot %>% visualize(endpoints = perc_ci, direction = "between")
+  expect_warning(
+    iris_boot %>% visualize(endpoints = perc_ci, direction = "between"),
+    "deprecated"
   )
 
   expect_warning(iris_boot %>% visualize(obs_stat = 3, endpoints = perc_ci))
 })
+
+iris_permute <- iris_tbl %>%
+  specify(Sepal.Width.Group ~ Sepal.Length.Group, success = "large") %>%
+  hypothesize(null = "independence") %>%
+  generate(reps = 100, type = "permute") %>%
+  calculate(stat = "z", order = c(">5", "<=5"))
+iris_viz_sim <- iris_permute %>% visualize(method = "simulation")
+# Warnings are about checking conditions for the theoretical method.
+iris_viz_theor <- suppressWarnings(
+  iris_permute %>% visualize(method = "theoretical")
+)
+iris_viz_both <- suppressWarnings(
+  iris_permute %>% visualize(method = "both")
+)
+
+test_that("shade_p_value works", {
+  expect_silent_pval <- function(viz_obj) {
+    for (dir in c("right", "left", "both")) {
+      expect_silent(viz_obj + shade_p_value(1, dir))
+      expect_silent(viz_obj + shade_p_value(NULL, dir))
+    }
+    
+    expect_silent(viz_obj + shade_p_value(1, NULL))
+    
+    expect_warning(viz_obj + shade_p_value(1, "aaa"), "direction")
+  }
+  
+  expect_silent_pval(iris_viz_sim)
+  expect_silent_pval(iris_viz_theor)
+  expect_silent_pval(iris_viz_both)
+})
+
+test_that("shade_confidence_interval works", {
+  expect_silent_ci <- function(viz_obj) {
+    expect_silent(viz_obj + shade_confidence_interval(c(-1, 1)))
+    expect_silent(viz_obj + shade_confidence_interval(NULL))
+    expect_silent(viz_obj + shade_confidence_interval(c(-1, 1), fill = NULL))
+  }
+
+  expect_silent_ci(iris_viz_sim)
+  expect_silent_ci(iris_viz_theor)
+  expect_silent_ci(iris_viz_both)
+})
+
+test_that("two_tail_data works", {
+  attr(iris_permute, "viz_method") <- "both"
+  expect_equal(colnames(two_tail_data(1)(iris_permute)), c("x_min", "x_max"))
+  
+  attr(iris_permute, "viz_method") <- "theoretical"
+  expect_equal(colnames(two_tail_data(1)(iris_permute)), c("x_min", "x_max"))
+})

From c73d3d01f5b88f8e21a9eadf97a14a6f0afa8661 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Fri, 14 Sep 2018 16:42:02 +0300
Subject: [PATCH 54/78] Fix messages for `visualize()` argument deprecation.

---
 R/visualize.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 91983036..4156a419 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -193,7 +193,7 @@ warn_depricated_args <- function(obs_stat, endpoints) {
     warning_glue(
       "`visualize()` shouldn't be used to plot p-value. Arguments `obs_stat`, ",
       "`obs_stat_color`, `pvalue_fill`, and `direction` are deprecated. ",
-      "Use `add_p_value()` instead."
+      "Use `shade_p_value()` instead."
     )
   }
   
@@ -201,7 +201,7 @@ warn_depricated_args <- function(obs_stat, endpoints) {
     warning_glue(
       "`visualize()` shouldn't be used to plot confidence interval. Arguments ",
       "`endpoints`, `endpoints_color`, and `ci_fill` are deprecated. ",
-      "Use `add_ci()` instead."
+      "Use `shade_confidence_interval()` instead."
     )
   }
   

From 6e475d3738744d856b1aaeea8c4c10c35e5a87ea Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Fri, 14 Sep 2018 17:54:34 +0300
Subject: [PATCH 55/78] Update `shade_*()` functions to properly check their
 arguments.

---
 DESCRIPTION                     |  3 ++-
 R/utils.R                       |  5 +++++
 R/visualize.R                   | 37 +++++++++++++++++++++++++++++++--
 tests/testthat/test-visualize.R | 23 ++++++++++++++++++++
 4 files changed, 65 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index c3301693..ad8812c4 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -27,7 +27,8 @@ Imports:
     rlang (>= 0.2.0),
     ggplot2,
     magrittr,
-    glue (>= 1.3.0)
+    glue (>= 1.3.0),
+    grDevices
 Depends:
     R (>= 3.1.2)
 Suggests: 
diff --git a/R/utils.R b/R/utils.R
index 9c7d0e96..ee1411e9 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -50,6 +50,11 @@ has_response <- function(x) {
   !is_nuat(x, "response")
 }
 
+is_color_string <- function(x) {
+  rlang::is_string(x) &&
+    tryCatch(is.matrix(grDevices::col2rgb(x)), error = function(e) {FALSE})
+}
+
 stop_glue <- function(..., .sep = "", .envir = parent.frame(),
                       call. = FALSE, .domain = NULL) {
   stop(
diff --git a/R/visualize.R b/R/visualize.R
index 4156a419..b556aaff 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -98,7 +98,7 @@ visualize <- function(data, bins = 15, method = "simulation",
     pvalue_fill, direction, endpoints, endpoints_color, ci_fill
   )
   warn_depricated_args(obs_stat, endpoints)
-  endpoints <- impute_enpoints(endpoints)
+  endpoints <- impute_endpoints(endpoints)
   obs_stat <- impute_obs_stat(obs_stat, direction, endpoints)
   
   # Add `method` to `data` attributes to enable later possibility of
@@ -208,7 +208,7 @@ warn_depricated_args <- function(obs_stat, endpoints) {
   TRUE
 }
 
-impute_enpoints <- function(endpoints) {
+impute_endpoints <- function(endpoints) {
   if (is.vector(endpoints) && (length(endpoints) != 2)) {
     warning_glue(
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector. ",
@@ -216,7 +216,14 @@ impute_enpoints <- function(endpoints) {
     )
     endpoints <- endpoints[1:2]
   }
+  
   if (is.data.frame(endpoints)) {
+    if ((nrow(endpoints) != 1) || (ncol(endpoints) != 2)) {
+      stop_glue(
+        "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
+      )
+    }
+    
     endpoints <- unlist(endpoints)
   }
   
@@ -411,6 +418,9 @@ NULL
 #' @export
 shade_p_value <- function(obs_stat, direction,
                           color = "red2", fill = "pink", ...) {
+  obs_stat <- check_obs_stat(obs_stat)
+  check_shade_p_value_args(obs_stat, direction, color, fill)
+  
   res <- list()
   if (is.null(obs_stat)) {
     return(res)
@@ -444,6 +454,19 @@ shade_p_value <- function(obs_stat, direction,
 #' @export
 shade_pvalue <- shade_p_value
 
+check_shade_p_value_args <- function(obs_stat, direction, color, fill) {
+  if (!is.null(obs_stat)) {
+    check_type(obs_stat, is.numeric)
+  }
+  if (!is.null(direction)) {
+    check_type(direction, is.character)
+  }
+  check_type(color, is_color_string, "color string")
+  check_type(fill, is_color_string, "color string")
+  
+  TRUE
+}
+
 #' Add information about confidence interval
 #' 
 #' `shade_confidence_interval()` plots confidence interval region on top of the
@@ -482,6 +505,9 @@ NULL
 #' @export
 shade_confidence_interval <- function(endpoints, color = "mediumaquamarine",
                                       fill = "turquoise", ...) {
+  endpoints <- impute_endpoints(endpoints)
+  check_shade_confidence_interval_args(color, fill)
+  
   res <- list()
   if (is.null(endpoints)) {
     return(res)
@@ -510,6 +536,13 @@ shade_confidence_interval <- function(endpoints, color = "mediumaquamarine",
 #' @export
 shade_ci <- shade_confidence_interval
 
+check_shade_confidence_interval_args <- function(color, fill) {
+  check_type(color, is_color_string, "color string")
+  if (!is.null(fill)) {
+    check_type(fill, is_color_string, "color string")
+  }
+}
+
 get_percentile <- function(vector, observation) {
   stats::ecdf(vector)(observation)
 }
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index bbe039a2..f3a94e48 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -439,6 +439,13 @@ test_that("shade_p_value works", {
   expect_silent_pval(iris_viz_both)
 })
 
+test_that("shade_p_value throws errors", {
+  expect_error(iris_viz_sim + shade_p_value("a", "right"), "numeric")
+  expect_error(iris_viz_sim + shade_p_value(1, 1), "character")
+  expect_error(iris_viz_sim + shade_p_value(1, "right", color = "x"), "color")
+  expect_error(iris_viz_sim + shade_p_value(1, "right", fill = "x"), "color")
+})
+
 test_that("shade_confidence_interval works", {
   expect_silent_ci <- function(viz_obj) {
     expect_silent(viz_obj + shade_confidence_interval(c(-1, 1)))
@@ -451,6 +458,22 @@ test_that("shade_confidence_interval works", {
   expect_silent_ci(iris_viz_both)
 })
 
+test_that("shade_confidence_interval throws errors and warnings", {
+  expect_warning(iris_viz_sim + shade_confidence_interval(c(1, 2, 3)), "2")
+  expect_error(
+    iris_viz_sim + shade_confidence_interval(data.frame(x = 1)),
+    "1 x 2"
+  )
+  expect_error(
+    iris_viz_sim + shade_confidence_interval(c(-1, 1), color = "x"),
+    "color"
+  )
+  expect_error(
+    iris_viz_sim + shade_confidence_interval(c(-1, 1), fill = "x"),
+    "color"
+  )
+})
+
 test_that("two_tail_data works", {
   attr(iris_permute, "viz_method") <- "both"
   expect_equal(colnames(two_tail_data(1)(iris_permute)), c("x_min", "x_max"))

From 1d74bff028778a3567036f9a950d380b7bbd68a8 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Fri, 14 Sep 2018 19:46:07 +0300
Subject: [PATCH 56/78] Update vignettes to use `shade_*()` visualization
 functions.

---
 inst/doc/chisq_test.R                |  13 +-
 inst/doc/chisq_test.Rmd              |  13 +-
 inst/doc/chisq_test.html             |  30 +--
 inst/doc/flights_examples.html       |  40 ++--
 inst/doc/observed_stat_examples.R    | 125 +++++++++----
 inst/doc/observed_stat_examples.Rmd  | 125 +++++++++----
 inst/doc/observed_stat_examples.html | 263 ++++++++++++++-------------
 inst/doc/two_sample_t.R              |  13 +-
 inst/doc/two_sample_t.Rmd            |  13 +-
 inst/doc/two_sample_t.html           |  22 ++-
 vignettes/chisq_test.Rmd             |  13 +-
 vignettes/observed_stat_examples.Rmd | 125 +++++++++----
 vignettes/two_sample_t.Rmd           |  13 +-
 13 files changed, 507 insertions(+), 301 deletions(-)

diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
index 7d0faec3..e93599f9 100644
--- a/inst/doc/chisq_test.R
+++ b/inst/doc/chisq_test.R
@@ -42,7 +42,9 @@ chisq_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
+chisq_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
 chisq_null_distn %>% 
@@ -54,7 +56,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq") %>%
-  visualize(method = "theoretical", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ----eval=FALSE----------------------------------------------------------
 #  fli_small %>%
@@ -62,12 +65,14 @@ fli_small %>%
 #    hypothesize(null = "independence") %>%
 #    generate(reps = 1000, type = "permute") %>%
 #    calculate(stat = "Chisq") %>%
-#    visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+#    visualize(method = "both") +
+#      shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ----echo=FALSE----------------------------------------------------------
 # To use same distribution calculated above
 chisq_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ------------------------------------------------------------------------
 fli_small %>% 
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
index d56b5201..0f7ea2f0 100644
--- a/inst/doc/chisq_test.Rmd
+++ b/inst/doc/chisq_test.Rmd
@@ -92,7 +92,9 @@ chisq_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
+chisq_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -111,7 +113,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq") %>%
-  visualize(method = "theoretical", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
@@ -122,13 +125,15 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq") %>% 
-  visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ```{r echo=FALSE}
 # To use same distribution calculated above
 chisq_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
index 25831c30..0f324cff 100644
--- a/inst/doc/chisq_test.html
+++ b/inst/doc/chisq_test.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-09-03" />
+<meta name="date" content="2018-09-14" />
 
 <title>Chi-squared test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Chi-squared test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-03</em></h4>
+<h4 class="date"><em>2018-09-14</em></h4>
 
 
 
@@ -126,7 +126,7 @@ <h2>Calculate observed statistic</h2>
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">0.571898</td>
+<td align="right">0.5719</td>
 </tr>
 </tbody>
 </table>
@@ -146,7 +146,7 @@ <h2>Calculate observed statistic</h2>
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">0.571898</td>
+<td align="right">0.5719</td>
 </tr>
 </tbody>
 </table>
@@ -165,7 +165,7 @@ <h2>Calculate observed statistic</h2>
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">0.571898</td>
+<td align="right">0.5719</td>
 </tr>
 </tbody>
 </table>
@@ -179,8 +179,10 @@ <h2>Randomization approach to <span class="math inline">\(\chi^2\)</span>-statis
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-chisq_null_distn <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+chisq_null_distn <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
@@ -208,10 +210,11 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>, <span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" 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+srr7ySn5+vvXFaWlp0dLS9vb2vr++ECRMePHigWRUREaFTs+HacnNze/fuLZFIPvnkE7beFebbtm3r06cPN0d4wYIF3CXZLMueOnUqKirK2tra399/7ty5crn8zTfftLW1/eGHH1iDl15ziouLp06d2qNHD6lU6uvr++KLL54+fbrp7WP4MniWZUtKSubMmdOpUyeJROLo6BgaGvrBBx+Ul5drNlCr1atXrw4JCbG1tY2Ojj58+HBMTIzmMvhHjx698cYbzs7Obm5uY8aMKS4u7ty5s+YybMPH/rTtr0NzGbw2lUrFnQLTXAZvOMKnugxeg6IoT0/P6dOnFxcXa2+m3dq5ubkJCQmurq4SiSQgIODDDz9UKBSaVdodqX476FwGv2/fvsWLFwcEBDg6Og4ePDg3N1ezpeGjM9xjDXw0+vXrp/kVc5577jnNZfwAAK3I/8beAYwUGxtrY2Pz+++/mzsQAACARrTNOUAAAAAABiABAgAAAIuDBAgAAAAsDuYAAQAAgMXBCBAAAABYHCRAAAAAYHGQAAEAAIDFQQIEAAAAFgcJEAAAAFgcJEAAAABgcdrmw1Cbh/rGjbq0NO0SQWws06mTueIBAACAJmqhCVBpaSl/lTs5OSkUCrlcbmQ91OHD9JQp2iWiZclMwkgjq23thEKhUqnE/aV4IpFIlEqlUqk0dyBtk0AgUKlU6L08EYvFKpUKvZcn6L0GSEI61y/EKTAAAACwOEiAAAAAwOIgAQIAAACLgwQIAAAALI7pJ0FnZ2dv2bJFoVCEhoZOnjxZJBJpr50zZ87169cpiiKEDBo0aNKkSSYPAAAAAMAwEydAcrl83bp1S5cu9fb2Tk5OTk1NTUhI0KxlWbaoqOinn37SyYoAAAAAmpOJT4GdPXvWz8/Pz8+PYZghQ4YcOXJEe21JSQkhZM2aNZMnT16/fn1lZaVp9w4AAADQFCYeASopKZHJZNyyq6urzu18Hj16FBAQMHHiRAcHh40bN27atCkpKYlb9fDhw/fee49bHjhw4NChQ00bmDaKomiatre3N7IelbW14q8lAgEjtPjBLYqihEKhuaNoyxiGoWnM3uMF98fB3FG0WRRFoffyB723IWq1Wm+5iRtL5xZMOnv19/dfvHixTCYTiUSvvvpqVlaWWW7ZlJKSEhUVhbtFAQAAWCwTjwA5OztfuHCBWy4tLXV2dtZeW1hYyLJshw4dCCECgYBhGM0qR0fHjRs3al7yeifobt26zZ8//6effhoyZIgx9VCPH+vkj0qliq2tNabONgB3guaVRCLBvXT5g3vp8gp3guYVeq8BenMdE48AhYWF5efnFxUVsSyblpYWGRnJld++fbumpqaysjI5ObmsrIxl2Z07d/bt25e7HKyZDRo0qF+/fikpKQ0NiwEAAEDbZuIESCqVJiUlpaSkJCYmWllZDR8+nCtPTEwsKCgICQl58cUX582bN2XKlEePHk2YMMG0e2+6efPm5eXl7dmzx1wBAAAAgBlRLXO4rBkehhofH19WVnbo0KFnnjVG7dlDjxunXYKHoRKcAuMZHobKK5xE4BVOgfEKvdcAPAz1L95+++3Lly/v2rXL3IEAAABAc7PcBCgqKqpv376rVq3CTCAAAABLY7kJECFkwYIFV65c2bFjh7kDAQAAgGZl0QlQREREZGTkihUrcE4aAADAolh0AkQImT9/fmFhIQaBAAAALIqlJ0C9evWKiYlZtWoVBoEAAAAsh6UnQISQd999t7Cw8D//+Y+5AwEAAIBmggSIhIeHx8XFrVmzpq6uztyxAAAAQHNAAkQIIfPmzbtx48b3339v7kAAAACgOSABIoSQ0NDQ4cOHr1y5sqqqytyxAAAAAO+QAP3X4sWLKyoqvvjiC3MHAgAAALxDAvRf3t7er7322oYNG0pKSswdCwAAAPALCdD/vPPOOzRNr1271tyBAAAAAL8E5g5AP4ZheK2foqj6u5DJZG+99dbatWunTJnSoUOHxmup9xh5iiIURZkqyNYLjcA3tDBPKIpC2/KHesLcgbRNaNun1UITIIlEwl/lFEUJBAK9u5gzZ87XX3+9YsWKb7/9ttF61CJR7V9LaJrmO3Vr+bjkkmVZcwfSZtH1Mm8wFfRevun95xNMAr23IQ21SQtNgB4/fsxf5RKJpK6uTi6X119FUVRSUtK8efMmTpzYs2dPw/VQ1dU6X0QqlZpY/B2lhUKhUqnEh5AnAoFArVbjxuU8EQgEKpUKvZcnDMOg9/IHvdcAob5C/Cup6/XXX+/YseMHH3xg7kAAAACAL0iAdAkEgnnz5h07diwjI8PcsQAAAAAvkADpMXz48PDw8CVLlqjVanPHAgAAAKaHBEgPiqIWLlyYl5f373//29yxAAAAgOkhAdKvX79+L7zwwocffsjrdGwAAAAwCyRADVq+fPnDhw/Xr19v7kAAAADAxJAANcjHx2fy5MkbN24sLCw0dywAAABgSkiADJkzZ067du2WLl1q7kAAAADAlJAAGWJjY7Nw4cI9e/YcPHjQ3LEAAACAySABasTf/va30NDQRYsW1dXVmTsWAAAAMA0kQI2gaXr58uVXr1795ptvzB0LAAAAmAYSoMaFh4e//PLLK1asePjwobljAQAAABNAAtQk3CmwFStWmDsQAAAAMAHTJ0DZ2dnTp09/4403NmzYUFtbq3ebM2fOTJo0yeS75o+7u/vMmTO/+eabS5cumTsWAAAAMJaJEyC5XL5u3bo5c+Zs2rSpvLw8NTW1/jbFxcWfffYZy7Km3TXf3nrrreeee+69994zdyAAAABgLBMnQGfPnvXz8/Pz82MYZsiQIUeOHNHZoK6ubvXq1a+++qpp99sMxGLxBx98cPTo0W3btpk7FgAAADCKwLTVlZSUyGQybtnV1bW0tFRng82bN0dFRQUEBOiUV1dXHz16lFv28fFxdXU1bWA6GIYRi8VP+66RI0f+/PPPixYtGjx4sJOTEysUqv66AU3TDMOYKshWiqIohmFa3QhfK8K1sLmjaJtomiaEoPfyhKIo/JHkD3pvQxpqExMnQDq7UavV2i8zMzMrKiqGDRt2584dnTeWl5fPmzePWx43btyMGTNMG5gOsVj8DAkQIeTzzz8PDg5OTk7+8ssv66ysqv66lmEYoVBokghbNe5zCDxhGAZfIfxB7+UVTdNoYf6gbfXSSUU0TJwAOTs7X7hwgVsuLS11dnbWXnv27Nm8vLxJkyYplcry8vI333xz1apV7dq1I4TIZLKMjAxuM7FY/ODBA9MGps3R0VGhUCgUimd4r7W19dtvv7106dKEhITelZXUX9fW1dWpq6tNEmTrJRQKVSpVQx0OjCSRSJRKpVKpNHcgbRN6L6/EYrFKpULv5Ql6rwESfYUmToDCwsK+/PLLoqIiNze3tLS0yMhIrvz27dsuLi6zZ8/WvFyyZMnmzZs1b6Rp2s7OTvOysrLStIHV98zjhJMnT/7ll1/mzJlz6J13MNqjF8ZgoZViWRa9F1op9N6nZeLhMqlUmpSUlJKSkpiYaGVlNXz4cK48MTGxoKDAtPsyF4FAsGbNmvz8/L1795o7FgAAAHgWVMtMGOvPnjYhJycnhUIhl8uNqeSdd94p+/77f/31AWGiZclMwkjjomv1hEKhUqlsmf2qDcApMF4JBAKVSoXeyxOcAuMVeq8BkpDO9QsxYeoZLVq0yNra2txRAAAAwLNAAvSMbG1tW+PdjAAAAIAgATJGRESEuUMAAACAZ4EECAAAACwOEiAAAACwOEiAAAAAwOIgAQIAAACLgwTIlMrLy8wdAgAAADQOCZApFRQU1v311ogAAADQAiEBMqXautozZ86YOwoAAABoBBIgU/Ly8rp69erdu3fNHQgAAAAYggTIlDw8PGQy2YkTJ3AiDAAAoCVDAmRKFKH69u2rUChwIgwAAKAlQwJkYnZ2dj169Lh69ert27fNHQsAAADoJzB3APrZ29vzVzlFUWKxWCgUGlmPytpa8dcSgYARikTdunUrKio6duzYSy+9JJVKjdxLq0NRlPFtCwYwDEPT+NeFFxRFoW35Q1EUei9/0Hsbolar9Za30ATo0aNH/FXu5ORUU1Mjl8uNrId6/FinrymVKra2lhDSp0+fXbt2ZWZmxsfHG7mXVkcoFCqVSpZlzR1I2ySRSFQqlVKpNHcgbZNAIFCpVOi9PBGLxei9/EHvNUBvroNskRdSqbRPnz537969fPmyuWMBAAAAXUiA+OLl5eXv73/69OmyMtweGgAAoGVBAsSj8PBwW1vbI0eOqFQqc8cCAAAA/4MEiEcCgSAqKqqiogJXxQMAALQoSID45ejo2L1798uXL+OqeAAAgJYDCRDvgoOD3d3djx07Vl1dbe5YAAAAgBAkQM2Aoqh+/fqxLHv48GFcoAgAANASIAFqDlKpNDo6+v79++fOnTN3LAAAAIAEqLm4u7t37dr14sWLt27dMncsAAAAlg4JUPPp1q2bh4fHH3/8UVVVZe5YAAAALBoSoOZDUVRkZKRAIDh8+HBDjyYBAACAZoAEqFlJJJKYmJiHDx+ePn3a3LEAAABYLiRAzc3FxSU0NPTy5cuFhYXmjgUAAMBCIQEyg6CgIC8vrxMnTpSXl5s7FgAAAEuk9xHx/8OyLEVRT1Vjdnb2li1bFApFaGjo5MmTRSKRdm3ffffdoUOHlEqlr6/vzJkz27Vr9yxRt359+/bdvXv34cOHBw8eLBQKzR0OAACAZWlkBKhLly4pKSlNv3JbLpevW7duzpw5mzZtKi8vT01N1V577ty5U6dObdiw4V//+pe9vf3PP//8jFG3fiKRKC4urqqq6siRI7g7IgAAQDNrJAH629/+9vXXX3t7e/fv3/+rr76qqKgwvP3Zs2f9/Pz8/PwYhhkyZMiRI0e017q7u8+ePdvKyoplWYlEYmtra2z4rZmDg0NUVNSdO3dwd0QAAIBm1sgpsMWLFy9atOjs2bNbt25dtGjRW2+9NWLEiNdff33AgAECgZ73lpSUyGQybtnV1bW0tFR7rZubGyHk0KFDH3/8sYuLy7p16zSriouLx4wZwy2PGTNm0qRJxhyVYRRFWVlZWVlZGVlPna3t47+WCIVCkUTS9Br8/f25Z8W7uLh07NjRyHhaDoZhzB1CWyYQCPR++sAk0La8Qu/lFdpWL5VKpbe88caiKCo0NDQ0NPTdd9+dO3fut99++9NPP8lksqlTpy5YsEAsFmtvrHM2R+/dbmJiYsLDw//zn/98/PHH8+fP5wqtra3HjRvHLXfp0kUulzflqJ6NVCqtq6urq6szsh51TY1uiVqlVCqfqpKQkJCysrJDhw5ZW1u7uLgYGVJLQNM07nLEH4FAoFar0cI8Qe/lFXovr9B7G8KyrN5/yhtPgO7du7djx45ffvklMzOzffv2c+fOHTly5JUrV5KTky9evLh9+3btjZ2dnS9cuMAtl5aWOjs7a6/NyMigaTo2NpZ7NtbSpUs1q7QTIO69T3FwT0kqlSqVSoVCYWQ9VG2tzhlElUpNnjIBIoT06dPn0aNH6enpQ4cOlUqlRkZldkKhUKVSYWITT7ivkKfNs6GJBAIBei9/GIZB7+UPeq8Bei81amQOUHR0tIeHx4oVK3r06HH8+PHCwsJVq1b17dt3woQJn3zyycGDB3W2DwsLy8/PLyoqYlk2LS0tMjKSK799+3ZNTY1EItm2bVt1dTXLskePHu3cubMJDqv1YxgmNjaWZdlDhw4hfwcAAGgGjYwA9e7de82aNT179qx/MXzv3r115jgTQqRSaVJSUkpKilKpDAwMHD58OFeemJj40Ucf9enT5+bNm3Pnzq2trfX19Z02bZqpDqO1s7a2jouL27dv37FjxzRZIwAAAPCEMjxcFh8fv2/fPu05rffu3Zs2bdqvv/7Ka1i8ngJzcnJSKBTGTzOi9uyhtU7bEUJEy5KZhJHPXGF+fv7x48dDQ0O7dOliZGxmJBQKlUolhmF5IpFIlEolTiLwBCcReCUWi1Wqp54oCU2E3muAJETPGSf9I2X2NnYAACAASURBVEBHjx49evQoIeTAgQMpKSnaCdC1a9fqD/yASXAXhWVnZ1tbW/v6+po7HAAAgDZLfwJ07dq1vXv3cstpaWna579oml6zZk1zhGaRwsLCFArFH3/8IZFI3N3dzR0OAABA26Q/ARo/fvz48eMJIT179jxw4ABuLdCc+vbtK5fLMzMzBw0aZLGPCgEAAOCV/szm9OnTMpnMy8vriy++0Huf4p49e/IcmOWiaTomJmbv3r0HDhwYMmRIG7gwHgAAoKXRnwCFh4fPnDlz/fr14eHhejfANCteicXi559/PjU1NT09fdCgQdoPlAUAAADj6U+AFAoFN/HZ+Nslw7OxsbHp37//vn37MjMz4+PjabqROzYBAABA0+n/WpVIJEKhkBAiEAhOnDhx+/ZtgUCwZ8+eV1555aOPPsLwT/NwcnKKjo4uLi4+fvy4uWMBAABoUxoZV1i5cmVUVNSlS5cqKirGjh0rEAj++c9/Llq0qHmCA09Pz4iIiIKCglOnTpk7FgAAgLajkQTo008/Xb169ZAhQ7777ruuXbtu27Zt1apVP/74Y/MEB4QQf3//Xr165eXlnT9/3tyxAAAAtBGNXN9eUlISHR1NUVRGRsaQIUMIIQEBAffv32+W2OC/AgICqqurz58/LxAIgoODzR0OAABAq9dIAuTv779t2zYHB4fU1NSVK1dyjzj19PRsnuBAo3v37kql8syZM0KhsFOnTuYOBwAAoHVr5BTYkiVL1q5d26lTpwEDBnTo0GHhwoXz5s2bOXNm8wQH2nr27NmxY8eTJ0/euHHD3LEAAAC0bo2MAI0aNSo/P7+wsLBfv36EkPj4+EGDBkVHRzdLbKCrT58+SqXy6NGjAoEA43AAAADPrPFnXLi5ualUqtu3bxNCvLy8CCHXrl3r2LEjr2FJJBJe6xcIBMbvghWJdB5qTNO09oNj+RATE5Oenn748OH4+PiW+bAwiqIYhsG9EvjTDN3MYnE33ELv5QlFUei9/EHvbUhDbdJIArRt27axY8fW1NQ0sTpTUalUvNavVqtNsAu1WqeAJSzfLUNRVFxcXHp6enp6+vPPP98ycyCW5b0dLBmal1doXl6hefnDPmHuQFqNRhKgd999d8SIEUuXLnVycmqegDh834FarVYbvwtKqdSZQsWqWXW9rMjkaJru37//wYMH09PT4+LiPDw8+N7jU2FZVq1W40PIH66FzR1F26RWq9F7+cN9PaP38oSmafTep9LIJOiysrJFixZ17tzZ+a+aJzhoiEAgiIuLk8lkGRkZ3NlJAAAAaLpGEqC4uLi8vLzmCQWeikAg6N+/v5ubW2ZmJnIgAACAp9LIKbC333572rRpOTk5YWFhVlZWmvL4+HieA4PGMQzTv3//zMzMzMzMmJiY9u3bmzsiAACA1qGRBCguLo4QkpKSolNeXV3NV0TwNGiajomJOXjw4KFDh2JjY3FtPAAAQFM0cgqsugHNExw0BcMwcXFx7u7umZmZ169fN3c4AAAArUAjCRAh5OHDh7/99tvatWvLyspycnL4vj4LngGXA3l5eR05ciQ3N9fc4QAAALR0jZwCy83NjYmJoWm6tLT0pZdemjNnzp9//pmamurj49Ms4UFT0TQdFRUlEolOnz5dXV0dGhpq7ogAAABarkZGgJKSkqKjo2/dumVra0sI+fbbb93c3JKSkpolNng6FEX17t07NDQ0JycnKysLd4MAAABoSCMjQEePHk1PTxeJRNxLV1fXBQsWjB49mv/A4Bl16dJFIBCcOnWqpqamX79+3M3RAQAAQFsjCZCjo6POlGeFQmFtbc1nSGCsgIAAoVB4/Pjxurq66OhogaDxJ74BAABYlEaGBxISEhYtWlRaWsq9zMvLmz179tChQ/kPDIzSoUOH6OjooqKitLQ0XLUHAACgo5EEaMWKFa6urm5ubo8ePQoPDw8KCvL391+1alXzBAfG8PLyeuGFF6qqqn7//feysjJzhwMAANCCNHJyRCqV/vLLL5cvX87JyVEqlUFBQSEhIc0TGRjP2dl5yJAhBw4c2Lt3b0xMTEt7bCoAAIC56E+A0tPTdUocHBwIIcXFxdwqA4/CyM7O3rJli0KhCA0NnTx5smYCNSczM3Pbtm1VVVWenp7Tp0/HVzLfbGxsBg8enJmZmZGR0atXr06dOpk7IgAAAPPTnwCNGDFCsyyXy7kLqimKYlnW2trax8cnJydH7xvlcvm6deuWLl3q7e2dnJycmpqakJCgWfvnn39u2rRpzZo1zs7OP/744/r161euXGnSwwE9RCJRfHz8sWPHTpw4UV5eHh4eTlGUuYMCAAAwJ/1zgKqe+OGHH3x8fFJTUxUKhUKh2Ldvn6ur60cffdRQdWfPnvXz8/Pz82MYZsiQIUeOHNFeW1paOmDAADc3N4FAEBsbe/fuXRMfDTSApunIyMiQkJDLly8fOXJEqVSaOyIAAABzamQO0Hvvvbd27dpBgwZxLwcOHLhmzZpFixa9+OKLercvKSmRyWTcsqurq+byMU5oaCh3h+La2toff/wxKipKs6qiomLDhg3ccu/evfv27ftMh9MkFEWJRCLjb5CjtrKq+WsJwzBCodDIavkTHh7erl27o0eP7tu3Lz4+3sbGho+90DSNC+95RdN0S+5mrRpFURgf5Q9FUei9/EHvbYhardZb3sgX1c2bN9u1a6dd4uDgcPPmzYa217n7sN69Hj9+/KuvvoqIiBg/frymsKam5uTJk9yyp6cn358Qk3wIlQyjU0LRVAu/8WCnTp2cnJzS0tJ27tzZv3//5557zuS74P7Gmbxa0MCfOf5wJ/rNHUVbRtM0Wpgn6L1Pq5EEqFevXh988EGPHj3s7OwIIY8ePVq2bFnv3r0b2t7Z2fnChQvccmlpqbOzs/ZalmW/+OKL69evL1q0qH379tqrXFxcdu7cqXmpM3RkWk5OTtXV1XK53Mh6qKoqne95ZZ2SranRv3WLYWNjM3To0EOHDu3du7d79+5dunQxbf1CoVCpVOJzyBOJRKJSqXASkycCgUClUqH38kQsFqP38ge91wDd4QpCSKMJ0GeffRYTE+Pt7d2rVy9CyMmTJ6VS6eHDhxvaPiws7MsvvywqKnJzc0tLS4uMjOTKb9++7eLicunSpby8vHXr1jH1xk6gOYnF4vj4+HPnzmVnZ5eXl/fp0we/EQAAsCiNJECdO3fOz8/funVrbm6uQCAYPXr03//+d6lU2tD2Uqk0KSkpJSVFqVQGBgYOHz6cK09MTPzoo49ycnJu3rypeZSYVCr97rvvTHUk8FRomg4NDbWxscnKyqqoqIiJicETTgAAwHK00FOGfJ8CUygUJjgFtmcPPW6cdoloWTKTMNLIaptZcXHx4cOHVSpVv379PD09ja8Qp8B4JZFIlEolTiLwBCcReIVTYLxC7zVAEtK5fiEmq1o6mUw2fPhwJyenjIyMrKyshmbLAwAAtCVIgOC/U4LCw8OvXr26d+/eqqoqc0cEAADALyRA8F+BgYEDBgyQy+W7d+++c+eOucMBAADgERIg+B9XV9dhw4Y5OjoeOHDgzJkzOB0GAABtFRIg+AuJRBIfH9+9e/e8vLzdu3eXl5ebOyIAAADTQwIEuiiKCgkJGTRokEql2r17d05ODi4rAACANgYJEOjn7Ow8fPhwf3//7Ozs9PR04+8aAAAA0HIgAYIGMQzTq1ev/v37l5WV7dq1y8Az4AAAAFoXJEDQCE9PzxdffNHFxeXQoUOHDh2qrq42d0QAAADGQgIEjZNIJP3794+Jibl3796OHTuuXr1q7ogAAACM0sizwAA0vL29ZTLZyZMnT5w4cevWrd69e+PxYQAA0EphBAiegpWVVWxsbExMzIMHD3bt2oWhIAAAaKUwAgRPzdvb29XVNSsr68SJEzdu3IiIiLC3tzd3UAAAAE+hhY4AUXwy4S7M20pmJJFIoqOj+/fvX1VVtWvXrrNnz6pUKnMHBWAsC/9cQ6uG3vu0WugIkI2NDX+VUxQlEokYhjGyHpVEUvPXEoZhhEKhkdW2Ir6+vl5eXhcuXDh//nxBQUHv3r19fX1pmhYKhbh3In+4FjZ3FG0TTdMURaH38oSiKIZh8CXNE/TehjTUJi00AaqsrOSvcpFIVFtba/yd/SiFQmcATaVSkbo6I6ttdbp06eLl5ZWVlZWRkeHp6dm3b18rKyt8CHnCMIxarVYqleYOpG0SCAQqlQq9lyc0TatUKvRenqD3GqA312mhp8CgdbGzs4uPj4+MjCwtLd2+ffu5c+fwNw4AAFqyFjoCBK2Rn5+fp6fnxYsXL168mJ+fHxoa6ufnZ+6gAAAA9MAIEJiSSCTq3bt3QkKCi4vL0aNHd+/eXVxcbO6gAAAAdCEBAtOzs7OLiYmJj49XqVR79+49cuRIVVWVuYMCAAD4H5wCA754eHgMGzYsPz//3LlzN2/e7NSpU0hIiEQiMXdcAAAASICATzRNd+7cuUOHDpcvX7548eK1a9cCAgK6dOkiEonMHRoAAFg0JEDAO4FA0KVLF39//0uXLuXm5ubn5wcHBwcGBhp/KyYAAIBngwQImolYLA4NDe3UqdPZs2fPnj175cqVrl27duzYkaYxEQ0AAJobvnugWdnY2ERFRQ0bNszJyenEiRO//vrrlStX1Gq1ueMCAADLghEgMIN27drFxsaWl5fn5ORkZWXl5OQEBQV16tQJJ8UAAKB5IAECs3FwcIiMjAwODj5//vypU6cuXboUHBzs7+8vEKBbAgAAv/BNA2amGQ26cOHC6dOnL1y40Llz54CAAFwwDwAA/EECBC2Cg4NDdHR0VVVVbm5ubm5uTk6Oj49PSEiInZ2duUMDAIA2CAkQtCA2Nja9evUKCQnJy8u7cuXK9evXvb29AwMDXVxczB0aAAC0Kaa/Ciw7O3v69OlvvPHGhg0bamtr62+gUqkmT55cUVFh8l1D2yCRSHr06DFq1KiwsLDS0tLU1NTdu3cXFhbiYjEAADAVEydAcrl83bp1c+bM2bRpU3l5eWpqqs4Gqamp8+bNu3fvnmn3C22PUCgMCgoaOXJk//79RSLR0aNHf/nll+zsbLlcbu7QAACg1TPxKbCzZ8/6+fn5+fkRQoYMGbJ169aEhATtDby8vEaPHp2SkqLzxrq6umvXrnHLTk5OfD8qgaZpE1xqxDDsXwsomsJt/agnTFWhl5eXl5fXw4cP857w8vLq3Lmzq6urCffSilAUuhlfTN57QRvXtui9PEHvbQjLsnrLTZwAlZSUyGQybtnV1bW0tFRng+DgYEJI/du9PHjwYOzYsdzyuHHjZsyYYdrAdEgkEuMvMqqzttZ5xLmAEeApV4QQPhrBzc3Nzc2tT58+ly9fzsvLu379uoODQ2BgYKdOncRiscl315IxDIMbJvEHbcsr9F5eoW31amj6hIkTIJ08q+mTNpycnL777jvNcnl5uWkD02Zvb19TU1NdXW1sRY8f6xQoVUqib9qTRREIBEqlkqfKaZoOCgoKDAy8c+fO1atXT5w4kZWV5ePj07lzZ03m3baJRCKVSqVSqcwdSNvEMAzalj/ovbxC720Iy7J6/0s2cQLk7Ox84cIFbrm0tNTZ2bmJbxQKhYGBgZqX9YeOTEutVhv/JU2pVDojuayaxURd9gle9+Lh4eHh4SGXy/Pz8/Pz8wsKCuzs7Dp06NChQwepVMrrrs2OZdHN+ELTdDP0XovFtS16L0/Qe5+WiROgsLCwL7/8sqioyM3NLS0tLTIykiu/ffu2i4uLpZ2qAL5JpdJu3bqFhITcu3evoKDg4sWL586dc3Fx4Sai4Y7SAADQEBN/Q0il0qSkpJSUFKVSGRgYOHz4cK48MTHxo48+CgoKMu3uAAghFEW5u7u7u7vX1tYWFhYWFhaeOHEiOzvby8vL19fXzc0NswIBAECH6f9FDg0NDQ0N1SncsWOH9st///vfJt8vgEgkCggICAgIKC8vLygouH79+rVr16RSqY+Pj4+PT9NPyAIAQJuHcwTQBjk4OISFhYWGhhYXFxcWFhYUFOTm5trZ2fn4+Hh7e7dr187cAQIAgJkhAYI2i6IoV1dXV1fXiIiIO3fuXL9+PTc398KFC3Z2dt7e3t7e3o6OjuaOEQAAzAMJELR9NE23b9++ffv2KpXqzp07N2/evHz58sWLF21sbHx8fNq3b+/s7Ix5QgAAFgUJEFgQhmG4+0qrVKqioqKbN29evXo1JydHIpF4enq2b9/e3d0d144BAFgC/K0HS8QwjKenp6enp1qtLi4uvnXr1q1bt65duyYQCNzd3T09PZ977rk2fz8hAABLhgQILBpN09xDNsLDw8vKym7dunX79u0TJ06wLOvg4ODp6enh4SGTyfD0IgCANgYJEMB/tWvXrl27diEhIdXV1Xfu3Llz505+fn5OTo5QKHR/ws7OztxhAgCACSABAtAlkUi4p2qwLFtaWnrnzp27d+9mZWWxLGtjY+Pm5ubh4eHm5mb883QBAMBckAABNIiiKBcXFxcXl+7du9fU1Ny/f//u3btFRUXXrl0jhDg4OHCnz1xdXfGYFwCA1gUJEECTiMVi7goyQkhVVdW9e/eKior+/PPPy5cvkyfJkKurq0wms7KyMnewAADQCCRAAE/NxsamY8eOHTt2JIRUVFTcv3///v37mmTI1tZWJpPJZDIXFxcHBwdzBwsAAHogAQIwip2dnZ2dnb+/PyGkqqqq+ImCggJCiFgsdnFxcX5CJBKZO14AACAECRCACdnY2NjY2Pj5+RFCamtri4uLS0pKSkpKcnJylEolRVF2dnZcPuTk5NSuXTtcXQ8AYC4tNAGytbXlr3KKokQiEcMwRtajsrKq+WsJwzBCodDIals7mqaFQiHLsuYOxMyEQqGvr6+vry8hhGXZsrKykpISzeAQy7I0TTs6OmoGh5qeD3EtzHP4FoqmaZqm0Xt5QlEUwzB47AxP0Hsb0lCbtNAEqKqqir/KRSJRbW2tQqEwsh6qulrnc6xSqUhdnZHVtnZCoVCpVOJDqMPW1tbW1pYbHFIqlQ+euHv3LjdziKZpBwcHxyfatWunN8thGEatViuVyuY+AMsgFApVKpVarTZ3IG0TTdMqlQq9lyfovQbozXVaaALUDF+fJtgFy+IfGXgGAoGAe0w997K2tvbhE6WlpYWFhdyfMBsbG0dHRwcHh3bt2jk4ONja2uKUGd9YlkXuDq0Ueu/TaqEJEIDlEIlE3P2EuJcqlapMy+XLl2trawkhDMPY29tzKZGNjQ2XEuFsAgDAs0ECBNCyMAzDzQrSlMjl8vLy8ocPH5aXl5eXl9+4cYM7iUDTtL29vb29vZ2dnWYBT7MHAGgK/K0EaOmkUqlUKvXw8CCESCSSurq6R48eccnQo0ePHj16dPfuXW6UiBBibW1tZ2dna2vLXZ/PzT3CuTMAAB1IgABaGYqiuOvtPT09NYXV1dWPHj2qqKjgft67d+/atWvcXCKKorisiHsXlxLZ2NjgpkQAYMmQAAG0BRKJRCKRaCZWE0JYln38+HFFRUVFRUVlZWVlZSV3Bb5KpeI2EIlEXEpkbW1ta2trbW3NLSMxAgBLgAQIoG3SDBRx58405HJ5VVVVZWVl1RMPHz58/Pix5voRoVDIJUNSqdT6CSsrK2tra+PvngUA0EIgAQKwLNyMIplMpl2oVqu5xOixluLi4qqqKu27tojFYi4rsrKykkql3E9uQSKR4JI0AGhFkAABAKFpmhsuqr+qpqZGoVBUVVUpFAq5XP748WO5XF5SUqJQKGpqarRr4E7DSaVSzU8uMeKIxeJmPCAAgEYgAQIAQ8RisVgs1vtYe5VKxWVFiifkcnl1dfWDBw/u3LlTXV2tfVs2TYaknRJZWVmJ/wrDSADQPJAAAcAzYhimoXEjQgjLstXV1TU1NVxWxOHGjbgZ2dXV1fWfiqCTD4lEIu1l7iW3gFQJAIyBBAgAeEFRlJWVlZWVld7RI45KpeKSJO6nRnV1dW1tbXl5OfeyTt8j9kQNEAqFQqGQW9D+iRncAKANCRAAmA3DMNxVZoY3U6vVtbW1tbW1NTU1mp86qqqquIW6ujq9T0SiaVqTGwkEAqFQKBAIxGKx4AlueImmac1azQKSJ4C2BwkQALR0mvlDTdy+rq6urq6OS4bqL2teKhSKyspKzSrDj9EWiUQMwzAMo50k0TStKRcKhRRFcS+5nEm7kPvJ1WOCFgEAo5k+AcrOzt6yZYtCoQgNDZ08ebLOp93wWgAA43EjPVKp9KnexT1Mu7q6uq6uTqlU1tbWqlQqlUpVU1PDLdTW1iqVSm44SqVScZtVVFTU1taq1WqlUsmtbcq+uJRIKBQSQrjr47hUifvJ5U9cmkWe5EwCgYCmaW4cS7O9phIu69KUE2RaAI0xcQIkl8vXrVu3dOlSb2/v5OTk1NTUhISEJq4FADAjLplgGEbvGbSm0+RDKpWK+8llSyzLcuNMXJ6kKWFZVnszbgOWZbnnu3EbcGufIRhNFqVJlTRjUUQrSdKkTZpESrtQ815uA80JQe2Ntbchf02/NJtxiSNXolnLJXbPcGgARjJxAnT27Fk/Pz8/Pz9CyJAhQ7Zu3aqd4hheCwDQBvA69MJlRVwKpUmSuJyJPMmWCCHcLZq4BKuhBc0DdLkzgIQQTZqlvVZTuWa//B0d0crYNOpnSHpnZdVvdp2czPDb9e5aQzvtq6/R3zh3ttTwNoZ3oa2hlFEgEKhUKr3pu3bW+wwMtIzZazOGiYMoKSnR3GHW1dW1tLS0iWsfPHgwa9YsbnnYsGGjRo0ybWDaKIqSSCTG/5FSWlsr/loiEDBCix925j5pRv4PDQZwJ0fMHUXbRFGU8SNAvGo5J7a0J5trz6Dixr00m2kSKc1Lzbu4YTCdtdovuYExwyWalE6bJgbtSLSzOm31w9Cup6GBN53DBGPExMT4+/vzV39DWbuJEyCdPxw6ezWwVigUBgYGcssuLi68diyBQKD9P80zq9+mSmcH1sfdyGpbO6FQyA3gmzuQtkksFuMvL38M/A8NBugZZtHHonqvzq3S9ZLL5Y1uQwhRKpVVVVWNbsYwDJeuqdXqR48eNTFOHY8fP24oHXwq3A3Amrhxna9HLZ9fnUqlUu/NykycADk7O1+4cIFbLi0tdXZ2buJaOzu7BQsWaF7qDB2Zllgsrq2tbfrvpiGUQqHzb3hNTY26Cd20bbOxsVEoFM82XwEaZareC3pZW1tXV1ej9/JEJBJZVO9t9ESPnZ2dCXfXqntvUzI8Y+i9X6uJB9LDwsLy8/OLiopYlk1LS4uMjOTKb9++XVNT09BaAAAAgOZk4hEgqVSalJSUkpKiVCoDAwOHDx/OlScmJn700UdBQUF61wIAAAA0J9PPxA4NDQ0NDdUp3LFjh4G1AAAAAM0J15IAAACAxUECBAAAABYHCRAAAABYHCRAAAAAYHEoC7zl15dffhkWFtazZ08j61Hn59du365dIhwyhOna1chqAQzYtGlTt27devXqZe5AAJ7a5s2bu3btGhERYe5AAAjh4yqwlm/r1q1CodD4BIj295e8+65JQgJooh9//JEQggQIWqOff/5ZrVYjAYIWAqfAAAAAwOIgAQIAAACLY4lzgO7evWtra2tra2vuQACe2t27d21sbEz7CCGA5lFUVGRtbY3eCy2EJSZAAAAAYOFwCgwAAAAsjmVdBZadnb1lyxaFQhEaGjp58mSRSGTuiACaas6cOdevX6coihAyaNCgSZMmmTsigMapVKpp06atXr1ac+YLf4ehhbCgBEgul69bt27p0qXe3t7JycmpqakJCQnmDgqgSViWLSoq+umnn/BtAa1IampqRkbGvXv3NCX4OwwthwWdAjt79qyfn5+fnx/DMEOGDDly5Ii5IwJoqpKSEkLImjVrJk+evH79+srKSnNHBNA4Ly+v0aNHC4VCTQn+DkPLYUEJUElJiUwm45ZdXV1LS0vNGw9A0z169CggIGDixIkbNmwghGzatMncEQE0Ljg4ODw8nGEYTQn+DkPLYUEJkM71bmq12lyRADwtf3//xYsXy2QykUj06quvZmVl4fpNaI3wdxhaDgtKgJydnTX/bZSWljo7O5s3HoCmKywsLCgo4JYFAoH2v9QArQj+DkPLYUEJUFhYWH5+flFREcuyaWlpkZGR5o4IoKkqKyuTk5PLyspYlt25c2ffvn25y8EAWhf8HYaWw7JuhJidnf3NN98olcrAwMApU6ZoT80DaMlYlt21a9fu3btZlg0KCpo8ebJUKjV3UABNMnr06M2bN2tfBo+/w9ASWFYCBAAAAEAs6hQYAAAAAAcJEAAAAFgcJEAAAABgcZAAAQAAgMVBAgQAAAAWBwkQAAAAWBwkQAAAAGBxkAABQFM9fvz4nXfe6d69u1Qq9fHxmTRp0t27dzVrJRJJZmamzluqq6spijp9+nT92tRq9RdffNGjRw8bGxtXV9cXXnihJT8b/Ouvvz569Kjx2wBAC4EECACaRC6X9+zZc9u2bW+++eZvv/02f/78w4cPDxgwoKqqysC7GIaZOXOm5gHg2lauXJmUlDRu3LjMzMyff/65ffv2zz///KlTp3g7AqMgAQJoYwTmDgAAWofk5OTHjx9nZ2dzD7CMj48fMWJEhw4dvv/++6lTpzb0LqFQuH79er2rtmzZ8vbbb8+aNYt7GRMTU1BQsHnz5vDwcD7iBwDQhhEgAGicWq1et27dO++8o/34bldX16+++srb21tTcv/+/WHDhtnb2/v5+f373/8mhCiVyoZOgVVWVubn52uexkNR1Oeffz5lyhTu5Y0bN4YOHerg4BASEvLTTz916dIlPT29qqqKoqicnBxum2vXrlEUxT1d/MqVK8OGDXNxcbG1tY2KisrOzua2EQgEJ06cGDBgwIgRIwghcrl85syZ3t7etra2gwcPzsvL1mTfywAABZhJREFUqx/Y9u3bu3XrZmVl5e3tvXr1akJIz549Dx06NH/+/Pj4+Ib2pbMNALR0LABAYwoLCwkhZ86cMbCNWCx2d3fftm1bTk7O+PHjJRJJdXV1XV0dIeTUqVP1t1++fDkhpHv37h9//PHFixfVarVmlUKh8PLyiouLy8jI+PXXX728vEQi0f79+ysrKwkhFy9e5DbLz88nhJSUlLAs27lz59jY2PT09PT09KioqNDQUG4bhmECAwMXLVp05MgRlmVffvnlfv36ZWRkHD9+fOTIkS4uLg8ePNA5UoZh5s6de/LkyVWrVhFCjhw5UldXFx0dvXz5cqVS2dC+dLYBgBYOCRAANO7AgQOEkLt37xrYRiwWL1u2jFvmEqbr168bSIBYlt2/f/8bb7zh6+tLCHFxcZk9e3ZFRQXLsl9//bW9vX15eTm3WWpqKiHEQAKkUqnWrVtXUFDAlX/77bdOTk7cMsMws2bN4pYvX77MMExxcTH3sqamRiaT7dy5Uzuk9PR0QsjVq1dZllWr1b///vv169dZlo2JiUlOTmZZ1sC+NNsAQMuHOUAA0DgvLy9CSFFRkbu7u3Z5cXHxgwcPAgMDuZc9e/bkFqysrHRq2Llz57hx47jlY8eOBQUFEULi4+O5E0Y3btzYvHnzp59+mpubu3fv3pycnIiICHt7e277mJgYw+HRND1t2rTDhw9v3779zJkzaWlpFEVp1kZFRXELFy9eVKlU/v7+mlWVlZXXrl3Trqpfv37Dhw/v1q3b4MGDn3/++ddee83Ozq7p+wKA1gJzgACgcb6+vlZWVvUvcXrrrbcmTZqkeVk/79GIj48/90THjh0vXrw4YsQIhULBrfXx8Vm+fPmnn366b9++ioqKmpoa7ffSNK03ydC8vaqqql+/fnPnzq2rq5s+ffrGjRu1N3N0dOQWlEqls7PzOS0FBQUTJkzQ3lgikfz2228XLlwIDQ394Ycf2rdv/9tvv2lvYHhfANBaIAECgMYxDDNjxozk5OR79+5pCq9fv75v376BAwc2pQZra2ufJ0QikYODw2+//bZnzx7tbR4+fOjg4GBjY9O5c+esrKyKigqu/PDhw+yTudKEkLKyMm5BM7c6MzMzLy/v1KlT8+fPj4yMVKlUemMICgoqLS1VKBRcGFZWVlOmTLl9+7b2NhkZGQsWLOjQocPChQv/+OOPgQMHfvXVV9obNHFfANDCIQECgCZ5//337e3tw8LC1q9fn5aWtn79+piYGJlMlpSU9Ay1tW/ffuLEif/4xz8WL168Z8+e/fv3f/jhhwsWLJg9ezZN06+99hrDMCNHjjx06NDOnTunTZvGjQBZW1s7OzsvX778zJkzu3fv/uSTT7ja7Ozs5HL5999/f/fu3Z07d7733nuPHz++c+eOzk5DQkJeeOGF4cOH79q1a//+/WPGjPnzzz8DAgK0t2EYJjk5eeXKlefOnfvxxx+PHz/eo0cPQghN09euXbt3756BfWm2eYYGAYDmZu5JSADQalRUVMyaNSswMFAikXTo0GHKlCn379/XrBWLxQcPHuSWi4qKSGOToGtra9esWRMbG+vo6MilVp9//rlKpeLWXrx4MSYmxtbWtkuXLrt27bK2tt6/fz/LsqmpqZ07d7a2to6Li7t48SIhpKSkRK1WL1myRCaTOTo6vvLKK1euXAkODg4PD2dZlmEYTVQsyz569OjNN9/08PCwt7dPSEjgJjjr+Pjjj319fUUiUfv27d9+++2amhqWZb/++msnJ6eEhAQD+9JsY5LWBgBeUazWwDIAQMtkY2OzY8cO3GIHAEwFp8AAAADA4iABAgAAAIuDU2AAAABgcTACBAAAABYHCRAAAABYHCRAAAAAYHGQAAEAAIDFQQIEAAAAFgcJEAAAAFgcJEAAAABgcf4f3WZAnAk/5woAAAAASUVORK5CYII=" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-chi2-distribution-on-top-of-permuted-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution on top of permuted statistics</h2>
@@ -220,10 +223,11 @@ <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>, <span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. `infer` currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
+## theoretical method. {infer} currently does not check these for you.</code></pre>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="compute-theoretical-p-value" class="section level2">
 <h2>Compute theoretical p-value</h2>
@@ -231,7 +235,7 @@ <h2>Compute theoretical p-value</h2>
 <span class="st">  </span><span class="kw">chisq_test</span>(<span class="dt">formula =</span> origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(p_value) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.7513009</code></pre>
+<pre><code>## [1] 0.7513</code></pre>
 </div>
 </div>
 
diff --git a/inst/doc/flights_examples.html b/inst/doc/flights_examples.html
index 55215ca2..70b57a27 100644
--- a/inst/doc/flights_examples.html
+++ b/inst/doc/flights_examples.html
@@ -124,7 +124,7 @@ <h3>One numerical variable (mean)</h3>
 <span class="kw">ggplot</span>(<span class="dt">data =</span> null_distn, <span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> x_bar, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>x_bar) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
 <div class="kable-table">
@@ -155,7 +155,7 @@ <h3>One numerical variable (median)</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> x_tilde, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>x_tilde) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
 <div class="kable-table">
@@ -186,7 +186,7 @@ <h3>One categorical (one proportion)</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> p_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>p_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
 <div class="kable-table">
@@ -226,7 +226,7 @@ <h3>Two categorical (2 level) variables</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -248,7 +248,7 @@ <h3>One categorical (&gt;2 level) - GoF</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="kw">pull</span>(Chisq_hat), <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span><span class="kw">pull</span>(Chisq_hat))) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -266,7 +266,7 @@ <h3>Two categorical (&gt;2 level) variables</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="kw">pull</span>(Chisq_hat), <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span><span class="kw">pull</span>(Chisq_hat))) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -289,7 +289,7 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -312,7 +312,7 @@ <h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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BAgUDEBAaBiHao5BAgQIECAAAECBPIEBIA8HWUECBAgQIAAAQIEKiYgAFSsQzWHAAECBAgQIECAQJ6AAJCno4wAAQIECBAgQIBAxQQEgIp1qOYQIECAAAECBAgQyBMQAPJ0lBEgQIAAAQIECBComIAAULEO1RwCBAgQIECAAAECeQICQJ6OMgIECBAgQIAAAQIVExAAKtahmkOAAAECBAgQIEAgT0AAyNNRRoAAAQIECBAgQKBiAgJAxTpUcwgQIECAAAECBAjkCQgAeTrKCBAgQIAAAQIECFRMQACoWIdqDgECBAgQIECAAIE8AQEgT0cZAQIECBAgQIAAgYoJCAAV61DNIUCAAAECBAgQIJAnIADk6SgjQIAAAQIECBAgUDEBAaBiHao5BAgQIECAAAECBPIEBIA8HWUECBAgQIAAAQIEKiYgAFSsQzWHAAECBAgQIECAQJ6AAJCno4wAAQIECBAgQIBAxQQEgIp1qOYQIECAAAECBAgQyBMQAPJ0lBEgQIAAAQIECBComIAAULEO1RwCBAgQIECAAAECeQICQJ6OMgIECBAgQIAAAQIVExAAKtahmkOAAAECBAgQIEAgT0AAyNNRRoAAAQIECBAgQKBiAgJAxTpUcwgQIECAAAECBAjkCQgAeTrKCBAgQIAAAQIECFRMoG2w2rN79+549tlno6WlJS644IIYPnx410tv3LgxnnvuuXjjG98Yp59+etf8tM7KlSuzdc4///we63Qt5AEBAgQIECBAgAABAjULDMoZgB07dsS1114bTzzxRKxYsSJ+7/d+L9LOfZpWrVoVCxYsiDVr1sSNN94YDzzwQDZ/586dMX/+/Hjsscfi3nvvzcoOHjyYlfmPAAECBAgQIECAAIGjExiUMwDLli3Ljux/8pOfzGp5zz33xOrVq2PGjBmxdOnSuPXWW+PNb35zfOADH4hrrrkmLr/88rj//vtj1qxZcf3112frpADx9NNPx4UXXnh0LbUWAQIECBAgQIAAAQIxKAHgmWeeife9733x1FNPxf79++Oqq66K1tbW2LdvX6xbty4LAqkvpkyZEqNGjYr169fH2rVrY86cOV1dNHPmzHjxxRd7BIB0liBtI03p0qL0z/R/Fhz6FzBO+rdJJX6X8n06jQZeyhJDJVCG3/Ey1HGo+q/zdRl1SvT9k0/fLt3nMuqu0fPxoASAn/3sZ/HlL385Jk2aFHv37o077rgjPv/5z8fmzZtj9OjRPXbcx40bF5s2bYqXX345xo4d21Xb9DiFhe7TDTfcEI888kg2q62tLV544YXuxU35ODkkU1P/AieeeGL/hUrCGBp4EBhDAxsN5RJl6J8y1HEo+/C4446L9M/Uv8DIkSP7L2zykn2HjgcPHzEimvn3LF1+nzcNSgBIR/1PPfXUWLRoUVaXm266Kb7zne/Eeeedl50R6F7BdES/o6Mjhg0b1qMszT98sKczCZdcckm2ejqjsGXLlu6barrHY8aMyc6IpDMjpr4F0hmmgX4p+l6zOeamMZRC+q5du5qjwUfRSmPoKNAGeZWi/y0whvIHRDrgt2fPHu9DOUzGUA7OoaLjDt0yuv/QfuO2Jt4vHOjsx6AEgMmTJ8eZZ57Z1VunnXZadjnP7NmzY/v27dkNwe3t7Vl5Ovo/derUmDhxYnYmoHOlNH/atGmdT7Of6ZOBuk8bNmzo/rTpHqejJSkoCQD9d30Kl3z69zGG+rfpLDGGOiWK+7Pov+PGUP7YcTAr3yeVpn2moo/zgVtx7JZI544OHDjQ1Ead+9X9KQ/KpwC97W1vi0cffTTrjJTq06cBpRuA06UG6UbfdJNwmh5//PGYMGFC9u/iiy+O5cuXZ0cA0seEPvnkk9kZg/4aYj4BAgQIECBAgAABAgMLDMoZgPe85z3ZEf/Oj/+86KKLIh39T9PChQu7Pv4zXcZz8803Z/NTefrI0Hnz5mU3DM+dOzemT5+elfmPAAECBAgQIECAAIGjExiUAJC+9GvJkiXZtddpJz+d/uycTjnllOwjP9M1m+PHj++cnZ0duOWWW2Lbtm3Ztf/pbIGJAAECBAgQIECAAIH6BAZ1rzrdtNLf1H3nv/sy6VpAEwECBAgQIECAAAECjREYlHsAGlNVWyFAgAABAgQIECBAoF4BAaBeQesTIECAAAECBAgQKJGAAFCizlJVAgQIECBAgAABAvUKCAD1ClqfAAECBAgQIECAQIkEBIASdZaqEiBAgAABAgQIEKhXQACoV9D6BAgQIECAAAECBEokIACUqLNUlQABAgQIECBAgEC9AgJAvYLWJ0CAAAECBAgQIFAiAQGgRJ2lqgQIECBAgAABAgTqFRAA6hW0PgECBAgQIECAAIESCQgAJeosVSVAgAABAgQIECBQr4AAUK+g9QkQIECAAAECBAiUSEAAKFFnqSoBAgQIECBAgACBegUEgHoFrU+AAAECBAgQIECgRAICQIk6S1UJECBAgAABAgQI1CsgANQraH0CBAgQIECAAAECJRIQAErUWapKgAABAgQIECBAoF4BAaBeQesTIECAAAECBAgQKJGAAFCizlJVAgQIECBAgAABAvUKCAD1ClqfAAECBAgQIECAQIkEBIASdZaqEiBAgAABAgQIEKhXQACoV9D6BAgQIECAAAECBEokIACUqLNUlQABAgQIECBAgEC9AgJAvYLWJ0CAAAECBAgQIFAiAQGgRJ2lqgQIECBAgAABAgTqFRAA6hW0PgECBAgQIECAAIESCQgAJeosVSVAgAABAgQIECBQr4AAUK+g9QkQIECAAAECBAiUSEAAKFFnqSoBAgQIECBAgACBegUEgHoFrU+AAAECBAgQIECgRAICQIk6S1UJECBAgAABAgQI1CsgANQraH0CBAgQIECAAAECJRIQAErUWapKgAABAgQIECBAoF4BAaBeQesTIECAAAECBAgQKJGAAFCizlJVAgQIECBAgAABAvUKCAD1ClqfAAECBAgQIECAQIkEBIASdZaqEiBAgAABAgQIEKhXQACoV9D6BAgQIECAAAECBEokIACUqLNUlQABAgQIECBAgEC9AgJAvYLWJ0CAAAECBAgQIFAiAQGgRJ2lqgQIECBAgAABAgTqFRAA6hW0PgECBAgQIECAAIESCQgAJeosVSVAgAABAgQIECBQr4AAUK+g9QkQIECAAAECBAiUSKCtRHUdsKpjx44dcJkqL9Da2hojRoyIZnfI6+O2tjY+OUBpDLW3t0dLS0vOUs1dZAwVv/+L/h5oDOWPofT+429ZvtHw4cP9LcsnimHDhjW10YEDB3KFKhUAXn/99dzGVr0w7bjt3bs3mt0hr5/TH14+/QulMbRnzx5G/ROFMZSDU5Ciov+Opx2TotdxKLuyo6PD37IBOsAYygdqP1S8f//+pv49SyExb6pUABgo7eRBVKXs4MGDwaH/3uTTv01nCaNOib5/8unbpUhzy/AeWIY6DmWf+j3L1+eT79NZ6vesU6L3T/cA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBQSAynathhEgQIAAAQIECBDoLSAA9DYxhwABAgQIECBAgEBlBWoOAJdcckm8/vrrvSC+8IUvxAc/+MFe880gQIAAAQIECBAgQKB4Am15VXr66afjkUceyRZ54okn4s/+7M+io6Oja5X9+/fHP/3TP8Wpp57aNc8DAgQIECBAgAABAgSKK5AbAN70pjfFokWLYvfu3bF379745je/GcOGDetqzfDhw+ONb3xj3HTTTV3zPCBAgAABAgQIECBAoLgCuQHghBNOiBUrVmS1f+973xv33XdfHHfcccVtjZoRIECAAAECBAgQIJArkBsAuq+5bNmy7EzAj3/84+xn97IUCk466aTus/p9vHr16qzs9NNP71pm48aN8dxzz2VnE7rPT2ceVq5cGS0tLXH++edHOuNgIkCAAAECBAgQIEDg6AVqvgn461//epx44olxyimnxGmnndbj38c+9rGaarBly5a48cYb47vf/W7X8qtWrYoFCxbEmjVrsrIHHnggK9u5c2fMnz8/Hnvssbj33nuzsoMHD3at5wEBAgQIECBAgAABAkcuUPMZgIULF8b73ve++P3f//04/vjje7xSrZcFffazn+217tKlS+PWW2+NN7/5zfGBD3wgrrnmmrj88svj/vvvj1mzZsX111+fvda1114b6abkCy+8sMdre0KAAAECBAgQIECAQO0CNQWArVu3xquvvhp//ud/HpMnT659692WfPDBB2PChAlx8sknd83dt29frFu3LmbMmJHNmzJlSowaNSrWr18fa9eujTlz5nQtO3PmzHjxxRd7BIAUHDrvUWhra4vOswddKzXZg3SD9siRI6O9vb3JWl57c1tbW2PSpEm1r9BkS6YxlH4Hu3/aV5MRDNhcY2hAoiFfoOi/48ZQ/hBJPt6HBjYq+jjPb8GxL207dNl4Mxuly+jzppoCwNixY2P69OnZ9fiXXXZZ3vb6LEs7+d/4xjfizjvvjHvuuadrmRQqRo8enV3j3zlz3LhxsWnTpnj55ZcjvW7nlB6n7XSf0qVIBw4cyGalN4yBGtt93So+Tjv/6aNZ9+zZU8XmNaRNKRw1+zjJgzSG8nT+r8wYGthoqJco+u+4MZQ/QtL7UDpAmD590NS3gDHUt0vn3HQY9OCh/cM9A+wEdy5fxZ9pfzBvqikApA2ko+0f//jHs53wFAbSEffOKZ0VOOusszqf9viZfon/+I//OD7xiU9kR6e7F6ajjYdXMC2fjj4eXpbmpzeF7lO6ZKj7tGHDhu5Pm+5xekNIO//pjI2pb4F0FopP3zZpbucflW3btvW/UJOXGEPFHwBF/x03hvLHUNoHSH/LvA/17zR+/Hh/y/rniXSeP+1fFv29IKcJdRelv+d50y/24vOWOlSW7gF47bXXIl2Lf/j0/ve/P77yla8cPjt7/qMf/SheeumlLDykGSnRp0/1SZ/8k67v3759e3ZEtrOi6ej/1KlTY+LEidmZgM6NpvnTpk3rfOonAQIECBAgQIAAAQJHIVBzAHjllVeiv0/hSUfr+5vSl4n967/+a1dxugwoXds3/9An/KQp3eibPmI0hYjHH388u08gHR25+OKLY/ny5dnP119/PZ588snsm4izlfxHgAABAgQIECBAgMBRCdQcANIR+P4CQLo0J+20H82UziykjwZNN/Cm6/hvvvnmbDOzZ8/ObvCdN29eNn/u3LnZfQhH8xrWIUCAAAECBAgQIEDg/wRqDgBnnHFGdglQX3B5lwAdvnza4e8+pe8VSB/5mb4jIF3T1jmlewxuueWW7BrAFDC633PQuYyfBAgQIECAAAECBAgcmUDNAeCJJ57occNuujnne9/7Xtxxxx3Zx4Me2cv2Xrr7zn/30jFjxnR/6jEBAgQIECBAgAABAnUI1BwAzj777F4v89a3vjV27NgRf/InfxL/8A//0KvcDAIECBAgQIAAAQIEiiXQWm91TjrppPjP//zPejdjfQIECBAgQIAAAQIEBkGg5jMAzzzzTPbFHJ11Sp/Ln76sK33G/9vf/vbO2X4SIECAAAECBAgQIFBggZoDwJw5c/q8CThdBpRu1jURIECAAAECBAgQIFB8gZoDwI9//ONeHwM6evRon85T/D5WQwIECBAgQIAAAQJdAjUHgLFjx2Yf1XnffffFmjVr4sQTT4zzzjsv3vGOd8SIESO6NugBAQIECBAgQIAAAQLFFag5APzHf/xHpMuA0jcCz5gxI372s5/Fhg0b4tJLL82+xKujo6O4rVQzAgQIECBAgAABAgQygZo/BejDH/5wvOUtb4mf/OQn8fzzz8f69euz7wH4wQ9+ELfffjtOAgQIECBAgAABAgRKIFBTAEif9b9y5crsE39OPvnkrFktLS3ZJUCLFi2Kb3/72yVoqioSIECAAAECBAgQIFBTAGhtbc1uAE5B4PBp+/btPT4e9PByzwkQIECAAAECBAgQKI5ATQEgXd+fPut/8eLF8eyzz2ZhYNeuXbF8+fL4m7/5m7jkkkuK0yI1IUCAAAECBAgQIECgX4GaAkBa++///u+zG4AvuOCCmDJlShx//PFx2WWXxa/92q/FH/zBH/T7AgoIECBAgAABAgQIECiOQM2fAnTqqafGc889F48++mikG3/TWYFzzz03LrroouK0Rk0IECBAgAABAgQIEMgVqDkApK20t7dnR/0vv/zybKN93ROQ+2oKCRAgQIAAAQIECBAYUoGaLwFKn/m/YMGC+OIXv5hVON38O3369Pjrv/5rNwEPaRd6cQIECBAgQIAAAQK1C9QcAD7+8Y/Hj370o/j1X//1bOsjR46MpUuXZjcBL1u2rPZXtCQBAgQIECBAgAABAkMmUNMlQHv27ImHH344XnjhhZg6dWpW2fTRoPPmzYtt27bFfffdF1dcccWQNcILEyBAgAABAgQIECBQm0BNZwDSl37t3bs3+/bfwze7devW2LRp0+GzPSdAgAABAgQIECBAoIACNQWA4cOHx2/8xm/EJz/5yfjJT37S1Yz07cDpHoBLL720a54HBAgQIECAAAECBAgUV6CmAJCq/4UvfCHSl3+94Q1viHHjxkW6B+D888/PviBs0aJFxW2hmhEgQIAAAQIECBAg0CVQ0z0Aaen0xV//9m//FqtXr45///d/j3QPwDnnnBNnnnlm18Y8IECAAAECBAgQIECg2AI1B4DUjLTTf8YZZ2T/it0stSNAgAABAgQIECBAoC+Bmi8B6mtl8wgQIECAAAECBAgQKJeAAFCu/lJbAgQIECBAgAABAnUJCAB18VmZAAECBAgQIECAQLkEBIBy9ZfaEiBAgAABAgQIEKhLQACoi8/KBAgQIECAAAECBMolIACUq7/UlgABAgQIECBAgEBdAgJAXXxWJkCAAAECBAgQIFAuAQGgXP2ltgQIECBAgAABAgTqEhAA6uKzMgECBAgQIECAAIFyCQgA5eovtSVAgAABAgQIECBQl4AAUBeflQkQIECAAAECBAiUS0AAKFd/qS0BAgQIECBAgACBugQEgLr4rEyAAAECBAgQIECgXAICQLn6S20JECBAgAABAgQI1CUgANTFZ2UCBAgQIECAAAEC5RIQAMrVX2pLgAABAgQIECBAoC4BAaAuPisTIECAAAECBAgQKJeAAFCu/lJbAgQIECBAgAABAnUJCAB18VmZAAECBAgQIECAQLkEBIBy9ZfaEiBAgAABAgQIEKhLQACoi8/KBAgQIECAAAECBMol0Fau6ubXduTIkfkLVLy0paUl2traotkd8rp52LBhfHKAjKEcnJ8XGUMDGw31EkV/D2xtbfU+lDNIvA/l4Py8yPvQwEbN/nuWfo/ypkoFgIMHD+a1tSnKkgGH/K7mk++TShn1b+R3rH+bopSUYfyWoY5D3Z+M8nuAz0A+/pblCVUqAOzatSuvrZUvGzNmTOzfvz+a3SGvo9ORQT79C6UxtG/fPkb9E2VHbo2hHKACFBW9f7wP5Q+SsWPHeh/KJ4qOjg7v0zlGYw6VHTx4oKmN2tvbc4Qi3AOQy6OQAAECBAgQIECAQLUEBIBq9afWECBAgAABAgQIEMgVEAByeRQSIECAAAECBAgQqJaAAFCt/tQaAgQIECBAgAABArkCAkAuj0ICBAgQIECAAAEC1RIQAKrVn1pDgAABAgQIECBAIFdAAMjlUUiAAAECBAgQIECgWgICQLX6U2sIECBAgAABAgQI5AoIALk8CgkQIECAAAECBAhUS0AAqFZ/ag0BAgQIECBAgACBXAEBIJdHIQECBAgQIECAAIFqCQgA1epPrSFAgAABAgQIECCQKyAA5PIoJECAAAECBAgQIFAtAQGgWv2pNQQIECBAgAABAgRyBQSAXB6FBAgQIECAAAECBKolIABUqz+1hgABAgQIECBAgECugACQy6OQAAECBAgQIECAQLUEBIBq9afWECBAgAABAgQIEMgVEAByeRQSIECAAAECBAgQqJaAAFCt/tQaAgQIECBAgAABArkCAkAuj0ICBAgQIECAAAEC1RIQAKrVn1pDgAABAgQIECBAIFdAAMjlUUiAAAECBAgQIECgWgICQLX6U2sIECBAgAABAgQI5AoIALk8CgkQIECAAAECBAhUS0AAqFZ/ag0BAgQIECBAgACBXAEBIJdHIQECBAgQIECAAIFqCQgA1epPrSFAgAABAgQIECCQKyAA5PIoJECAAAECBAgQIFAtAQGgWv2pNQQIECBAgAABAgRyBQSAXB6FBAgQIECAAAECBKolIABUqz+1hgABAgQIECBAgECugACQy6OQAAECBAgQIECAQLUEBIBq9afWECBAgAABAgQIEMgVEAByeRQSIECAAAECBAgQqJaAAFCt/tQaAgQIECBAgAABArkCAkAuj0ICBAgQIECAAAEC1RIQAKrVn1pDgAABAgQIECBAIFdAAMjlUUiAAAECBAgQIECgWgICQLX6U2sIECBAgAABAgQI5AoIALk8CgkQIECAAAECBAhUS0AAqFZ/ag0BAgQIECBAgACBXAEBIJdHIQECBAgQIECAAIFqCQgA1epPrSFAgAABAgQIECCQKyAA5PIoJECAAAECBAgQIFAtgUELAHv37o2nnnoqnn/++Th48GAPxY0bN8bDDz8cq1ev7jF/9+7dsWLFinjyyScjrW8iQIAAAQIECBAgQKA+gUEJAC+//HLMnTs325F/8MEH43d/93cj7dynadWqVbFgwYJYs2ZN3HjjjfHAAw9k83fu3Bnz58+Pxx57LO69996s7PDgkC3oPwIECBAgQIAAAQIEahYYlADw1a9+NS6//PJYtGhRLFmyJN70pjfFI488klVy6dKlceutt8Z1110Xd911V9x9992xZ8+euP/++2PWrFnZ8nfeeWfs2LEjnn766ZobZkECBAgQIECAAAECBHoLtPWe1fg51157bbS0tHRt+LXXXot0hH/fvn2xbt26mDFjRlY2ZcqUGDVqVKxfvz7Wrl0bc+bM6Vpn5syZ8eKLL8aFF17YNS+dGfj+97+fPW9ra4ubbrqpq6wZH7S2tsaIESNi3Lhxzdj8mtqcxgmf/qnSGGpvb4/009S3gDHUt0uR5hb9d9wYyh8taX/B+1C+0fDhw/0tyyeKYcOa++/9/v37c4UGJQCkndLO6dvf/na20//ud787Xn311Rg9enSPcJDeuDdt2hTpsqGxY8d2rpY9TmGh+5SCQud9A+kNNf1CNPOU3jTTjluzO+SNAT55OpH9LjLKN+KT71OE0qK/BxpD+aPE37J8n1RqDA1slMZR0d8LBm7F0S9x4MCB3JUHJQB01mDZsmXZ9fy33XZbHHfccbF9+/Y4PKGkswIdHR2HktuwHmVp/siRIzs3lf1cvHhxj+cbNmzo8bzZnkyaNCm7t2Lr1q3N1vSa2zthwoTYvHlzzcs324JpDO3atSu2bdvWbE2vub3GUM1UQ7Zg+mCJIk/GUH7vTJ48ObtKwPtQ/07jx4+PLVu29L9Ak5dMOtT+ffv2xuaCvxccy25KZ9HypkELAF/60peyT/r527/920iX+qTphBNOyEJAuiG4s6Lp6P/UqVNj4sSJ2ZmAzsqn+dOmTet86icBAgQIECBAgAABAkchMCgX+j700EPxrW99K/7u7/6ua+c/1TVdtpNu9E1nBtL0+OOPRzoykv5dfPHFsXz58uxoZDqakz4K9LzzzsuW8x8BAgQIECBAgAABAkcnMChnANIn+7zyyivZJwF1VvO3fuu34mMf+1gsXLiw6+M/0zVtN998c7bI7Nmzs+8AmDdvXnatW/oY0enTp3eu7icBAgQIECBAgAABAkchMCgB4Gtf+1q/VTvllFOyj/xM17Kla9o6p3R24JZbbsmuRU7X/qfnJgIECBAgQIAAAQIE6hMozF51953/7k0aM2ZM96ceEyBAgAABAgQIECBQh8Cg3ANQR/2sSoAAAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0EABAaCBmDZFgAABAgQIECBAoOgCAkDRe0j9CBAgQIAAAQIECDRQQABoIKZNESBAgAABAgQIECi6gABQ9B5SPwIECBAgQIAAAQINFBAAGohpUwQIECBAgAABAgSKLiAAFL2H1I8AAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0EABAaCBmDZFgAABAgQIECBAoOgCAkDRe0j9CBAgQIAAAQIECDRQQABoIKZNESBAgAABAgQIECi6gABQ9B5SPwIECBAgQIAAAQINFBAAGohpUwQIECBAgAABAgSKLiAAFL2H1I8AAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0EABAaCBmDZFgAABAgQIECBAoOgCAkDRe0j9CBAgQIAAAQIECDRQQABoIKZNESBAgAABAgQIECi6gABQ9B5SPwIECBAgQIAAAQINFBAAGohpUwQIECBAgAABAgSKLiAAFL2H1I8AAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0EABAaCBmDZFgAABAgQIECBAoOgCAkDRe0j9CBAgQIAAAQIECDRQQABoIKZNESBAgAABAgQIECi6gABQ9B5SPwIECBAgQIAAAQINFBAAGohpUwQIECBAgAABAgSKLiAAFL2H1I8AAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0EABAaCBmDZFgAABAgQIECBAoOgCAkDRe0j9CBAgQIAAAQIECDRQQABoIKZNESBAgAABAgQIECi6gABQ9B5SPwIECBAgQIAAAQINFBAAGohpUwQIECBAgAABAgSKLiAAFL2H1I8AAQIECBAgQIBAAwUEgAZi2hQBAgQIECBAgACBogsIAEXvIfUjQIAAAQIECBAg0ECBtgZua8g3NWzYsCGvw1BXoKWlJTj03wt8+rdJJcmntbXVGMphMoZycApSVPT3QGNo4IHCKN+IT77P/5U29/5QGiN5U6UCwKhRo/LaWvmytOPW1tYWze6Q19Fpx4BP/0LpDcMY6t8nlRhD+T5FKC3677gxlD9K0vvQ8OHDvVfnMHmfzsH5edGwYa1NPYYOHDiQi1SpALBt27bcxla9sKOjI/bu3RvN7pDXz+lNk0//QmkM7dmzh1H/RFlAMoZygApQVPT+8T6UP0hGjhzpfSifKDsQUfRxPkATjmlxx6Gt79+/v6n/lrW3t+cauwcgl0chAQIECBAgQIAAgWoJCADV6k+tIUCAAAECBAgQIJArIADk8igkQIAAAQIECBAgUC0BAaBa/ak1BAgQIECAAAECBHIFBIBcHoUECBAgQIAAAQIEqiUgAFSrP7WGAAECBAgQIECAQK6AAJDLo5AAAQIECBAgQIBAtQQEgGr1p9YQIECAAAECBAgQyBUQAHJ5FBIgQIAAAQIECBColoAAUK3+1BoCBAgQIECAAAECuQICQC6PQgIECBAgQIAAAQLVEhAAqtWfWkOAAAECBAgQIEAgV0AAyOVRSIAAAQIECBAgQKBaAgJAtfpTawgQIECAAAECBAjkCggAuTwKCRAgQIAAAQIECFRLQACoVn9qDQECBAgQIECAAIFcAQEgl0chAQIECBAgQIAAgWoJCADV6k+tIUCAAAECBAgQIJArIADk8igkQIAAAQIECBAgUC0BAaBa/ak1BAgQIECAAAECBHIFBIBcHoUECBAgQIAAAQIEqiUgAFSrP7WGAAECBAgQIECAQK6AAJDLo5AAAQIECBAgQIBAtQQEgGr1p9YQIECAAAECBAgQyBUQAHJ5FBIgQIAAAQIECBColoAAUK3+1BoCBAgQIECAAAECuQICQC6PQgIECBAgQIAAAQLVEhAAqtWfWkOAAAECBAgQIEAgV6Att1QhAQKDKrBgwYJBfb0ivtg999xTxGqpEwECBAgQqIyAMwCV6UoNIUCAAAECBAgQIDCwgDMAAxtZggCBAgk4SxLhLEmBBqSqECBAoIQCzgCUsNNUmQABAgQIECBAgMDRCjgDcLRy1iNAgAABAhUUcJbNWbZ6h/VQj6H/t2lTPPL1b8SX/vPf623KUa9f9DO1AsBRd23vFYd6wPeu0eDPKfqAH3wRr0iAAAECBAgQKJaAS4CK1R9qQ4AAAQIECBAgQOCYCjgDcEx5bZwAAQIEiibgbK1LXIo2JtWHwGALOAMw2OJejwABAgQIECBAgMAQCggAQ4jvpQkQIECAAAECBAgMtoAAMNjiXo8AAQIECBAgQIDAEAoIAEOI76UJECBAgAABAgQIDLaAADDY4l6PAAECBAgQIECAwBAK+BSgIcT30gQIECBAgED5BHySlE+SKt+o7VljZwB6enhGgAABAgQIECBAoNICzgBUunuL1zhHTRw1Kd6oVCMCBAgQINBcAs4ANFd/ay0BAgQIECBAgECTCxT6DMDu3btj5cqV0dLSEueff34MHz68ybtL8wkQIECAAAECBAjUJ1DYALBz5864+uqr46yzzoqf/vSn8dWvfjVuu+22LAzU12RrEyBAgAABAgQIEGhegcJeAnT//ffHrFmzYsmSJXHnnXfGjh074umnn27entJyAgQIECBAgAABAg0QKOwZgLVr18acOXO6mjhz5sx48cUX48ILL+yat2LFili3bl32PF0m9K53vaurzIOhERg1atTQvHCJXpVRfmfxyfdJpYzyjfjk+xhDfAYWGHgJv2f5RkX3aTl4aMpvwtCUXnPNNfGRj3wkzj333KwCX/7yl7Od/RtuuKGrQqn8kUceyZ63tbXFCy+80FXmAQECBAgQIECAQPMJ7Jv6xmhZcGUM++PPNF/jf97idOVMXggp7BmAYcOGxf79+7s6bt++fTFy5Miu5+nB7bffHgcOHMjmpTMAGzZs6FHebE8mTZoU6cbprVu3NlvTa27vhAkTYvPmzTUv32wLpjG0a9eu2LZtW7M1veb2GkP5VMZQvk8qNYbyjSZPnhzpPkDvQ/07jR8/PrZs2dL/Ak1eMunQoe29e/bEq028X9je3l7OADBx4sTYtGlT1xBOj6dNm9b1PD1IISH9MxEgQIAAAQIECBAgUJtAYW8Cvvjii2P58uXZ0ciNGzfGk08+Geedd15trbIUAQIECBAgQIAAAQJ9ChT2EqDZs2dHusl33rx50draGnPnzo3p06f32QgzCRAgQIAAAQIECBCoTaCwASDd1HvLLbdk1wCma//TcxMBAgQIECBAgAABAvUJFH6vesyYMfW10NoECBAgQIAAAQIECHQJFPYegK4aekCAAAECBAgQIECAQMMEBICGUdoQAQIECBAgQIAAgeILCADF7yM1JECAAAECBAgQINAwAQGgYZQ2RIAAAQIECBAgQKD4AgJA8ftIDQkQIECAAAECBAg0TEAAaBilDREgQIAAAQIECBAovoAAUPw+UkMCBAgQIECAAAECDRMQABpGaUMECBAgQIAAAQIEii8gABS/j9SQAAECBAgQIECAQMMEBICGUdoQAQIECBAgQIAAgeILCADF7yM1JECAAAECBAgQINAwAQGgYZQ2RIAAAQIECBAgQKD4Am3Fr2LtNWxrq1Rzam/4z5f87//+7xgxYkSMHj36iNdtlhX2798fzT5O8vr6v/7rv6K9vd0YykEyhnJwDhUZQ/k+qXTfvn3eh3KYfvjDH0ZHR4f3oRwj70M5OIeK/uetb4mRJ/9StDfxfuGwYcNykVoOHppyl1BYGoE5c+bE2972tliyZElp6qyixRJ45zvfGbNnz44//MM/LFbF1KY0Au94xzvi0ksvjcWLF5emzipaLIH0d+y9731vfOITnyhWxdSmNAJvfetb44orrohFixaVps6DXVGXAA22uNcjQIAAAQIECBAgMIQCAsAQ4ntpAgQIECBAgAABAoMtMOz/OzQN9ot6vWMj0NraGueee2684Q1vODYvYKuVF0jXDBpDle/mY9rAdI9NGkPTpk07pq9j49UV6HwfMoaq28fHumVpDM2cOTNOPvnkY/1Spd2+ewBK23UqToAAAQIECBAgQODIBVwCdORm1iBAgAABAgQIECBQWgEBoLRd13fFN2/eHI8++mhs2LCh7wXMJVCDwOuvvx4rVqyoYUmLEPiFwMaNG+Phhx+O1atX/2KmRwSOUMD7zxGCWbyHgP2gHhz9PhEA+qUpX8E///M/x0c/+tH4n//5n/j0pz8dS5cuLV8j1HjIBXbt2hXp1qA0nkwEahVYtWpVLFiwINasWRM33nhjPPDAA7WuajkCXQLef7ooPDgKAftBtaMJALVbFXrJ9KUgX/rSl+Izn/lMfOhDH4rbbrstOxOQkrCJQK0C6cvk5s+fH9u2bat1FcsRyATSAYdbb701rrvuurjrrrvi7rvvjj179tAhULOA95+aqSzYh4D9oD5QcmY191fn5sCUrSjd8f7FL36x65sT01GUdBo1/UKYCNQqsGPHjvijP/qj+N///d946KGHal3Nck0ukL7Zdt26dTFjxoxMYsqUKTFq1KhYv359TJ8+vcl1NL9WAe8/tUpZri8B+0F9qfQ/zxmA/m1KVzJ69OiszgcOHIjbb789+zbOiRMnlq4dKjx0AmeffXacc845Q1cBr1xKgVdffTU7+NDS0tJV/3HjxsWmTZu6nntAYCAB7z8DCSkfSMB+0EBCvyh3BuAXFqV6lK7z/5d/+ZeszmPHjo2rrroqe7x79+7sMqCDBw9mR3JL1SiVHVSB/sbQoFbCi1VCIB15O/xsYzor0NHRUYn2aQQBAuURsB9UW18JALU5FW6p4cOHRzrClqbjjjsu+5lOny5evDh+6Zd+KW644YZIf5RNBPoT6GsM9bes+QTyBE444YTYvn17pD+87e3t2aLp6P/UqVPzVlNGgACBhgrYD6qdUwCo3apQS6Zvt7vyyit71OlTn/pUnH766fGRj3ykx3xPCPQl0NcY6ms58wgMJJC+/XfWrFmxbNmyeP/73x+PP/54TJgwIfs30LrKCRAg0CgB+0G1SwoAtVsVesmXXnopnnrqqezfV77yla66fu5zn+u6Ma9rpgcECBBosMDChQu7Pv6ztbU1br755ga/gs0RIECgfwH7Qf3b9FXScuha8YN9FZhHgAABAgSOVGDLli0xfvz4I13N8gQIECAwiAICwCBieykCBAgQIECAAAECQy3gY0CHuge8PgECBAgQIECAAIFBFBAABhHbSxEgQIAAAQIECBAYagEBYKh7wOsTIECAAAECBAgQGEQBAWAQsb0UAQIECBAgQIAAgaEWEACGuge8PgECBAgQIECAAIFBFBAABhHbSxEgQKCqAlu3bj2iph3p8ke0cQsTIECAQK6AAJDLo5AAAQIEBhJ46KGH4tJLLx1osa7yI12+a0UPCBAgQKAhAgJAQxhthAABAs0r8P3vfz+2b99eM8CRLl/zhi1IgAABAjUJCAA1MVmIAAECzS2watWqWLBgQbzzne+MD33oQ/HMM89kIN/5znfi61//eqxbty6bv3nz5mz+gw8+GFdffXW2/JVXXhnLly/PXb65dbWeAAECgysgAAyut1cjQIBA6QReeeWVePvb3x4dHR3ZTn5LS0tcdNFF8YMf/CAmT54cJ598cowaNSouuOCCGDFiRNxxxx3xwQ9+MH75l385rrrqqtixY0dcdtll8eyzz/a5fOlAVJgAAQIlF2gref1VnwABAgSOsUC6ZCftxH/605/OduB/53d+J84444w4ePBg9nPWrFmxdu3aLBykqrz66qvxV3/1V/HhD384q1kKA5MmTYqnnnoqrrvuujh8+WNcfZsnQIAAgcMEBIDDQDwlQIAAgZ4CaYf9V37lV+LUU0+NOXPmxLvf/e5Il/WccMIJPRf8+bMUFDZs2BDf+MY3srMEzz//fOzcuTN27drV5/JmEiBAgMDgCrgEaHC9vRoBAgRKJ3Dcccdl1/z/6Z/+aXYmYOHChdnlPY899lifbVm6dGlMnz490vLpbMBv//ZvZ2cO+lzYTAIECBAYdAEBYNDJvSABAgTKJbBmzZpIN/WmHf9vfvOb2U59ugToc5/7XNaQdE9A55SO9C9evDj+4i/+IrvmP4WBK664IlvnwIEDvZbvXM9PAgQIEBg8AQFg8Ky9EgECBEorMH/+/Pja174W+/fvj/QlXunTftJlQWk6/vjjs0t+UlBIU7o06OWXX460w5/uHfjoRz8au3fv7roEqPvy+/bty9bxHwECBAgMnoAAMHjWXokAAQKlFDjttNPiL//yL2PJkiUxbty47PKec845J3ueGvS2t70thg0bFqeffnqk6/0/+9nPxj/+4z9ml/2ceOKJkS4hmjt3bqSPEj18+ZUrV2bz/EeAAAECgyfQcuhTHA4O3st5JQIECBAos0A6sj9+/PjsI0EPb8eWLVuyss756bsBUgBoa+v78yYOX75zPT8JECBA4NgKCADH1tfWCRAgQIAAAQIECBRKwCVAheoOlSFAgAABAgQIECBwbAUEgGPra+sECBAgQIAAAQIECiUgABSqO1SGAAECBAgQIECAwLEVEACOra+tEyBAgAABAgQIECiUgABQqO5QGQIECBAgQIAAAQLHVkAAOLa+tk6AAAECBAgQIECgUAICQKG6Q2UIECBAgAABAgQIHFsBAeDY+to6AQIECBAgQIAAgUIJ/P90UsT4gkwmJAAAAABJRU5ErkJggg==" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -332,7 +332,7 @@ <h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> F_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)  </code></pre></div>
-<p><img 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" 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>F_hat)) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -353,7 +353,7 @@ <h3>Two numerical vars - SLR</h3>
 <span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
 <span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> slope_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)  </code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwAAAAHgCAYAAAASWgolAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7d0HeBzVtcDxo17dbbnjLjdsirGBUGwTagj1QUJJIEACCR+E8oAAAV6A8CDJC6QRkhACCSShEyChmd5swKa4G/feZctFXdqnc2GkXWm1u5Jmp/7n+2ztzk6593fvzs6ZufdORqRxEiYEEEAAAQQQQAABBBAIhUBmKHJJJhFAAAEEEEAAAQQQQMAIEABQERBAAAEEEEAAAQQQCJEAAUCICpusIoAAAggggAACCCDgWABQV1cnM2fOlIULF6KOAAIIIIAAAggggAACLglkO7HfiooKOf/88+Wwww6TsrIyKS8vl1//+tdO7Jp9IIAAAggggAACCCCAQJSAIwHAa6+9JuPGjZMrr7zS7Prss8+WJUuWyOjRo6OSwksEEEAAAQQQQAABBBBIt4AjAUD//v1l6dKlsnnzZtm9e7fs3LlTevTokTBveqfAb1NmZqZkZGRIfX2935Ke1vSqSV5entTU1EhDQ0Na9+W3jVNn4peYVWeqq6uFkYpjjbKysowJ36VYF/0u5ebmCnUm1kXfZWdnm98lvkuxNvpdysnJkaqqqtgPeEedaaMOpFpnch54SBpGjpD6qUe0saX0z9bjYXFxcZs7ciQAmDRpkrkDoFf+a2tr5dJLL5WSkpKYRF1wwQXy/vvvm3ndu3eXDz74IOZz3vhfoLCw0P+ZIAeOClBnHOUOxM6oM4EoRkczQZ1xlDsQO0tWZ+ru+5NknHayZJ12imv51eb3iSZHAoAHHnjAXPl/5plnZM+ePfKjH/1IBg0aJEcc0RwZfetb35KvfvWrJq16tVj7Cfht0qssehVKr3QzNQuoSZcuXUzZc3ek2UVf6dUnnTQwZmoW0KsseuVC7xhypbvZRV/pVR010YEVmJoF9PhbVFQku3bt4q5RM4t5pXVGj70cf2Nh9PirJ3J+PN+IzYn97/Lz87lrH4dVv0sFBQVJ60xB4zG6vrpGajx8LutIADBnzhw577zzpFu3bubfMcccIx9++GFMAGCd/FveGzdutF765q9WDP0RShZ1+SZDNiVUTTQA0FvzBEexqHqQ1eYulZWVsR+E/J3+MGsAoLfmOdFtXRn0RE6/T0zNAnrhSAMArTOc6Da76Cs9xuixlwsNsS56IqcBgB5/aR4Va6MXYTj+xppY77TeJDvPy28MAPT7lmw5a5vp+KvHxESTI8OA6ug/H3/8sUmH/pjPnj1bDjnkkETp4jMEEEAAAQQQQAABBBBIg4AjAcBJJ51kbpdo2/+LLrrIjP5z6KGHpiE7bBIBBBBAAAEEEEAAAQQSCTjSBEib/tx4443mdpI2B9F/TAgggAACCCCAAAIIIOC8gKNn4tremQkBBBBAAAEEEEAAAQTcE3CkCZB72WPPCCCAAAIIIIAAAgggEC1AABCtwWsEEEAAAQQQQAABBAIuQAAQ8AImewgggAACCCCAAAIIRAsQAERr8BoBBBBAAAEEEEAAgYALEAAEvIDJHgIIIIAAAggggAAC0QIEANEavEYAAQQQQAABBBBAIOACBAABL2CyhwACCCCAAAIIIIBAtAABQLQGrxFAAAEEEEAAAQQQCLgAAUDAC5jsIYAAAggggAACCCAQLUAAEK3BawQQQAABBBBAAAEEAi5AABDwAiZ7CCCAAAIIIIAAAghECxAARGvw2haBZcuWyRlnnCGHHXaY3HvvvbZsk40ggAACCCCAAAII2COQbc9m2AoCXwisX79ezjrrLMnOzpbx48fLHXfcIXV1dXLXXXdBhAACCCCAAAIIIOABAe4AeKAQgpSEm2++WWpra+XJJ5+UBx54QC6++GL55S9/KStXrgxSNskLAggggAACCCDgWwECAN8WnfcSPm/ePHnppZfkmmuukUGDBpkE6usuXbrIrbfe6r0EkyIEEEAAAQQQQCCEAgQAISz0dGX5V7/6lTnx1yZA1lRcXCyXXXaZPPLII7Jx40ZrNn8RQAABBBBAAAEEXBIgAHAJPmi73bZtm7zyyivy3e9+V3JycmKyd84550hGRoY8++yzMfN5gwACCCCAAAIIIOC8AAGA8+aB3OPTTz9t8nX66ae3yl+vXr3kq1/9qljLtFqAGQgggAACCCCAAAKOCRAAOEYd7B09/vjjMn36dOndu3fcjGqzoI8//ljWrl0b93NmIoAAAggggAACCDgjQADgjHOg97J06VJZuHChGfu/rYyeeuqpkpWVZToJt7UM8xFAAAEEEEAAAQTSL0AAkH7jwO9BR/7Jzc01zXzaymz37t3lwAMPlHfeeaetRZiPAAIIIIAAAggg4IAAAYADyEHfxcsvvyyHH364FBUVJczq1KlTZebMmebBYAkX5EMEEEAAAQQQQACBtAkQAKSNNhwb3rJli2nbf/zxxyfN8JFHHil79+6VTz/9NOmyLIAAAggggAACCCCQHgECgPS4hmarM2bMMHk99thjk+b5oIMOkoKCApoBJZViAQQQQAABBBBAIH0CBADpsw3Fll9//XWZOHGilJSUJM2v9hOYPHmyzJo1K+myLIAAAggggAACCCCQHgECgPS4hmKrtbW15mr+tGnTUs6v3gX45JNPpKGhIeV1WBABBBBAAAEEEEDAPgECAPssQ7el2bNny549e+Soo45KOe+TJk0y63z++ecpr8OCCCCAAAIIIIAAAvYJEADYZxm6LWnzn27duome1Kc6HXDAAWbROXPmpLoKyyGAAAIIIIAAAgjYKEAAYCNm2Db1xhtviI7sow/4SnXS5wGMGDHCjByU6joshwACCCCAAAIIIGCfAAGAfZah2tLmzZvN03+nT5/e7nzrHQNtPsSEAAIIIIAAAggg4LwAAYDz5oHY45tvvmny0Z4OwFbGtRnQsmXLpKKiwprFXwQQQAABBBBAAAGHBAgAHIIO2m60/f/YsWOlX79+7c7avvvuK5FIRBYsWNDudVkBAQQQQAABBBBAoHMCBACd8wvl2vX19fL2229LR5r/KJgGDpmZmTJv3rxQ+pFpBBBAAAEEEEDATQECADf1fbpvbb9fXl4uRx99dIdyUFhYaDoCz58/v0PrsxICCCCAAAIIIIBAxwUIADpuF9o1X3vtNenatavoQ706Ok2YMEEIADqqx3oIIIAAAggggEDHBQgAOm4X2jVfffVV0c6/2dnZHTbQAGDx4sVSU1PT4W2wIgIIIIAAAggggED7BQgA2m8W6jXWrVtnTtw72vzHwtOOwHV1dbJkyRJrFn8RQAABBBBAAAEEHBAgAHAAOUi7ePnll00H3o52ALYsxo0bZ14uWrTImsVfBBBAAAEEEEAAAQcECAAcQA7SLl566SWZMmWK9OrVq1PZ6tGjh/Tt21cIADrFyMoIIIAAAggggEC7BQgA2k0W3hV27Nghs2bNkhNOOMEWBB0OVPsBMCGAAAIIIIAAAgg4J0AA4Jy17/c0Y8YM0WcA2BUAjBkzhjsAvq8VZAABBBBAAAEE/CZAAOC3EnMxvc8//7xMnDhRBg0aZEsq9A7Ali1bpKyszJbtsREEEEAAAQQQQACB5AIEAMmNWKJRYOfOnebpvyeffLJtHnoHQCeaAdlGyoYQQAABBBBAAIGkAgQASYlYQAVefPFFqa2tFTsDgFGjRpkRhQgAqGMIIIAAAggggIBzAh1/klOa06hPmvXblJWVJRkZGZ16QJZX8/yf//xHJk+eLNbwne1Jp5roVFhYKPn5+U2rahmPGDFCVqxYYZ4s3PRBiF5YdSYnJydEuU6e1czML65NFBcXS0NDQ/IVQrSEPoAvEolIXl5eiHKdPKv6XdJJ64z6MDUL6PFF//FdajbRV1ad6dKlS+wHvDP1RY/DfJdiK4P1ANRk56hql5uXK1kunstqn81Ek2cDgL179yZKtyc/y81tLOzGH6HKykpPpq+jidq6dau8+eabcuutt0pHykVN9OS/qqrK3EWITofeBViwYEGHthu9Hb++1pM4DZDUhqlZQE9WCgoKzHdJHxjH1CygQbSeyPEU7WYTfaXHX7WpqKjgRDeWxhx/9Q6u/mNqFtDjr1VnONFtdtFXRUVF5ncp2Ulk7FrBf6e/S1pvkp0LZTdehKitrZNKF89lk11Y9GwA4MdKp2nWkzk/pj3R1/aZZ54xH5900kkdypt1B0BPWlrajBw5UmbOnNlqfqL0BOkzNQlinelsGVl3ALS+tKwznd2239fXE5V43yW/56uz6VcTnbBpLWmZ8F2KtbFO+tXFeh27RHjfcZyJX/bWcSbpd6nxOB2Jc84Tf6vpmWvdrWhr6/QBaEuG+U0CGgAcfvjhUlJS0jTPrhelpaWizxfQuwxMCCCAAAIIIIAAAukXIABIv7Gv97BmzRr5+OOP5bTTTktLPkaPHm22+/nnn6dl+2wUAQQQQAABBBBAIFaAACDWg3ctBJ577jnTGciuh3+12LzpBKxNYAgAWsrwHgEEEEAAAQQQSI8AAUB6XAOzVX3417Rp09I2So92wBoyZAgBQGBqDBlBAAEEEEAAAa8LEAB4vYRcTN/KlStl3rx5to79Hy872g+AOwDxZJiHAAIIIIAAAgjYL0AAYL9pYLaoD//SYaSOPfbYtOaJACCtvGwcAQQQQAABBBCIESAAiOHgTbTAq6++Kocccoik+yEp+iyA7du3y86dO6N3z2sEEEAAAQQQQACBNAgQAKQBNQibLC8vl48++kiOPvrotGdHnwWg09KlS9O+L3aAAAIIIIAAAgiEXYAAIOw1oI3865N/9UEXTgQAegdAJwKANgqD2QgggAACCCCAgI0CBAA2YgZpU6+//roZonPYsGFpz1ZxcbH069ePACDt0uwAAQQQQAABBBAQIQCgFsQVeP/99+Wwww6L+1k6ZupdAO4ApEOWbSKAAAIIIIAAArECBACxHrxrFFi3bp2sX79eDj30UMc8NABYtmyZY/tjRwgggAACCCCAQFgFCADCWvIJ8j1z5kzzqZMBgHYEXrt2rVRWViZIGR8hgAACCCCAAAIIdFaAAKCzggFcXwOA4cOHS0lJiWO50zsAkUhEVqxY4dg+2RECCCCAAAIIIBBGAQKAMJZ6kjzPmjXLjP+fZDFbP7ZGAqIZkK2sbAwBBBBAAAEEEGglQADQiiTcM3bs2CGrVq2Sgw46yFEIvdugDxyjI7Cj7OwMAQQQQAABBEIoQAAQwkJPlOW5c+eajydOnJhosbR8pv0AuAOQFlo2igACCCCAAAIINAkQADRR8EIFNADIz8+X0tJSx0EYCtRxcnaIAAIIIIAAAiEUIAAIYaEnyrIGAOPGjZPs7OxEi6XlM70DoJ2AGxoa0rJ9NooAAggggAACCCDAg8CoAy0EPvvsM3Gj+Y8mQ+8AVFdXm+cQtEgWbxFAAAEEEEAAAQRsEuAOgE2QQdhMWVmZOfneb7/9XMmO3gHQiY7ArvCzUwQQQAABBBAIiQABQEgKOpVsLliwwCw2YcKEVBa3fZkhQ4ZITk4OAYDtsmwQAQQQQAABBBBoFiAAaLYI/avPP/9cMjMzZcSIEa5YaL+DYcOGEQC4os9OEUAAAQQQQCAsAgQAYSnpFPK5ZMkScwKel5eXwtLpWYSRgNLjylYRQAABBBBAAAFLgADAkuCv6B0AN4b/jKYnAIjW4DUCCCCAAAIIIGC/AAGA/aa+3aJXAoDy8nLZunWrbx1JOAIIIIAAAggg4GUBAgAvl46DaduyZYvs3LlTRo8e7eBeW+9K7wDoxEhArW2YgwACCCCAAAII2CFAAGCHYgC2oVf/dXK7CZB2QM7IyCAACECdIgsIIIAAAggg4E0BAgBvlovjqdIOwG6OAGRluKCgQAYNGkQAYIHwFwEEEEAAAQQQsFmAAMBmUL9ubvny5bLPPvuImyMAWXbaDMi6I2HN4y8CCCCAAAIIIICAPQIEAPY4+n4rq1atkqFDh3oiH9oPgQDAE0VBIhBAAAEEEEAggAIEAAEs1I5kaeXKleYZAB1Z1+51tB+CdkrW0YCYEEAAAQQQQAABBOwVIACw19OXW6urq5N169Z5KgBQSO4C+LI6kWgEEEAAAQQQ8LgAAYDHC8iJ5K1Zs0bq6+s9EwBYQ4Fqx2QmBBBAAAEEEEAAAXsFCADs9fTl1rT9v05e6QNQXFwsAwcOZCQgUyr8hwACCCCAAAII2CtAAGCvpy+3pu3/dQhQHQXIKxMjAXmlJEgHAggggAACCARNgAAgaCXagfxoAKBj7+fk5HRg7fSsoh2BaQKUHlu2igACCCCAAALhFiAACHf5m9x7aQQgqzjGjBkjmzZtYiQgC4S/CCCAAAIIIICATQIEADZB+nkz2gl4yJAhnsqCBgA6LV682FPpIjEIIIAAAggggIDfBQgA/F6CNqR//fr1MnjwYBu2ZN8mtAmQTjQDss+ULSGAAAIIIIAAAipAABDyerB161apqqoyfQC8RFFYWGg6JXMHwEulQloQQAABBBBAIAgCBABBKMVO5GHt2rVmba/dAdBEaTMgAoBOFC6rIoAAAggggAACcQQIAOKghGmW1wMAmgCFqTaSVwQQQAABBBBwQoAAwAllD+9j3bp1kpeXJ7179/ZcKvUOwI4dO2TLli2eSxsJQgABBBBAAAEE/CpAAODXkrMp3XoHQJ8BkJGRYdMW7duMNRLQokWL7NsoW0IAAQQQQAABBEIu4FgAUF1dLe+++6689957UltbG3J272RfAwAvtv9XoZEjR5qHkxEAeKe+kBIEEEAAAQQQ8L+AIwFARUWFXHLJJU0BwLe+9S3RgIDJfQFtAqR3ALw4ZWdniw4HumDBAi8mjzQhgAACCCCAAAK+FHAkAHjuuedk9OjRcv3118t1110nxx9/POO7e6S6eDkAUKKxY8cKdwA8UllIBgIIIIAAAggEQiDbiVx8+OGHcuqpp8qsWbOkvr5ezj//fMnMjI09brjhBvn4449Ncrp27Sr//Oc/nUiarfuw2tEXFRXZut10bWzbtm1SWVkp48ePlz59+qRrN03b7d69u0Qikab3qbyYPHmyPPvss6Lr5uTkpLKKr5ax6kxxcbGv0u1UYnv06OHUrnyzH6vOtPe75JsMdjChlkvPnj07uIXgrqY2+rtEnYktY6vOeHEQjNiUOv9Oz9Hy8/Od37HH92jVmaTnTFlZos8zKnTg3KotsmQtbRwJAPRhU3pCr2Da/v/ee++VBx54QAoKCprSve+++xosnaFoyRLetKKHXugXRv/V1dV5KFVtJ2XFihXmw759+6bVW78w2pynpqZGGhoa2k5QnE+0I7DWmXnz5plAJc4ivp6V1XiQUB+/1BmnsPV7pHVGy769dcapNLq1H3XREzm9mMLULKDfJf2nxxlOdJtd9JVePNH6wncp1sWqM34834jNif3vqDPxTfX4q/UmWZ3JNcfoOql3sbl7st8IRwIATcSoUaPk6quvNqI33nijvPXWW6YpkEV87rnnWi/N340bN8a898Ob3Nxcc9KifR78MH3++ecmmd26dZNdu3alLcn6hdGgTl30x7k907Bhw8ziehfJq52V25OflsvqFRYNAPRODFOzgP746AWCPXv2EBw1s5hX+l3SY2qyH6AWqwX+rQ5nrN8nrTPJfvgCj9Eig3r1X4+9DMARC6PHGK0zu3fvJmiMpZEuXbqY3yUuTsXC6PFXjzXJzpl6Nl7srKmukb1pPLeKTVnrd5rORFNsO5xES3bis5KSEhk3blzTFrRj58KFC5ve88IdASvI6tevnzsJSGGvemtW/1FfUsBiEQQQQAABBBBAIAUBRwKAI488Ul599VVz+1GvQuhwoBMnTkwheSySToENGzaYZlleb1uvzcMYCSidNYFtI4AAAggggECYBBwJAE466STTiVOH/zz77LPN3YCjjz46TM6ezKsGAAMGDPBk2qITpZ2U58+fHz2L1wgggAACCCCAAAIdFHCkD4BeYb7ppptMG3B6lnewpNKwml8CAL0DUFZWJps2bRIvN1dKQxGxSQQQQAABBBBAwHYBR+4AWKnWzhMMK2VpuP9X+wD45Q6AanEXwP06QwoQQAABBBBAwP8CjgYA/ucKVg40AOjfv7/nMzV8+HAzIgz9ADxfVCQQAQQQQAABBHwgQADgg0JKRxK3b99uhoXzwx0AbTamTwTmDkA6agLbRAABBBBAAIGwCRAAhK3Ev8zv+vXrzSs/3AHQhGo/AAKALwuPPwgggAACCCCAQCcECAA6gefnVa1nAAwcONAX2ZgwYYKsXr066cM3fJEZEokAAggggAACCLgoQADgIr6bu7YCgL59+7qZjJT3rQGATvQDSJmMBRFAAAEEEEAAgbgCBABxWYI/U4fU7NWrl3j9IWBWSYwePVqys7Nl3rx51iz+IoAAAggggAACCHRAgACgA2hBWEUDAL+0/1fvvLw80SBg7ty5QeAnDwgggAACCCCAgGsCBACu0bu7482bN4tfmv9YUnQEtiT4iwACCCCAAAIIdFyAAKDjdr5e049P1dV+AMuWLZPKykpf25N4BBBAAAEEEEDATQECADf1Xdy3XwOAhoYGWbhwoYty7BoBBBBAAAEEEPC3AAGAv8uvQ6mvqqqS8vJy3zUBGj9+vGRkZNARuEOlzkoIIIAAAggggMAXAgQAIawJ2v5fp379+vkq94WFhTJixAgCAF+VGolFAAEEEEAAAa8JEAB4rUQcSI/1DAC/BQBKM3HiRAIAB+oIu0AAAQQQQACB4AoQAAS3bNvMmV/vAGiGtCPw4sWLpaamps388QECCCCAAAIIIIBA2wIEAG3bBPYTDQD0oVr6IDC/TRoA1NXVmSDAb2knvQgggAACCCCAgBcECAC8UAoOp0GbAJWUlJgOtQ7vutO702cB6MQTgTtNyQYQQAABBBBAIKQCBAAhLHi9A+DH9v9aVF27dpUhQ4YQAISw3pJlBBBAAAEEELBHgADAHkdfbUUDAL89BTgamCcCR2vwGgEEEEAAAQQQaJ8AAUD7vAKx9JYtW0wTIL9mRvsBLFq0SOrr6/2aBdKNAAIIIIAAAgi4JkAA4Bq9ezv2+x0ADQAqKytl+fLl7iGyZwQQQAABBBBAwKcCBAA+LbiOJruiokL27Nnj+zsAmn86Ane0FrAeAggggAACCIRZgAAgZKWvzX908nMfgN69e5v0EwCErPKSXQQQQAABBBCwRYAAwBZG/2zECgD69Onjn0THSak2A5o/f36cT5iFAAIIIIAAAgggkEiAACCRTgA/swIAP98B0GIZP368LFiwIIAlRJYQQAABBBBAAIH0ChAApNfXc1vXDsAZGRmizWj8POlQoOXl5bJu3To/Z4O0I4AAAggggAACjgsQADhO7u4O9Q6AnvxnZWW5m5BO7p0nAncSkNURQAABBBBAILQCBAAhK3q/DwFqFdc+++wjxcXFNAOyQPiLAAIIIIAAAgikKEAAkCJUUBbTOwB+7wCsZaHNmOgHEJRaST4QQAABBBBAwEkBAgAntT2wLw0A/N4B2GLUZkCMBGRp8BcBBBBAAAEEEEhNgAAgNafALKVNgEpKSgKRH70DsH79etm5c2cg8kMmEEAAAQQQQAABJwQIAJxQ9sg+6uvrpaysLDB3AMaNG2dkFy1a5BFhkoEAAggggAACCHhfgADA+2VkWwq3bdsmkUgkMHcARo8ebUYz4nkAtlURNoQAAggggAACIRAgAAhBIVtZtB4C5vdnAFj5ycvLkxEjRsjChQutWfxFAAEEEEAAAQQQSCJAAJAEKEgfb9261WQnKH0ANDOMBBSkGkpeEEAAAQQQQMAJAQIAJ5Q9sg/rDkCQAgDtB7BkyRKpq6vziDLJQAABBBBAAAEEvC1AAODt8rE1dRoAFBQUSFFRka3bdXNjGgDU1NTI8uXL3UwG+0YAAQQQQAABBHwjQADgm6LqfEK1E3CQrv6riDYB0ol+AIaB/xBAAAEEEEAAgaQCBABJiYKzgN4BCMJTgKNLRAOanj17CkOBRqvwGgEEEEAAAQQQaFuAAKBtm8B9EsQAQAtJmwFxByBw1ZUMIYAAAggggECaBAgA0gTrxc3qKEBBawKkzmPHjuUOgBcrHGlCAAEEEEAAAU8KEAB4sljSkyi9AxDEAEDvAGzcuFF27NiRHji2igACCCCAAAIIBEiAACBAhZkoK1VVVbJ79+7A9QHQPOsdAJ0WL15s/vIfAggggAACCCCAQNsCBABt2wTqEx0BSKcg3gEoLS2VzMxM+gEEqsaSGQQQQAABBBBIl0B2ujbc2e0WFhZ2dhOOr5+dnW1ORB3fcQo73LVrl1lqn332Eadt9eRcp7y8PFEjuyfNz8iRI2Xp0qWO562zecnJyTGbyMjI6OymArV+VlaWyU9+fr40NDQEKm+dzUxubq4xsYw6u72grG8dW7TORCKRoGTLlnxondHjsHW8sWWjAdiI5aHPx2GKFVAb/R5x/I110e+STsnOozK+/L4lWy52686+s/9szKb019fX27Ql5zZjneh6Me3aRl6nXr16idPps36M9UCSrn1rP4D58+enbfvpqkXWSVy6XNKV7nRv1wqI0lln0p2HdG1fTXBprWsdfy2f1kuEd45lwnEmtg5Yx1/1sX6nYpcI7zvqTPyyVxedkn6Xvgyeki4Xfze2zLWOiW1tzLMBQHV1dVtp9ux8PYDoVSgvpt0KALp27ep4+qwrc7W1teapvekowNGjR8vLL78s2tfBOnlMx37s3qamVf95sc7Yndf2bM86yOpTnuvq6tqzauCX1ZMW/VGhzsQvaq0zbv7oxk+Vu3P1GKwuegxmahawTpD0u0QA0Oyir/RKN8ffWBN9ZwWNyY6/RY3ng24fp7XVRaKJPgCJdAL0mQ4B2qVLF9MMJ0DZasrKmDFjpLKyUlavXt00jxcIIIAAAggggAACrQUIAFqbBHKOBgBBewpwdEFpEyCdeCBYtAqvEUAAAQQQQACB1gIEAK1NAjkn6AHAoEGDpKioiKFAA1l7yRQCCCCAAAII2ClAAGCnpoe3pcOABvkOgLaj54nAHq6AJA0BBBBAAAEEPCNAAOCZokhvQvQpwEEOAFRP+wEsWrQovZBsHQEEEEAAAQQQ8LkAAYDPCzDV5Ae9CZA66B2AlStXms7AqbqwHAIIIIAAAgggEDYBAoAQlLgOjblnzx7p3bt3oHOrAYAO5fb5558HOp9kDgEEEEAAAQQQ6IwAAUBn9Hyyrrb/1ykMTYA0nzQDUgUmBBBAAAEEEEAgvgABQHyXQM3V5j86BT0A6N69u/Tv358AIFC1l8wggAACCCCAgN0CBAB2i3pwe2EJAJReOwIvXrzYg6VAkhBAAAEEEEAAAW8IEAB4oxzSmgorAAh6HwBF1AeC8TCwtFYnNo4AAggggAACPhcgAPB5AaaSfA0AiouLpaCgIJXFfb2M3gHYvn27WP0efJ0ZEo8AAggggAACCKRBgAAgDahe22TQHwIW7a13AHTiLkC0Cq8RQAABBBBAAIFmAQKAZovAvtI7AGFo/qMFOHLkSMnOzqYjcGBrMxlDAAEEEEAAgc4KEAB0VtAH64cpAMjJyTFBAEOB+qBikkQEEEAAAQQQcEWAAMAVdmd3GqYmQCqrzYAIAJytY+wNAQQQQAABBPwjQADgn7LqcEo1AAhLEyBF0icC69OA6+vrO2zGiggggAACCCCAQFAFCACCWrJf5qu2tlZ27twZ+IeARRej3gGorq6WFStWRM/mNQIIIIAAAggggECjAAFAwKuBNRxm0J8CHF2MOhSoTjQDilbhNQIIIIAAAggg8IUAAUDAa4IVAISpCVD//v2lR48eDAUa8LpN9hBAAAEEEECgYwIEAB1z881aYQwAtHC0GdCCBQt8U04kFAEEEEAAAQQQcEqAAMApaZf2o0OA6hSmJkCaXw0AeBiYSjAhgAACCCCAAAKxAgQAsR6Be6d3APLy8qRLly6By1uiDGkAsHHjRtmxY0eixfgMAQQQQAABBBAInQABQMCLPEwPAYsuSg0AdKIjcLQKrxFAAAEEEEAAAUYBCnwdCGsAUFpaKllZWfQDCHwNJ4MIIIAAAggg0F4B7gC0V8xny2/fvj107f+1iLTZ04gRI7gD4LP6SnIRQAABBBBAIP0CBADpN3Z1D3oHIGwdgC3wfffdlzsAFgZ/EUAAAQQQQACBLwUIAAJeFcLaBEiLdfz48bJkyRKpq6sLeCmTPQQQQAABBBBAIHUBAoDUrXy3ZCQSkbKyMgnTQ8CiC0kDgJqaGlm6dGn0bF4jgAACCCCAAAKhFiAACHDx68l/fX19qAMALV4eCBbgSk7WEEAAAQQQQKDdAgQA7SbzzwraAVinsN4B6NWrl/Tr10/mz5/vn0IjpQgggAACCCCAQJoFCADSDOzm5vUhYDqFtROw5l2bAXEHQCWYEEAAAQQQQACBLwQIAAJcE7QDsE5hvQOgeScAUAUmBBBAAAEEEECgWYAAoNkicK/0DkBGRob07NkzcHlLNUMTJkyQnTt3yrp161JdheUQQAABBBBAAIFACxAABLh4NQDQk//MzPAWsz4LQKd58+YFuKTJGgIIIIAAAgggkLpAymeGu3btSn2rLOkJAQ0Awtz8RwthyJAh0qVLFzoCe6JGkggEEEAAAQQQ8IJAygFAaWmpfOMb35AXXnjBDC3phcSThsQCGgCEuQOwpaN3AbgDYGnwFwEEEEAAAQTCLpByAPDEE09I165d5eyzz5ZBgwbJtddey+gqHq89YX4KcHTRaADAUKDRIrxGAAEEEEAAgTALpBwAHHHEEfLnP/9ZNm3aJPfcc48sWrRIJk2aJJMnT5Z7771XysvLw+zoybzTBOiLYtGOwFpvrecieLKwSBQCCCCAAAIIIOCQQMoBgJWegoICOeuss+SWW26R7373u/Lpp5/KddddJwMHDpRrrrlGamtrrUX567IAAcAXBaABgE5z5879Ygb/I4AAAggggAACIRZoVwCwbNky+clPfiKjRo2Sr3zlK7Jq1Sp59NFHZceOHfLWW2/Jv/71L/N5iD09k/WKigrRf2HvBKwFMnLkSMnPz6cfgGdqJwlBAAEEEEAAATcFslPd+VFHHSVvvPGGjB07Vr73ve/Jt7/9benfv3/T6toc6LTTTqOtdZOIuy+s5i50AhbJysqScePGEQC4WyXZOwIIIIAAAgh4RCDlAODAAw+UO++8Uw4++OA2k/7DH/5QCgsL2/ycD5wT4CnAsdbaDEgDWCYEEEAAAQQQQCDsAik3Afrss89k/PjxrbweeughOeecc8z8wYMHS69evVotwwznBbT9v040AfrCXgOANWvW0Fn9Cw7+RwABBBBAAIEQCyS8A/DBBx/IjBkzDM+7774rd911l2lLbXnV19ebdv/aJ4DJWwIEALHlYXUE1ucBHH744bEf8g4BBBBAAAEEEAiRQMIAQDtPXn311VJdXW1G9/nPf/5j2lNbPjk5OTJ06FC58cYbrVlJ/y5ZssQsM3r06KTLskDHBbQJkDbH0lGbmETGjBkjWl8JAKgNCCCAAAIIIBB2gYQBgDbnee+994zRySefLP/4xz+kuLi4w2Y7d+40Q4ZqZ2ECgA4zprSidgKm+U8zlZ78awd2ngjcbMIrBBBAAAEEEAinQMIAQJsA6Qm/tv3/7//+b9H38aa+ffuKPm012fTzn/9cevbsmWwxPrdBQJsAMQJQLKQ2A5o1a1bsTN4hgAACCCCAAAIhE0gYAFx00UUyceJEc+X/lFNOabMD5ZlnnimPP/54Qrrnn39eevToIYMGDYq73K9//WtZuHCh+UyDjjvuuCPucl6emZmZKRkZGZKXl+d6MvXZDDpMq5q7PamJTl26dJGGhgbXkqMjWP397383TYE6cyfLzgxYdUafU8DULGDVma5du0okEmn+gFemGaaaMOJabGXQ75JO3bp1o87E0pg6o8cYN4+/LZLkibdWnenevbsn0uOlRGRnZ5vfSo6/saWiw4rrlOzcKjMzy/SZzXXxHKyuri428S3eJQwAPvnkE3NCq+ts2bKlxarNb60vUfOc2Ffr1q2Tp59+Wn7/+9/Lgw8+GPvhl+80oVZi9a91AhB3YY/P9ELatQ/AiBEjPOEY7RH92uli1KFsddIRrbzSEdjysP46beLV/Vke1l+vptPNdGETqx/tEf06dqlwvlMPPZHDJbb8LQ/rb+ynvMOl7TqQ1OaL656e/s4lDAC03bQ1aTT4yiuviD4QLDc3V/7v//5P3nnnHTn11FPlggsusBZr9VdP5vVq/jXXXJOwQ6o2MYqeNm7cGP3WF6/VRZ30CbxuT5s3bzZX3MvKytxOijHR5ki7d++Wmpoa19IzYMAAcyVM+7Xog8G8MOlVOT2QVFZWeiE5nkmDHnu0D0t5eXnThQHPJM7lhOiVfx2BTQdnYGoW0Duv2sRU64z6MDULFBUVmWNvbW1t80xemXMSvfqvd8y50h1bIfSOvf4uWRdmYz8N7zs9/updxmTnVj31GF1VJXtdPAdL1holYQAQXcQ//vGP5Re/+IUsX77cdAy+/vrr5cQTTxTr4V/f/OY3oxdver1q1SpZtGiRXHXVVWaeHoD0hEfbqGtQwGS/gN7m1cpJJ+BYWz3ZLi0tpSNwLAvvEEAAAQQQQCBkAikHAPfdd5+88MILMmTIEPn+979vTv6fffZZueeee+TRRx+VtgIAHUr0zTffbGLVZkAaQX3nO99pmscLewX05F+vZhAAtHbVjsDaBIgJAQQQQAABBBAIq0BKTwLWE0ptvjFt2jRzS0hP6HUoT520nbn10KmwInot31Z5MApQ65LRAGDp0qVS1XhrjgkBBBBAAAEEEAijQEp3ALS3s1611zb/2jZf23Eff/zxpm3YY489JtOnT0/Z7tJLL015WRbsmIAVAHAHoLWfDlerTaS0WdoBBxzQegHmIIAAAggggAACARdIKQDQNvvaB+C4444zJ0+XX3659OvXz3QA1mcD/OhHPwo4k7+ypyMA6aQPcmOKFdBnWug0f/58AoBYGt4hgAACCCCAQEgEUgoA1EI7/Z5wwgmm6cSUKVMMzxVXXCH6WkcYYPKOgAYAOlYtD11rXSY6/v+wYcPoCNyahjkIIIAAAgggEBKBlAMA9RgzZozo8JLLli0zPPpQrw0bNpinBetDp5i8IbB9+3Zz9T/pOLXeSK7jqdBmQHoHgAkBBBBAAAEEEAijQEqdgBXmqaeeMs1+dBQgHUox+p/eCWDyjoDeAaD9f9vloQHA4sWLGSu8bSI+QQABBBBAAIEAC6R8B0A77+pDv37wgx+0alqizSqYvCOgnYAZAajt8tAAQEcB0mdaaCDLhAACCCCAAAIIhEkgpQBg165dsmXLFvnZz34mJSUlYfLxZV71DoAOz8oUX8DqCLxgwQICgPhEzEUAAQQQQACBAAuk1ASoa9eupuPk7NmzA0wRnKxxByBxWWoQqyMkLVy4MPGCfIoAAggggAACCARQIKU7AJrvn/70p3LVVVfJunXrTDCQnd28qp5QWVdVA2jkuyxpJ2D6ACQuNq2vegeACQEEEEAAAQQQCJtA81l8kpxrH4Dy8nK55JJLWi155plnyuOPP95qPjOcF9izZ49p304AkNh+3LhxpmN74qX4FAEEEEAAAQQQCJ5AygGADv8ZiUTiCuiY80zeELAeAkYn4MTloXcA/vCHP4g2lyJYSmzFpwgggAACCCAQLIGU+gBolvPy8iQ/Pz/uv5ycnGCp+Dg3ekKrEye1iQtR7wDoRDOgxE58igACCCCAAALBE0g5ANCsV1ZWyqOPPiq33XabeQCYdgpuaGgInoqPc2TdASAASFyII0eOFA1c9XkATAgggAACCCCAQJgEUm4CtGjRIjnhhBNk06ZN5qT/pJNOkptvvtn0C3j66afNQ8LCBOfVvGoHYJ0IABKXkJ7861CpWq+ZEEAAAQQQQACBMAmkfAfgoosukkMPPdQ8D2Dw4MHG6KGHHpLMzEz55z//GSYzT+dVmwDpsK25ubmeTqcXEjd27FjuAHihIEgDAggggAACCDgqkFIAUFFRIR9++KFp+qMnl9bUt29fueKKK+SFF16wZvHXZQFtAsTV/9QKYcyYMfL555/TjC01LpZCAAEEEEAAgYAIpBQA6Jj/eqV/7969rbKtnSj1MyZvCGgAwAhAqZWFBgBVVVWyatWq1FZgKQQQQAABBBBAIAACKZ25a3OSY4891jwI7KOPPjLZ1rsC//jHP+S+++6T448/PgAUwcgCw1qmXo7aBEgnOgKnbsaSCCCAAAIIIOB/gZQCAM3mn/70J9GT/ilTpsjKlStl+vTpcu6558pRRx0lP/zhD/0vEZAccAcg9YIcNGiQFBUVEQCkTsaSCCCAAAIIIBAAgZRHARowYIDMmjVL3nnnHXPCpHcF9t9/f/MvAA6ByQIBQPuKUpsBcQegfWYsjQACCCCAAAL+FkgYAGhzkrq6upgclpaWiv6zJh0WVB8Q1r17d2sWf10SqK6ult27d9MHoB3+o0ePFqtZWztWY1EEEEAAAQQQQMC3AgkDgGnTpqX0pNQzzzxTHn/8cd8iBCXhPAW4/SWpwazWXQ10tbM7EwIIIIAAAgggEHSBhGc8Tz31lBklRREee+wx0fc/+9nPZL/99jMPBHv55Zfl/vvvN0OBBh3KD/mzAgBGAUq9tEaNGmVO/rVfi75mQgABBBBAAAEEgi6QMADQ5hE6RSIROfroo+Xhhx9uGvFn2LBh5sFg9fX1cs8998hhhx0WdCvP50/b/+tEAJB6UVnN2fR5AAQAqbuxJAIIIIAAAgj4VyClUYAqKyulvLw87gmSPgysrKzMvwIBSrkVAPAgsNQLdeDAgWYkoCVLlqS+EksigAACCCCAAAI+FkgpACgsLDRX+6+99loTCGh+NSh48cUX5a677pITTzzRxwTBSboGAAUFBaLlxZS6gN4F0DsATAgggAACCCCAQBgEUgoAFOIvf/mLLF26VEpKSmT48OHSs2dP+drXvmb+8RwAb1QV7QNA85/2lwUBQPvNWAMBBBBAAAEE/CuQsA9AdLZGjBghH374ofk3d+5c6dq1q0yePFnGjRsXvRivXRTgGQAdw9cA4JlnnhHtz5KVldWxjbAWAggggAACCCDgE4GUAwDNjzYvmTp1qvnnk/yFKpkEAB0r7pEjR0pNTY2sWbNGtHM7EwIIIIAAAgggEGSBlJsABRkhKHnTAIAOwO0vTQ0AdFq2bFn7V2YNBBBAAAEEEEDAZwIEAD4rsETJpQ9AIp22P9tnn30kNzeXAKBtIj5BAAEEEEAAgQAJEAAEpDBra2tlx44dppN2QLLkWDa03b82/eEOgGPk7AgBBBBAAAEEXBQgAHAR385da/MfnRgFqGOq2gyIAKBjdqyFAAIIIIAAAv4SIADwV3m1mVorANBhWpnaL6ABgA5zy4QAAggggAACCARdgAAgICVMANC5gtQAYOfOnbJ9+/bObYi1EUAAAQQQQAABjwsQAHi8gFJN3pYtW8yijAKUqljscqNGjTIzuAsQ68I7BBBAAAEEEAieAAFAQMpU7wAUFRVJYWFhQHLkbDb0QXc6LV++3NkdszcEEEAAAQQQQMBhAQIAh8HTtTu9A0D7/47ravDUt29fAoCOE7ImAggggAACCPhEgADAJwWVLJl6B4AAIJlS4s/1LgB3ABIb8SkCCCCAAAII+F+AAMD/ZWhyoAEA7f87V5gaAKxYsaJzG2FtBBBAAAEEEEDA4wIEAB4voFSTRxOgVKXaXk4DgNWrV0tdXV3bC/EJAggggAACCCDgcwECAJ8XoJV8AgBLouN/NQDQk38NApgQQAABBBBAAIGgChAABKBkKysrZe/evTwFuJNlqQGATvQD6CQkqyOAAAIIIICApwUIADxdPKklznoIWJ8+fVJbgaXiCgwePFhycnLoBxBXh5kIIIAAAgggEBQBAoAAlOTmzZtNLnQYS6aOC2RlZcnQoUO5A9BxQtZEAAEEEEAAAR8IZHs1jbm5uV5NWpvp0qvHehLpdNrLyspMmgYOHOj4vtvEiPpATXTKzvZsdWtKrT4ReOXKlY45WiZO15mmDHv0heWi36nMTK5TRBeTfp8yMjIkEolEzw796+g6Yx1zQo/yJYDaaH3ResPULGDVEz3+8n1qdtFXasPxN9ZE31nHmWS/2fpdU8Nky7Xeg31zkv12evaMTCue3yYtbOtL42TatQmQVrYBAwY0VU4n959sX1Yl1C+O13+ANAB49NFHzYEvWb7s+Fzri5rw4xOrqS46aZ2x6k/sEuF9pzaYtC5/64dZ//J9ivXR+uKH429sqtP/LrrOpH9v/tqDVWc41sSWm+WR9By18Xddl026XOzmbX2X7Djo2QBAO7X6bdJITw8oFRUVjiZ93bp10qtXL6murjb/HN15CjtTk+LiYqmqqpKampoU1nBvEe0HoE2q9J+mOd1Tfn6+CQC0IzdTs4AeNPXpzOrCsKzNLvqqsLBQ6uvrPfldj02ps+/y8vKMjdYZ9WGKFdBjb21tbezMkL8rKCgQ/ae/2clOlsJGpSevHH9bl7oef7XOJDtHzWtoMN+3ZMu13oN9c/SYmGji3noiHZ98piertP+3p7CGDx9uNsQDwezxZCsIIIAAAggg4D0BAgDvlUm7U6QBQElJSbvXY4XWAgwF2tqEOQgggAACCCAQLAECgACUpz4EjDsA9hSkDqXapUsXhgK1h5OtIIAAAggggIAHBQgAPFgo7U0SdwDaK5Z4+SFDhsiiRYsSL8SnCCCAAAIIIICATwUIAHxacFaytYOkDgPKHQBLpPN/tbPyO++80/kNsQUEEEAAAQQQQMCDAgQAHiyU9iRJhwDV0QsIANqjlnjZE044QRoae/AzIYAAAggggAACQRQgAPB5qWr7f53oBGxfQY4dO9YM8WXZ2rdltoQAAggggAACCLgvQADgfhl0KgXa/l8n7gB0ijFmZWskIIYCjWHhDQIIIIAAAggERIAAwOcFaV2l1tFrmOwRsJ4FsHz5cns2yFYQQAABBBBAAAEPCRAAeKgwOpIUvQPQrVs30SfKMtkjoE+g1TsqBAD2eLIVBBBAAAEEEPCWAAGAt8qj3anZtGmT9OvXr93rsUJiAW0GRBOgxEZ8igACCCCAAAL+FCAA8Ge5NaV648aNBABNGva90GZABAD2ebIlBBBAAAEEEPCOAAGAd8qiQynRJkDcAegQXcKV9A7AqlWrpL6+PuFyfIgAAggggAACCPhNgADAbyXWIr3cAWgBYtNbDQD0IWtr1qyxaYtsBgEEEEAAAQQQ8IYAAYA3yqFDqaipqTFPAe7fv3+H1meltgWsoUCXLVvW9kJ8ggACCCCAAAII+FCAAMCHhWYlmWcAWBL2/91nn30kJyeHkYDsp2WLCCCAAAIIIOCyAAGAywXQmd3rCEA6cQegM4rx183KypKhQ4cSAMTnYS4CCCCAAAII+FiAAMDHhWcFAHQCTk8hjhw5UmgClB5btooAAggggAAC7gkQALhn3+k9awdgvVLdu3fvTm+LDbQW0H4ABACtXZiDAAIIIIAAAv4WIADwcfnpHYCSkhLJzKQY01GMGgBs375dysvL07F5tokAAggggAACCLgiwJmjK+z27JRnANjj2NZWtAmQTsuXL29rEeYjgAACCCCAAAK+EyAA8F2RNSeYZwA0W6TjFUOBpkOVbSKAAAIIIICA2wIEAG6XQCf2v2HDBhkwYEAntsCqiQS6d+9u+lcsXbo00WJ8hgACCCCAAAII+EqAAMBXxdWc2EgkItoHgACg2SQdr0aNGiUEAOmQZZsIIIAAAggg4JYAAYBb8p3c77Zt20SfBEwA0EnIJKsTACQB4mMEEEAAAQQQ8J0AAYDviuyLBGvzH50IAL7wSNf/GgCsXr3aBFvp2gfbRQABBBBAAAEEnBQgAHBS28Z9rV+/3myNAMBG1Dib0gCgoaFBVqxYEedTZiGAAAIIIIAAAv4TIADwX5mZFOsdAB3/n6cAp7cANQDQiX4A6XVm6wgggAACCCDgnAABgHPWtu5JA4C+ffuaJwHbumE2FiPQv39/KS4uJgCIUeENAggggAACCPhZgADAp6XHEKDOFZw+EIw7AM55sycEEEAAAQQQSK8AAUB6fdO2dQKAtNG22vDo0aNl8eLFreYzAwEEEEAAAQQQ8KMAAYAfS60xzQQAzhXcmDFjTCfguro653bKnhBAAAEEEEAAgTQJEACkCTadm62vr5fNmzczBGg6kaO2XVpaKrW1tYwEFGXCSwQQQAABBBDwrwABgA/LTp8ArEHAwIEDfZh6/yVZ7wDotGTJEv8lnhQjgAACCCCAAAItBAgAWoD44e3atWtNMgcPHuyH5Po+jToSUNeuXekH4PuSJAMIIIAAAgggoAIEAD6sB9ZDwAYNGuTD1PszydoRmDsA/iw7Uo0AAggggAACsQIEALEevnindwCKioqkR48evkhvEBJJABCEUiQPCCCAAAIIIKACBAA+rAcaAND8x9mC034AK1eulKqqKmd3zN4QQAABBBBAAAGbBQgAbAZ1YnPr1q0Tmv84Id28j3HjxklDQwPNgJpJeIUAAggggAACPhUgAPBhwXEHwPlCGzt2rNnpwoULnd85e0QAAQQQQAABBGwUIACwEdOJTUUiEdFOwDQBckK7eR/dunUzw64SADSb8AoBBBBAAAEE/ClAAOCzctMHgOlDqWgC5HzBaTMgAgDn3dkjAggggAACCNgrQABgr2fat8YzANJO3OYOCADapOEDBBBAAAEEEPCRAAGAjwpLk2oFANwBcL7gNAAoLy+XDRs2OL9z9ogAAggggAACCNgkQABgE6RTm1mzZo15BkCvXr2c2iX7+VJg/Pjx5tX8+fMxQQABBBBAAAEEfCvgWACg7dZnzZoln332mWhHVqaOCaxatUqGDBnSsZVZq1MCw4YNM8HXvHnzOrUdVkYAAQQQQAABBNwUcCQA2LRpk5x11lny/vvvy/PPPy/nnnuuVFdXu5lv3+579erVBAAulV5GRobsu+++QgDgUgGwWwQQQAABBBCwRcCRAOCJJ56QE088Ua6++mq56aabZOTIkTJjxgxbMhC2jWgAMHTo0LBl2zP5nThxIgGAZ0qDhCCAAAIIIIBARwSyO7JSe9e55JJLRK+eWpN2pKysrLTemr+PPfaYrFy50rwuLCyUCy+8MOZzP7zJysqSzMxM0b/pmKqqqkTvppSWlkqXLl3SsYu0bNMqey3XvLy8tOzDqY0edNBBcv/994uWRZ8+fTq92+zsL76C1t9ObzAgG9DvkU5FRUXmCcwByZYt2cjJyTEmubm5tmwvKBuxjrtaZ2hmGluqWmf0GKNPM2dqFrCOu8XFxc0zeWUE9Piix2HqTGyF0O+STsnOwTIa7Yyhi+dqycrOkQAg+ofq9ddfl3Xr1skJJ5wQozpnzhyZPXu2mde1a1e59NJLYz73wxs90dV/1g+R3WlesWKF2eSYMWMkPz/f7s2nfXtaD/z+wzxlyhTjtHjxYlsexmYFR9YPUdoLwWc7iD52+CzpaUuu/ijr98jv3yW7gazvkt8vMtjtotvTOqPHGOpMrK5VZ/z4exqbE/vfaZ3RcxnqTKxtynWm8Vwws/E7l+3iuVqypvaOBAAW33PPPSePPPKI3H333dIy4v75z39uLWb+bty4Mea9H97oyYoeZCsqKtKS3E8//dRst3v37rJ169a07CMdG1UTvVq+c+dOqampSccuHNtm7969TfD19ttvy/7779/p/eoPjx5QWt4R6/SGfb4Bvcqi1jt27JC6ujqf58be5OudtPr6evpRtWDVE/+ePXtKWVmZ8Wnxcajf6l0RPfbqYBxMzQIFBQWiv6fbtm3jRLeZxbzSK9z6u8TxNxZGj7/dunVLeg7WU4/RjeeCe108V9Njon7325ocCwAefvhhefnll+W3v/39H0T5AAAmnElEQVSt9O3bt630MD+BgI4ApBH5wIEDEyzFR+kUUH8dDlRHs2JCAAEEEEAAAQT8KOBIJ+AXXnhBXnvtNbnvvvs4+e9ELdEOwHryT3ORTiDasKpe+ScAsAGSTSCAAAIIIICAKwKOBAB/+ctfZPny5WYkoCOPPFL0369//WtXMuznnWofAB2LnsldAQ0A9GnAfmqG5a4Ye0cAAQQQQAABLwk40gToySef9FKefZsWHSXpqKOO8m36g5Lw/fbbz2RF+2Qcc8wxQckW+UAAAQQQQACBkAg4cgcgJJZpzaZ24Fq7dq0MHz48rfth48kFRowYYYYAszplJ1+DJRBAAAEEEEAAAe8IEAB4pywSpkTb/+uYrgQACZkc+VBH7dG7AJ988okj+2MnCCCAAAIIIICAnQIEAHZqpnFb1jMA9Oozk/sCBxxwgHAHwP1yIAUIIIAAAggg0H4BAoD2m7myhgYAOjY6Q4C6wt9qpxoA6HMNrMCs1QLMQAABBBBAAAEEPCpAAODRgmmZLO0APGTIkLQ9Zbjl/nifWGDSpElmgY8//jjxgnyKAAIIIIAAAgh4TIAAwGMF0lZydBhVmv+0peP8fH2y8aBBg2TOnDnO75w9IoAAAggggAACnRAgAOgEnpOralMTOgA7KZ58X3oXgI7AyZ1YAgEEEEAAAQS8JUAA4K3yiJua3bt3y+bNm2XkyJFxP2emOwLaD2DBggVSWVnpTgLYKwIIIIAAAggg0AEBAoAOoDm9ytKlS80uR40a5fSu2V8CgYMOOkjq6+vls88+S7AUHyGAAAIIIIAAAt4SIADwVnnETQ0BQFwW12fuu+++kpubSz8A10uCBCCAAAIIIIBAewQIANqj5dKyGgBop9Nu3bq5lAJ2G09AT/4nTpxIABAPh3kIIIAAAggg4FkBAgDPFk1zwjQAoPlPs4eXXmlHYEYC8lKJkBYEEEAAAQQQSCZAAJBMyAOfEwB4oBDaSIIGAFu3bpU1a9a0sQSzEUAAAQQQQAABbwkQAHirPFqlprq62pxccgegFY0nZlgPBJs9e7Yn0kMiEEAAAQQQQACBZAIEAMmEXP5cx/9vaGigCZDL5dDW7vv37y8DBw6kGVBbQMxHAAEEEEAAAc8JEAB4rkhiE7RkyRIzY/To0bEf8M4zAvQD8ExRkBAEEEAAAQQQSEGAACAFJDcXWbx4sfTo0UNKSkrcTAb7TiCgAYA+EKyioiLBUnyEAAIIIIAAAgh4Q4AAwBvl0GYq9A4AV//b5PHEB9YDwebOneuJ9JAIBBBAAAEEEEAgkQABQCIdD3ymdwDGjBnjgZSQhLYE9IFgeXl5QkfgtoSYjwACCCCAAAJeEiAA8FJptEiLNinR4SUJAFrAeOxtTk4ODwTzWJmQHAQQQAABBBBoW4AAoG0b1z/R8f8jkQhNgFwvieQJoCNwciOWQAABBBBAAAFvCBAAeKMc4qZCm//oRB+AuDyemqkBwLZt23ggmKdKhcQggAACCCCAQDwBAoB4Kh6Zt2jRIunXr590797dIykiGW0JaACgE/0A2hJiPgIIIIAAAgh4RYAAwCslEScdCxculHHjxsX5hFleE9BAbcCAAfLxxx97LWmkBwEEEEAAAQQQiBEgAIjh8NYbDQDGjx/vrUSRmjYFDjzwQJ4I3KYOHyCAAAIIIICAVwQIALxSEi3SsWnTJikrK+MOQAsXL7/V5wHoA8Gqqqq8nEzShgACCCCAAAIhFyAA8GgF0Kv/OtEEyKMFFCdZBxxwgNTV1cm8efPifMosBBBAAAEEEEDAGwIEAN4oh1ap0CvJ+fn5Mnz48FafMcObAhMmTJDs7Gz55JNPvJlAUoUAAggggAACCDQKEAB4tBroHYDS0lLJysryaApJVksBDdj0js2cOXNafsR7BBBAAAEEEEDAMwIEAJ4pitiE6B2AfffdN3Ym7zwvoMOBMhKQ54uJBCKAAAIIIBBqAQIADxZ/RUWFLF++XLRJCZO/BHQkoPXr18uWLVv8lXBSiwACCCCAAAKhESAA8GBRz58/XyKRCAGAB8smWZK0I7BO3AVIJsXnCCCAAAIIIOCWAAGAW/IJ9qujyGRmZsrYsWMTLMVHXhTQTtv65OZPP/3Ui8kjTQgggAACCCCAAJ2AvVgH5s6dK6NGjZKCggIvJo80JRHYf//9uQOQxIiPEUAAAQQQQMA9Ae4AuGff5p71DsDEiRPb/JwPvC2gzYA+++wz04zL2ykldQgggAACCCAQRgECAI+Vuj5FdunSpbT/91i5tCc5GgDs3r1bli1b1p7VWBYBBBBAAAEEEHBEgADAEebUd6IdgOvr62W//fZLfSWW9JSA1RGYB4J5qlhIDAIIIIAAAgh8KUAA4LGqoKPH6MO/eAaAxwqmHcnp1auXDB48mH4A7TBjUQQQQAABBBBwToAAwDnrlPako8fo6D90AE6Jy7ML6V0ARgLybPGQMAQQQAABBEItQADgseLXZiNWExKPJY3ktENAy3DhwoVSXV3djrVYFAEEEEAAAQQQSL8AAUD6jVPeQ1lZmaxevZoAIGUx7y6oQ4HW1dXJggULvJtIUoYAAggggAACoRQgAPBQsVtNRrgD4KFC6WBSJkyYYB7mRkfgDgKyGgIIIIAAAgikTYAAIG207d/wRx99JF26dDEPAWv/2qzhJYHCwkIZM2aMEAB4qVRICwIIIIAAAgioAAGAh+qBBgCTJk0yV449lCyS0kEBOgJ3EI7VEEAAAQQQQCCtAtlp3XonNp6Z6b/YRNOckZHRoRN4bS+uTYAuv/zyDq3fCeq0r6omOnXUJu0JTNMODjzwQPn73/8uu3btku7du8fdi5qEzSUuRIuZ1vdf/1qvWywS2rfUmfhFry468X1q7WOZ8F2KtbHqjLpEIpHYD0P+Tm2sehNyipjsR9eZmA9avvGBn2cDgOLi4pacnn+v4/dr5dC/7Z10/P+KigqZOnWq+DHvifJrfWF0aNO8vLxEiwbqs8MOO8zkZ8mSJfLVr341bt6sOpOd7dmvYtx0p3umdaKiTakaGhrSvTtfbV/rip6s5Obm+ird6U6sddwtKiriZK4Fdk5Ojmi9CdPxtwVB3LfRdSbuAiGeqXVGf7sJjGIrgfVbnew8LbPRLic3x9XzuWS/nZ4969Crpn6b9AdZK4eeyLd3ev31103gUFpaaq4Yt3d9Ly+vJnoipy41NTVeTqqtaRs4cKB5nsN7770nkydPjrvt/Px8c5CtrKyM+3lYZ+qPjwaMe/bsMaMphdUhXr71u6RPC2eI2VgdPbnV75PWGfVhahbQoEiPvbW1tc0zeWWOMVpndu/ezYlui/qg/RH1d0lbJzA1C+jxV481yc5RezZeuKqprpG9Lp7LJgv4/dfOprkcAvXqgw8+EB05RisXUzAE9OrSxIkT6QgcjOIkFwgggAACCARGgADAA0Wpt2lmzpwpVpMRDySJJNgkoB2BtXkXEwIIIIAAAggg4BUBAgAPlIQ+LGrnzp0EAB4oC7uToB2Bt2/fLuvWrbN702wPAQQQQAABBBDokAABQIfY7F3p3XffFW3zPGXKFHs3zNZcF7Ae6jZnzhzX00ICEEAAAQQQQAABFSAA8EA90ABArxTT/t8DhWFzErQjcN++fWkGZLMrm0MAAQQQQACBjgsQAHTczpY1dSSPWbNmyRFHHGHL9tiI9wQ0uKMfgPfKhRQhgAACCCAQVgECAJdL/v333zdDbbU1TrzLyWP3NghoADBv3rxQDYFqAxubQAABBBBAAIE0CRAApAk21c2+9tpr0qdPHzNcZKrrsJy/BCZNmmRO/hcuXOivhJNaBBBAAAEEEAikAAGAy8U6Y8YM85RY62m5LieH3adBQJ8FoM8EmD17dhq2ziYRQAABBBBAAIH2CRAAtM/L1qWXLFkia9eulaOPPtrW7bIxbwlo5+7x48cTAHirWEgNAggggAACoRUgAHCx6J999lnzKPJp06a5mAp27YTAQQcdRADgBDT7QAABBBBAAIGkAgQASYnSt8Bzzz0nxx13HMN/po/YM1vWAGDDhg2yfv16z6SJhCCAAAIIIIBAOAUIAFwq97lz58qKFSvklFNOcSkF7NZJAQ0AdKIfgJPq7AsBBBBAAAEE4gkQAMRTcWDeU089JV27dpXp06c7sDd24bbAoEGDpH///gQAbhcE+0cAAQQQQAABngTsRh2oqqqSJ554Qs444wzJzc11Iwns0wWBKVOmmIe+ubBrdokAAggggAACCDQJcAegicK5F88//7zs3LlTzjvvPOd2yp5cFzjkkENEnwVQXl7uelpIAAIIIIAAAgiEV4AAwIWyf/DBB+Xggw+W0tJSF/bOLt0SOPTQQyUSiciHH37oVhLYLwIIIIAAAgggQBMgp+vAu+++K59++qlcfPHFTu+a/bksoAFfz549ZebMmS6nhN0jgAACCCCAQJgFuAPgcOn/5je/MVf+jz/+eIf3zO68IKB3fmbNmuWFpJAGBBBAAAEEEAipAAGAgwX//vvvi94BuPzyyyUjI8PBPbMrrwh85StfER0CdteuXV5JEulAAAEEEEAAgZAJEAA4VODa9vu2226TcePGyWmnnebQXtmN1wSOPPJIaWhoMIGg19JGehBAAAEEEEAgHAIEAA6V85NPPmmu/N5yyy2SmQm7Q+ye282oUaNkwIAB8vbbb3subSQIAQQQQAABBMIhwJmoA+WsQ37eeuutou3+9QowU7gFtA689dZb4UYg9wgggAACCCDgmgABgAP02vRHH/51++23O7A3duF1galTp8rq1atlzZo1Xk8q6UMAAQQQQACBAAoQAKS5UN944w159NFH5frrr5eBAwemeW9s3g8CegcgKytLXnnlFT8klzQigAACCCCAQMAECADSWKA60su1115rHvp10UUXpXFPbNpPAj169DB14qWXXvJTskkrAggggAACCAREgAAgjQV50003yY4dO+See+5h2M80Ovtx08cdd5x88MEHov1DmBBAAAEEEEAAAScFCADSpP3CCy+IjvzzP//zPzJ06NA07YXN+lVAO4TX19fLyy+/7NcskG4EEEAAAQQQ8KkAAUAaCm7r1q1y3XXXybRp0+S8885Lwx7YpN8FBg8eLBMnTpSnnnrK71kh/QgggAACCCDgMwECgDQU2DXXXGMe9nT33XenYetsMigCZ5xxhrz++uuyefPmoGSJfCCAAAIIIICADwQIAGwupEceeURmzJghd955p/Tr18/mrbO5IAnoE6H1oXBPPPFEkLJFXhBAAAEEEEDA4wIEADYW0Nq1a+WGG26Qk08+WU455RQbt8ymgijQq1cvOfroo+Vvf/ubRCKRIGaRPCGAAAIIIICABwUIAGwqFD2Bu+yyy6SwsNBc/bdps2wm4ALf//73ZenSpeauUcCzSvYQQAABBBBAwCMCBAA2FcSf/vQnee+99+R3v/ud6DjvTAikIqAPBdtvv/3k97//fSqLswwCCCCAAAIIINBpAQKAThOKLF682Fz1/853viPHHHOMDVtkE2ESuOqqq+TDDz+UV199NUzZJq8IIIAAAggg4JIAAUAn4auqquQHP/iBDBo0SG677bZObo3Vwyig/UUOPvhg88yImpqaMBKQZwQQQAABBBBwUIAAoJPYt9xyi6xYsULuu+8+0/6/k5tj9ZAK/PSnP5XVq1fLHXfcEVIBso0AAggggAACTgkQAHRCWh/ipMN+3nzzzTJhwoRObIlVwy4wfvx4ufHGG+X+++83T5AOuwf5RwABBBBAAIH0CWSnb9PB3vLcuXPN035POukk+e53vxvszJI7RwQuvfRSmTdvnlxxxRVSUVHBU6QdUWcnCCCAAAIIhE+AOwAdKPP169fL+eefL8OHD5d77rmnA1tgFQTiC+goUt/85jfl+uuvlwsuuMAMERp/SeYigAACCCCAAAIdEyAAaKfb9u3b5ayzzpKMjAx56KGHaPffTj8WTyyQlZUld999txlOdvbs2TJt2jRT3/7+97/Lxo0bE6/MpwgggAACCCCAQAoCNAFKAclaRE/A9Orsjh075JlnnpGBAwdaH/EXAVsFTj/9dDnhhBPkiSeekMcff1yuvfZas/1hw4bJlClTZNKkSabfyahRoxIGoTqqUENDg+Tn59uaPjaGAAIIIIAAAv4VIABIsew+++wzufDCC83JlHb+1RMvJgTSKVBQUGD6AZx33nmyZcsWefvtt2XmzJny0UcfyWOPPda06549e4r+05P8+vp62bt3r+zatUt2795t3uuCemehX79+MmbMGDn00ENNcKHBBBMCCCCAAAIIhE+AACBJmevVUx2Z5c477zQn/Q8++KAZ8z/JanyMgK0CJSUlcsYZZ5h/umE9uV+4cKHpI6B9UvSulF7t1xP9wsJC6dq1q3Tp0sW8zszMlD179ogup52Mf/7zn4sOO6rPHrjooovka1/7mugyTAgggAACCCAQDgECgATl/N5775mHe+lJk16F1TH/9eSKCQG3BfTkXk/g9V97Jw0GXnzxRfnrX/8qF198sYwYMUKuvPJKOe200wgE2ovJ8ggggAACCPhQwLHLftXV1aIn1O+//77U1tZ6nmry5Mly5plnmiYU2uTnrrvu4uTf86VGAlMRKC4uNnX73//+tzz99NMyYMAAufzyy02HY32vzYiYEEAAAQQQQCC4Ao4EAJWVlfKd73xH3njjDfPgrOuuu04ikYinVXW0n5NPPllmzJhh2kx7OrEkDoEOChxyyCGmP4Ge+Gszo8suu0wOO+ww+eMf/yj6HejspHcbNmzYYJ5yvG7dOtNUyevf/c7mmfURQAABBBDwuoAjTYC0w6I2VdBmBjpdcskl8sEHH4iefHh1ysvLk9GjR5vhPr2aRtKFgF0C+l188sknTQdjPfm//fbb5Y477jDf0alTp8qBBx5o+sD06tUr5jtRVVUlmzdvlrVr18qqVatk2bJlsmLFCvNa+xxo8N9yys7ONh2Shw4dap6loR3qR44caZoi6d0I+iO0FOM9AggggAAC9go4EgDoScGxxx7blHI9mdAOjNEBwFtvvSWbNm0yy+jJ9/Tp05uWd+OFjvM/f/58MwRjqvvXdfTkpbNNKLTPQWlpqahDECZ10RFq9GSRq7+xJar1RX2i64yO4KNDzmoA6sak31X9bs6ZM0cWLFhgmr9Z6bPKUtOlTfnq6upiktitWzdzJ6FPnz5mxCHtjKyjGWnnZO1Qr3VAOzBrp+Vt27bJ4sWLzZ0Gq17oct27d5cePXqIbis3N9fUHf2bk5NjtqNmlpumJ3pq+T76s/a81rsWmgfdpxuTjvqk/TzUruWkeVcvy6zl52F9r2WvwaUdTUy1yerOnTulb9++rnDq90p/DwcNGmTL/qkz8RnVRY858eqMfge1uaRb/f70GKnp0+OgGxN1Rsxvj56vRk9aX/T3KN7Frejlvt04Gl9d4++dW/UnOi1tvXYkANADmZ4IWJO+1uYA0ZM+VEv7B+ikJwCnnnpq9MeOv9Yx/rWjpP5jQgCBLwT0pDPRga+8vFz039KlSztEpoGGNj2yo/lRhxLASggggAACCHwp8Le//a1DFl+TXNnVeGd8kksBnCa6oqIiYdodCQA0YrKuIGpq9OpGyytbf/7zn80VQv1cr+S4/dTTV155pdXVTU1bokmjQr0ClQw90TaC+Jma6NVUPanToSqZmgX0zojW90Qn1c1Lh+eVXnnv3bu3bN26tc3vod5RiL4SHpYr4npFSY+nepWaqVlA75jq8zD0ym30703zEuF9VVRUZI698a50h1dFzHmIXnDUi5RhOX6kWt56B1J/l1re5U11/SAsp+eueicketLjr96VSXaOWnL4UVLT2Lw12XLR27b7tR4TE92BcCQA0B/ysrKyprzp68GDBze91xcKrf+8MulJWXtv/+vyerLb3vW8kud0pcMyURcOsrHKaqJ1LcwH2ViRL96pi/VPfZiaBawAwEvHy+bUuffK+rGzfNxLiff2rCZ6HCYAiC0bvRCpNvqP36ZYGzXhtynWRN9Zv0v6N9Hkh1+t2NAmUW468dkRRxxhmtJo+19t96tNfQ444IBObJFVEUAAAQQQQAABBBBAoCMCjtwBOProo80zAM4++2xzO+Wss86SYcOGdSS9rIMAAggggAACCCCAAAKdEHAkANBbjzqsoI7+obfc9D0TAggggAACCCCAAAIIOC/g6Jm4diphQgABBBBAAAEEEEAAAfcEHOkD4F722DMCCCCAAAIIIIAAAghECxAARGvwGgEEEEAAAQQQQACBgAsQAAS8gMkeAggggAACCCCAAALRAgQA0Rq8RgABBBBAAAEEEEAg4AIEAAEvYLKHAAIIIIAAAggggEC0AAFAtAavEUAAAQQQQAABBBAIuAABQMALmOwhgAACCCCAAAIIIBAtQAAQrcFrBBBAAAEEEEAAAQQCLkAAEPACJnsIIIAAAggggAACCEQLEABEa/AaAQQQQAABBBBAAIGACxAABLyAyR4CCCCAAAIIIIAAAtECBADRGrxGAAEEEEAAAQQQQCDgAtlezV92tmeT1iZZZuYX8ZQf095mpmz4oL6+XhYuXCjdu3eXnJwcG7YYnE1kZGSYzFBnYsu0pqbG1JkePXoINrE2kUhEtN7gEutSWVlp6kzPnj2xiaURrTP6+0SdiYXZs2ePbNiwQXr16iVZWVmxH4b8XUNDA3UmTh3YtWuXrF+/Xvr06RPn0+ZZdVOPkMi4sa5+55LV6YzGA0OkOcm8QsB+gWXLlsmJJ54of/3rX+WQQw6xfwdsMXACc+bMkXPOOUf+9a9/ydixYwOXPzJkv8Cbb74pl1xyibz22msyaNAg+3fAFgMn8Oyzz8p1110ns2fPli5dugQuf2TIfoFHHnlEbr/9dlmyZIn9G3d4izQBchic3SGAAAIIIIAAAggg4KYAAYCb+uwbAQQQQAABBBBAAAGHBbJ+0jg5vE92F0KB4uJimTJlinTt2jWEuSfL7RXQ9srdunUzdaawsLC9q7N8CAW0X4S2/9fjTF5eXggFyHJ7BfQ4U1JSYupMsvbS7d02ywdTQI8z/fv3l8mTJ/s+g/QB8H0RkgEEEEAAAQQQQAABBFIXoAlQ6lYsiQACCCCAAAIIIICA7wX8N9am78mDlwHtDb969Wo58MADpXfv3nEzWF1dbUZa0NtneussejjQbdu2iY76MnToUBk9enTc9ZkZLIFU6kxtba2pFwUFBTJx4kQz9KUq6NB98+fPjwFhdKkYjsC9SXT8iM7sJ598IrqsNZWWlppmQfo+lTpnrcdf/wukUmf0OLJ79+6YzOpv2KhRo2TNmjVmiFDrQx0qVOczBV9gxowZMn369DaH8Ex0zuKn4wx9AIJfl9Oaw3vuuccM1ajjtt97771y2GGHmbbb0TvV8bkvuOAC2bt3r7z33nvyxhtvyHHHHWdO6PQH+8orrzTPCPjjH/8o+fn5DPsYjRfA16nUmU2bNsmFF15o6siCBQtE68bXv/51c0DWOvSrX/1KtmzZIosXLzb/jjnmmABKkSUVSHT8iBaqq6uT888/X8rLy5vqxbBhw6Rv376SSp2L3hav/S2Qap154okn5IMPPmiqL/pen1tz6KGHyi9/+Ut59913ZdWqVeZzrV96IYIp2AJPPfWU/OxnP5Nvf/vbcQOAROcsvjvO6HMAmBDoiMDKlSsjp512WqTxgGlW/+c//xn53//931abevDBByONX4ym+RdffHFk5syZ5v15550X+fTTT83rxpO+SONJXqTxyk3TsrwIlkCqdeY3v/lN5IEHHmjK/M033xx5/vnnzfvGYCDy0EMPNX3Gi2ALJDp+ROd86dKlkcYAIHqWeZ1qnWu1IjN8K5BqnYnOYOPdgMjpp58eaQwgzeyzzjor0nhnO3oRXgdYoPGOc+SGG26I6PnJ4YcfHqmqqoqb27bOWfx4nKEPQLCD2bTmbsWKFeaKiPUEZG0CpE/8bTnpg8D0M2uyltMrKuvWrWu6qqJX6nTEF33KHlMwBVKtM/pAJ70CY016VVev6unUeKInOqrUP/7xD/noo4/MU06t5fgbPIG2jh8tc6r1Qh8A9tJLL4k+4KmiosIskmqda7k93vtXINU6Y+VQmws1XrySH/3oR2akOq07ZWVlsnXrVtEHP+nvFFOwBfTOjzYl1ZYMbU2Jzln8eJwhAGirpJmfVGDjxo0xzX10iM/t27e3Wk+bc0QP/2ktp004ioqKmtp264o69KMeeJmCKZBqncnNzW3qJ/L666+bH+ATTjjBoOiJ3qxZs8xQj3/729/k2muvDSYWuTICbR0/WvJ8/vnnpp2/tunWdrjf+MY3RNvqplrnWm6P9/4VSLXOWDl89dVXm4Yd1nnLly83fUn0AoOeGGoz1f/85z/W4vwNoIAOHXzyySfHbfZjZTfROYsfjzN0ArZKlr/tFtBxk/XgaE0aHWuHzZZTW8u1nK/r6Ta0HwBTMAValnlbdcbK/XPPPWeuwN19993mqr/O/8tf/mL6jOidp5NOOklOOeUUEyDo1V+m4AmkWme+973vif6znhuhV3X1bkB2dnZKx6ngyYU3R6nWGUtIjzNnnHGG9db0Q3vmmWekR48eZt7IkSPNcefEE09sWoYX4RNoWa9UwDpnaflZst82L+hxB8ALpeDTNPTp0yfmar1eudcHZLScdFSF6Kv6+nrAgAGioypox2D9obYm6zPrPX+DJZBqndFcP/zww/L444/Lb3/7WxkyZIiB0M7mOuKU1exM7xRo0zG94scUTIG2jh8tc6tNB6OPJfvss4+5+t+eOtdym7z3p0CqdUZzp6P9aN2ZNm1aU2Z37twpO3bsaHqvdWnz5s3S0NDQNI8X4RNIdM7ix+MMAUD46rBtOdbhPHUYtbVr15oouLGTpnmiou5Ab73rP52OOOIIefHFF6WxU42Z9/7778sBBxxgrswdfPDBoldfdHr77bfNFRfrqouZyX+BEki1zrzwwgvy2muvyX333WdO8C0EHT5W7wZoEyCdFi1aZJqd7b///tYi/A2YQFvHD81m9HFGjx9/+MMfTO61Dbc2HdOh/BLVuYBRkZ0vBVKtM7q4jiQ2ZsyYpiaHOk/7HF111VWm31FjT1D597//LVOnTm268KDLMIVHwDrO6N3Ets5Z/Hic4UnA4anDacmpHhj1Cm3Pnj3NVdqf/vSn5sT+F7/4hTmgattJvRV26623mmBBr9w2jq4gZ555pkmPXs297rrrRG+f6We33HKL6NjdTMEVSKXO6O14veKmz42wpv/6r/+SK664wjwb4E9/+pPocwK0k971119vhp+1luNvsAQSHT+ijzO7du0yw/dt2LDB1AsdGvbyyy83x5W26lywpMiNJZBqndHl9Viiy1966aXW6ubvX//6V9G+AfqZ9lu7/fbbpaSkJGYZ3gRTQANILXvtF6BT9HEm0TmL344zBADBrL+O5kpPxPTWu47MkmjSznnaR0Cj6JaT3nLt3r17y9m8D6hAqnUmUfb1Kp3+MEcHCYmW5zN/CyQ6fkTnTK/+6wUF68fb+syOOmdti7/+EEi1zrSVG23yow8ejB7Eoq1lmR8ugbbOWfx0nCEACFedJbcIIIAAAggggAACIRegD0DIKwDZRwABBBBAAAEEEAiXAAFAuMqb3CKAAAIIIIAAAgiEXIAAIOQVgOwjgAACCCCAAAIIhEuAACBc5U1uEUAAAQQQQAABBEIuQAAQ8gpA9hFAAAEEEEAAAQTCJUAAEK7yJrcIIIBAhwX0Scz6QL/2TDo+PxMCCCCAgLcECAC8VR6kBgEEEPCkwI4dO2TixImyZs2alNN32WWXyW9+85uUl2dBBBBAAAFnBAgAnHFmLwgggICvBfTBN0uWLGlXHj744IN2Lc/CCCCAAALOCBAAOOPMXhBAAAHPC+hJ/o9//GM59thj5Zvf/Kb8+c9/lkgkInv37pUbbrjBpP+mm26SGTNmmNcrVqyQK6+8Uo4//ng5/fTT5Re/+IVoMyGdfvnLX8qqVavk2WeflTvvvNPM4z8EEEAAAW8IEAB4oxxIBQIIIOC6wLe+9S1544035JxzzpHJkyfLtddeK3fddZdkZ2fLfvvtZ9I3YcIE6devn6xcudI0CSorK5Nvf/vbMmrUKLn99ttFAwSdxowZI0VFRTJw4EAZN26cmcd/CCCAAALeEMhovLoT8UZSSAUCCCCAgJsCPXv2NFfrL7nkEpOMf//731JfXy+nnHKKOeEfPny4aQZUWloqL730kjzxxBNy//33S2bmF9eSLrzwQlm2bJm8/fbbZn0NInRdKyhwM2/sGwEEEECgWSC7+SWvEEAAAQTCLHD++efLpZdeKg8//LB87WtfMyfv48ePj0uizX6mTZsmr7/+uixatEgWLlwor776qrk7EHcFZiKAAAIIeEaAJkCeKQoSggACCLgrcM8995g2+6NHjzaj9+y7775y/fXXx03U3LlzZdiwYXLRRReJdvbVEYKmTp0ad1lmIoAAAgh4S4A7AN4qD1KDAAIIuCJQUVEhjz32mHz96183/xoaGkyb/jvuuEN+8pOfSEZGhkmX1WpU52k7f73qn5WVZT579913TZMhVzLAThFAAAEEUhbgDkDKVCyIAAIIBFegoKBAfv/735sr/joaUGVlpWzdutV04s3PzxftH6DTnDlzpLy83DT10Q7AupwGBTraz5NPPinV1dVNSL169TLNgzZu3Ng0jxcIIIAAAu4LEAC4XwakAAEEEHBdQK/w/+53v5Ply5ebk3494der+0899ZRJW9euXc1wn+eee67cdtttcvXVV0u3bt2kb9++0rt3b/ntb39rhgHV9a2n/5566qkmKJgyZYrr+SMBCCCAAALNAowC1GzBKwQQQACBRoE9e/aYK/t9+vRp5bF7924pLCxsavazfft2yc3NlS5durRaVmfocwHq6urMOnEXYCYCCCCAgOMCBACOk7NDBBBAAAEEEEAAAQTcE6AJkHv27BkBBBBAAAEEEEAAAccFCAAcJ2eHCCCAAAIIIIAAAgi4J0AA4J49e0YAAQQQQAABBBBAwHEBAgDHydkhAggggAACCCCAAALuCRAAuGfPnhFAAAEEEEAAAQQQcFyAAMBxcnaIAAIIIIAAAggggIB7AgQA7tmzZwQQQAABBBBAAAEEHBcgAHCcnB0igAACCCCAAAIIIOCewP8DALL+FNBB6QoAAAAASUVORK5CYII=" 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U6hsdDQUNkRNBYZGTnwHd5vLF/+WZ5pR4fGBcjhcFRWVt59993Jycmvvvrqo48++qtf/cp7V3R09JNPPun9edKkSe3t7dquenTCwsK6urrkZjh48OCcOXO0ekLGjRvX29vb3d095CPNZvOkSZO2b9++bNkyTVbtI6qqWiyWnp4e2UE0o6rquHHjOjs7e3t7ZWfRjM1mc7lcRtqi0NBQs9nc0dEhO4iW9PCJp62IiAiHw+FwOGQH0YzFYjGZTAbbIrvdfvz48QFHYsb19vY6nd2+rAQej2fAQQGNC1B0dHR2dnZ2drYQYunSpe+8847H41EURQhhtVrnzJnT/8iWlhZtVz06fX19LpdLbob6+vq4uDitYng8nuFvVFZWVllZmfRnYHB9fX2qquo85Ih4PwV6e3uNtFFWq9XtdrvdbtlBNGO1Wg32hyf08YmnOYO9lRRFMdgfnnfPl9vt7uvrO/3eEX1naUvjSdAFBQVVVVWNjY29vb1btmzJyMjwth+cSVdX17Fjx6ZMmSJl7Tk5Obt27ZKyagAAJNJ4BCgyMvKWW2558MEHOzs7k5KS/vu//1vb5RtPXV2dEGLy5MlS1p6Tk9Pa2lpfXz9p0iQpAQAAkEL7eddFRUVFRUWaL9aovAVI4giQEKKsrIwCBAAIKpwJWrL6+nohhKz+kZycHBYWxl4wAECwoQBJVldXFxkZKeuIaJPJlJWVRQECAAQbCpBkR44ckTUByCs3N7esrExiAAAA/I8CJJn0Ccg5OTmHDh0y2MlOAAAYHAVIsrq6uqlTp0oMkJOT4/F42AsGAAgqFCDJpI8AZWVlWSwWChAAIKhQgGQ6ceJEe3u73DlAISEhaWlpFCAAQFChAMnkPQY+Pj5eboy8vLzS0lK5GQAA8CcKkEwNDQ1C3kmA+uXl5VVWVg7n+qkAABgDBUgm7whQXFyc3Bi5ubm9vb179uyRGwMAAL+hAMnU2NhotVrHjx8vN0ZOTo7JZGIaEAAgeFCAZPIeAqYoitwY48aNS05OZhoQACB4UIBkamhokD4D2ot50ACAoEIBkkn6SYD65eXllZeXO51O2UEAAPAHCpBMuhoBcjqdFRUVsoMAAOAPFCBpPB5PY2OjfgqQoijsBQMABAkKkDRHjx51OBzSj4H3ioyMTExMpAABAIIEBUiaxsZGoYOzIPZjHjQAIHhQgKTxngZaJyNAQoi8vLzdu3f39vbKDgIAgM9RgKTR2whQbm5ud3d3dXW17CAAAPgcBUiahoaGsLCwsLAw2UH+ZebMmUII9oIBAIIBBUiaxsZG/ez/EkLExsbGx8dzQQwAQDCgAEnT2Ng4ceJE2Sn+Q25uLgUIABAMKEDSNDU16a0A5eXl7dq1y+PxyA4CAIBvUYCk0c9ZEPvl5OS0t7fX1NTIDgIAgG9RgKRpamrS1RwgIUReXp4Qgr1gAADDowDJ0dHRceLECb0VoISEhKioqLKyMtlBAADwLQqQHN6TAOmtACmKkpWVxQgQAMDwKEBy6LMACSFyc3MZAQIAGB4FSA7dFqCcnJy6urq2tjbZQQAA8CEKkBxNTU0WiyUqKkp2kFPl5uYKIRgEAgAYGwVIjqampgkTJphMunv+09LSrFbr7t27ZQcBAMCHdPcFHCT0dh2MflarNS0tjQIEADA2CpAczc3NejsNdL+cnBx2gQEAjI0CJIcOLwTWLzs7u7Ky0ul0yg4CAICvUIDk0OGFwPplZ2e7XK6qqirZQQAA8BUKkARut7utrW3ChAmygwwsJydHcCAYAMDQKEAStLa29vX16XYEKDo6etKkSXv27JEdBAAAX6EASdDU1CSE0G0BEkJkZ2dTgAAABkYBkiAgChBXBAMAGBgFSAL9F6DMzMzW1tbm5mbZQQAA8AkKkASNjY12uz0sLEx2kDPKysoSzIMGABgXBUiC5uZmfZ4Gup/3ghhMAwIAGBUFSAI9nwTIy2w2z5gxo7y8XHYQAAB8ggIkQXNzs25PAtQvOzubK4IBAIzKLGvFkZGRslZ9MrPZ7P8kra2tubm5Plqvqqomk2nsC589e/Ybb7xht9stFosmwUZNURSTyWQ2S/tb1ZyiKEKIsLAwj8cjO4tmVFW1WCwG2yJFUXTySaUVKZ94PqUois1ms1qtsoNoRlEURVGMtEUmk0kIER4ePuC9qqqarFaf/lm6XK4Bb5f2pdLe3i5r1SeLiIg4fvy4n1daX18fFRXlo2cgKirK7XZ3dnaOcTnJyckOh6O4uDgjI0OTYKOmqqrNZuvq6pIbQ0NmszkqKqqrq+tMb8tAFBYW5nA43G637CCaCQsLs1qtOvmk0oqUTzyfiomJ6enp6e7ulh1EM1arVVVVI21RSEhIeHh4R0dHX1/f6fdG9fa6nc5OH7/R7Hb76TeyC8zfnE7n8ePH9b8LzHsgGNOAAACGRAHyt+bmZo/Ho/8CFBcXFxMTw4FgAABDogD5m/csiPovQEKIzMxMRoAAAIZEAfI37+mVdX4eIK+srCxGgAAAhkQB8reWlhYhRGxsrOwgQ8vKyqqtrTXYlEkAAAQFyP+amprGjRsXGhoqO8jQMjMzPR7P3r17ZQcBAEBjFCB/0/9poPtlZGSYTCb2ggEAjIcC5G9NTU0BMQNaCGG325OSkpgHDQAwHgqQvwXEdTD6MQ8aAGBIFCB/a25uDpRdYEKIzMzMPXv2GOn6BgAACAqQ/wXcCNDx48ePHDkiOwgAAFqiAPmV0+lsb28PiGPgvTIzM4UQ7AUDABgMBcivWlpaPB5PAO0Cmz59emhoKAUIAGAwFCC/8p4GOoB2gamqmpGRwYFgAACDoQD5lbcABdAIkOBAMACAEVGA/CrgRoCEEFlZWVVVVU6nU3YQAAA0QwHyq+bm5tDQULvdLjvICGRlZbnd7srKStlBAADQDAXIrwLrGHivrKwsIcTu3btlBwEAQDMUIL8KrLMgekVHR8fHxzMPGgBgJBQgv2ptbQ2gkwD1y8rKYgQIAGAkFCC/amxsDLhdYEKInJycXbt2yU4BAIBmKEB+FYhzgIQQWVlZra2t3kPYAAAwAAqQ//T29h49ejQQC1B2drYQoqysTHYQAAC0QQHyn6NHj/b29gZiAUpNTQ0JCWEaEADAMChA/hOIZ0H0MpvNM2bM4HzQAADDoAD5T1NTkxAiEI8CE0JkZ2ezCwwAYBgUIP9pbW0VAVuAcnJyqquruSAGAMAYKED+09TUZLFYxo8fLzvIaOTk5Lhcrr1798oOAgCABihA/tPS0hIbG6soiuwgo5GTk6MoCnvBAADGQAHyn+bm5gDd/yWEiIyMnDp1KqdDBAAYAwXIfwL0LIj9cnNzGQECABgDBch/WlpaAroAZWdn79692+PxyA4CAMBYUYD8p6WlJSYmRnaK0cvNze3o6Dh06JDsIAAAjBUFyH8CfRdYTk6OEIJpQAAAA6AA+UlHR0dPT09AF6CEhITo6OjS0lLZQQAAGKshChATPrQSuNfBOFlOTg4jQAAAAxiiAOXk5KxevbqmpsY/aQyspaVFBH4Bys3NZQQIAGAAQxSgq6+++vnnn09KSjr//PPXrFlz/Phx/8QynoC+EFi/3Nzc5ubmxsZG2UEAABiTIQrQfffdV15e/sUXX+Tn569cuTIuLu6aa67ZsGGD2+32Tz7DaGlpURQloI8CE0Lk5eUJ5kEDAALf0JOgFUXJz89/+OGHS0pKrrrqqj//+c9LliyZMmXKz372M4fD4YeIxtDa2hoZGWm1WmUHGZPk5GS73U4BAgAEuqELUENDw9NPP33hhRdOmjRpy5YtP/7xjz/99NPVq1e/8sor11xzjR8iGkNTU1OgTwASQqiqmp2dzTQgAECgMw9+98KFC7du3ZqUlHTllVeuWrWqsLDQey3PefPmTZo0iQI0fN4rocpOoYGZM2e+//77slMAADAmQxSguXPnPvLII/2955S7PvnkE58FM5pAPwtiv7y8vGeffbatrS06Olp2FgAARmmIXWDFxcX5+fknt5+GhobLL79cCBEVFeU9NTCGo7m5eeLEibJTaMA7D5q9YACAgDbwCNDWrVu3bt0qhPjoo49Wr16tqmr/XdXV1Qz8jEKgXwisX3p6us1mKy0tPffcc2VnAQBglAYuQNXV1e+99573540bN548AmQymR555JEhl7tjx46nn376mWee0SRloHO5XO3t7caYA2Q2m7OyshgBAgAEtIEL0A033HDDDTcIIQoLCz/66COzeYipQqdoamp64oknTCYuNPYvra2tHo/HGAVICJGXl7d582bZKQAAGL2Bm80XX3wxceLExMTEp59++ssvvzz9AYWFhWdaosvlevjhh6+99to///nPmsUMcN7TQBtjDpAQIjc3d+3ate3t7ZGRkbKzAAAwGgMXoKKioh/96EePPfZYUVHRgA8Y5CKpzz777IIFCzIyMk65vaenxzuvSAgxbdq0uLi4UQXWmMlkCgkJ8fVa2tvbhRCTJ0/2w7pMJpOqqj5dUWFhocfjqaioWLBgge/W0s8PW+Rn3kl1FovFSKOkqqpaLJaT5wsGOlVVFUUx0h+e8Ncnnp+ZzWYjbZTZbDbYy+TdiWS1WgdsDoqi+PoTvq+vb+BgA97a3d3t/SBzuVwjWs2mTZuOHz++dOnSurq6U+46duzYT37yE+/P119//W233TaiJftOeHi4r1fR0dEhhEhOTvbDuoQQJpPJYrH4bvlz5861Wq179+5dsmSJ79ZyikA/ifbp7Ha77AgaM95rJPzy+eBnxtuikJAQI9UFL+Nt0bhx4wa+w/s/T778s3Q6nQPePnABstls/7rbbN66devUqVOnTZv21ltvrVmzZvbs2XffffeZVlNSUlJeXv7973/f7XYfO3bspptu+vWvfz1+/HghxMSJEz/++GPvw0JCQlpbW8e0QRoJDw/3thOfOnDgQEhIiMvl8sNWR0VFuVyurq4un64lPT39s88+88+LqKqqzWbz9Rb5k9lsjoyMbG9vN9I19cLCwhwOh8G2yGKxHDt2THYQLfnnE8+foqOju7u7u7u7ZQfRjNVqVVXVYFsUHh5+9OjRAUdiIt3uXoej08ffJgMehT3E7OZf/epXd9111zvvvBMdHf3tb3/7oosu+sMf/nDixInVq1cP+PgVK1Z4f6itrb3//vufffbZ/rtMJlNERET/P/XzJhxkd55WWlpaJkyY4IcViX9vjq/XlZeXt337dr9tkZcf1uUf/dtisI0y5MtkpC3yMuQWGWmjjPdW8hpko2Rt7xBTEH7/+98//PDDS5YsefHFF3Nzc9etW/frX//6lVde8U84wzDMdTD65eXl7du378SJE7KDAAAwGkMUoObm5oULFyqK8vHHH3snfGRkZDQ2Ng653KlTp548/BPkjHEl1JPl5ub29vbu3r1bdhAAAEZjiAKUlpa2bt26qqqqDRs2XH311R6PZ+PGjVOnTvVPOMMw3ghQTk6OqqqcDhEAEKCGKED333//o48+OmPGjEWLFqWkpPz0pz/9yU9+8qMf/cg/4QyjtbXVYAUoNDQ0JSVl165dsoMAADAaQ0yC/vrXv15VVbV///5zzjlHCHHhhRcuXrx44cKFfslmEB6PxzCXgj9Zbm4uBQgAEKCGvsZFfHx8b29vbW2tECIxMVEIUV1dnZqa6vNoRtHe3u50Og1ZgN5++22n02nIs78AAIxtiAK0bt26b3/72w6H45TbjXeEnu80NzcLIQy2C0wIkZub63Q6q6qqsrOzZWcBAGBkhpgDdNddd1122WV79+5t/k/+CWcM3rMFGm8EKCcnRwjBXjAAQCAaYgTo6NGjK1euTE9P908aQzLYlVD7RUdHT5kypaysTHYQAABGbIgRoPPOO6+8vNw/UYyqubnZZDJ5rwdiMNnZ2ZwKCAAQiIYYAbrzzjtvueWWsrKygoKC0NDQ/tsvvPBCHwczjpaWlvHjx3svh2sw2dnZzz//vOwUAACM2BDfyuedd54Q4vQrf/X09PgqkeE0Nzcbb/+XV05OztGjR+vq6qZMmSI7CwAAIzBEAaLojMKF/WYAACAASURBVF1zc7PxDgHz8s6D3r17NwUIABBYhpgDJIRoa2t76623Hn300aNHj5aVlblcLj/EMhJDngXRa9q0aWFhYUwDAgAEnCFGgPbs2fOVr3zFZDK1tLRcccUVd9xxx+HDhzds2DBt2jS/xDOC1tbWWbNmyU7hEyaTKTMzkwIEAAg4Q4wA3X777QsXLqypqQkPDxdCvPDCC/Hx8bfffrtfshlEU1OTUecACSEyMzP37t0rOwUAACMzRAHaunXrnXfe2X+tg7i4uHvuuefvf/+774MZRE9PT2dnp1F3gQkhMjMz9+3b53Q6ZQcBAGAEhihA0dHRp8yD7u7uDgsL82UkQ/GeNdvYBcjtdldVVckOAgDACAxRgJYtW7Zy5cqWlhbvP8vLy1esWPHVr37V98EMwluAYmJiZAfxlczMTCHEnj17ZAcBAGAEhihAv/zlL+Pi4uLj49vb24uKirKystLS0n7961/7J5wBeAuQgecAxcTETJgwgWlAAIDAMsRRYHa7/bXXXtu7d29ZWZnb7c7KysrLy/NPMmMw/C4wIURWVhbXSwEABJaBC9CHH354yi1RUVFCiKamJu9dXApjmFpaWsLDw202m+wgPpSRkfHuu+/KTgEAwAgMXIAuu+yy/p9PnDjh8XiEEIqieDyesLCwadOmcQ3wYWpsbDT28I8QIj09/Q9/+MPx48cjIiJkZwEAYFgGngPU+W8vvfTStGnTNmzY0N3d3d3d/f7778fFxT300EN+Thm4DHwa6H7p6ekej4cDwQAAAWSISdD33nvvo48+unjxYpvNFhISctFFFz3yyCMrV670TzgDCJICJISorKyUHQQAgOEaogAdOnRo/PjxJ98SFRV16NAhX0YyFANfCr5fZGRkfHx8RUWF7CAAAAzXEAVozpw5DzzwwPHjx73/bG9v/8UvfjF37lzfBzOIYBgBEkKkp6dTgAAAAWSIw+CfeOKJr3zlK0lJSXPmzBFCbNu2zW63b9myxS/ZAp7T6Wxvbw+GAjRjxoz33ntPdgoAAIZriBGg9PT0qqqq1atXZ2Rk5OTkPPLII9XV1ampqf4JF+haWlo8Hk8wFKD09PTa2trOzk7ZQQAAGJYhRoCEEOHh4TfffLMfohiP9xIisbGxsoP4XEZGhvdAsNmzZ8vOAgDA0IYYAcJYNDU1CSHi4uJkB/G5tLQ0IQRHwgMAAgUFyIeC4ToYXtHR0dHR0dXV1bKDAAAwLBQgH2pubg4NDbXb7bKD+MOMGTMYAQIABAoKkA81NTUZ/iRA/dLS0ihAAIBAQQHyoaAqQKmpqQcOHHC73bKDAAAwNAqQDzU3NwfDDGivtLQ0p9PJWcIBAAGBAuRDQTUCxIFgAIAAQgHyoebm5mA4CZBXQkJCSEgIBQgAEBAoQL7idDqPHTsWPLvAVFVNTk7et2+f7CAAAAyNAuQrjY2NHo8neHaBCSFSUlI4FRAAICBQgHzFexrooCpAqampFCAAQECgAPlK8JwGul9qampra+vRo0dlBwEAYAgUIF8JzhEgIQTTgAAA+kcB8pWmpqaoqKiQkBDZQfzHW4DYCwYA0D8KkK80NzcH1fCPECIyMjI2NpYRIACA/lGAfKWpqSmoJgB5MQ8aABAQKEC+QgECAEC3zLJWrCiKrFWfwkdJGhsbCwoK/L+Z3jXKenpTUlL+8pe/9PX1qaqq1TKVf9NqgdL1b4uRNsrLSFsk963kO4bcIkNulOwIGhvkZZL1CkorQGFhYbJWfTKz2eyjJE1NTQkJCf7fTJPJZLFYZD292dnZTqfz6NGjSUlJWi1TURRVVXXyB6MJk8kkhAgNDe3r65OdRTNms9lkMnk8HtlBNGOxWEwmk5H+8IQvP/FkURTFarV631PGYDKZvB96soNoxrstdrt9wM8Hk8mk+PjP0u12D3i7tALU2dkpa9Uni4iI8EWS9vb27u7uqKgo/2+m2Wx2u92ynt7JkycLIUpLS2NiYrRapqqqNputq6tLqwVKZzabrVZrd3e3y+WSnUUzYWFhDofjTB80gSgsLMxqterkk0orPvrEkygkJMThcHR3d8sOohmr1aqqqpG2KCQkxGKxdHV1Dfi/fFF9fW6Xy9d/luPGjTv9RuO0Zl1paGgQQgTPhcD6TZ8+3Ww2cyAYAEDnKEA+0djYKIKyAFmt1qlTp1KAAAA6RwHyiaAtQEKIlJQUChAAQOcoQD7R2NhotVojIyNlB5GAAgQA0D8KkE80NTXFxcUZ7zjG4UhJSamtrXU4HLKDAABwRhQgn2hoaIiPj5edQo6UlJS+vr4DBw7IDgIAwBlRgHyisbExOCcACSFSUlIEl0QFAOgbBcgngrkATZkyJTQ0lGlAAAA9owD5RDAXIEVRpk+fTgECAOgZBUh7J06c6OzsnDhxouwg0nBJVACAzlGAtFdfXy+EmDRpkuwg0nAkPABA5yhA2vNeByPIC1BbW9vRo0dlBwEAYGAUIO15R4CC9jB4IURqaqoQgkEgAIBuUYC019DQEBISEhUVJTuINBwJDwDQOQqQ9urr6ydNmhScp4H2ioqKio2NZQQIAKBbFCDteQuQ7BSScSAYAEDPKEDaC+brYPRLTU2tqqqSnQIAgIFRgLTHCJAQIjU19cCBA263W3YQAAAGQAHSWF9fX1NTEwUoNTXV6XTW1tbKDgIAwAAoQBprbW11Op3sAktLSxNCsBcMAKBPFCCNcRZEr8TERKvVyjxoAIA+UYA05i1AjACZzeZp06YxAgQA0CcKkMbq6uoURaEACSHS0tIoQAAAfaIAaayuri42NjYkJER2EPnS0tIqKytlpwAAYAAUII3V1dVNmTJFdgpdmDFjRltbW1tbm+wgAACcigKkMQpQvxkzZgghGAQCAOgQBUhjdXV1kydPlp1CF9LS0hRFoQABAHSIAqQlj8dTX1/PCJCX3W6fMmUKBQgAoEMUIC21tLQ4nU4KUD/mQQMA9IkCpKUjR44IIdgF1m/GjBkUIACADlGAtOS99BUjQP3S09OPHDly/Phx2UEAAPgPFCAtHTlyRFXVuLg42UH0IiMjw+PxVFRUyA4CAMB/oABpqa6uLj4+3mw2yw6iFxkZGYqi7N27V3YQAAD+AwVISxwDf4rw8PApU6aUl5fLDgIAwH+gAGmppqYmISFBdgp9SU9PZx40AEBvKEBaOnz4cGJiouwU+pKZmckIEABAbyhAmunp6WlpaZk6darsIPqSnp7e1NTEFcEAALpCAdJMbW2tx+NhF9gpsrOzhRAMAgEAdIUCpJmamhohBAXoFOnp6RaLZffu3bKDAADwfyhAmjl8+LCiKOwCO4XVak1JSaEAAQB0hQKkmdra2tjY2NDQUNlBdCc7O3vPnj2yUwAA8H8oQJrhELAzycrK2rt3r9vtlh0EAIB/oQBphpMAnUlOTk5PT8/+/ftlBwEA4F8oQJqhAJ1JVlaWEIJpQAAA/aAAaaOnp6exsTEpKUl2ED2Kj4+PiYkpKyuTHQQAgH/R/rKdmzZtWrduXWdn59SpU2+99dYguTbWoUOHPB7PtGnTZAfRqby8vNLSUtkpAAD4F41HgA4fPvzMM8+sXLnyueeey8jIeOyxx7Rdvm4dPHhQCDF9+nTZQXRq5syZFCAAgH5oXIBaWloWLVoUHx9vNpvPPffcI0eOaLt83Tp48KDZbA6S4a5RyM3NbWtr854rEgAA6TTeBZafn5+fny+EcDqdr7zyyoIFC/rv6ujoeP75570/FxQUzJ49W9tVj46qqmFhYWNfTm1tbVJSUmRk5NgXNUYmk8lsNmuyURo666yzhBCVlZUZGRkj/V1FUXS4RWNhMpmEEDabzWq1ys6iGYvFYjKZ+vr6ZAfRjHeLjPSHJ7T7xNMPRVGsVqv3PWUMpn+THUQzqqoKIex2u8fjOf1ek8lksVh8+md5ppOwaD8HSAjx2WefrVmz5qyzzrrhhhv6b+zp6fnwww+9P0dGRnq/EaUzmUyafAkdOnQoJSVFD99niqKoqqooiuwg/yE9PX38+PFlZWVXXnnlSH9X+TdfBJPCuy0Wi2XAj4MA5f3INtgWeb9cZQfRklafeLqiqqqR6oL3485gWySEsFgsZ7pX8fGf5Zm+PjQuQB6P5+mnnz5w4MDKlStPOSZ8woQJb775Zv8/W1patF316ERERBw/fnzsy6murp4/f/7Ro0fHvqgxioqKcrvdnZ2dsoOcKjs7e9u2baN4ilRVtdlsXV1dvkglhdlsjoqK6ujocLlcsrNoJiwszOFwGOl0l2FhYVarVQ9vag1p9YmnHzExMd3d3d3d3bKDaMZqtaqqaqQtCgkJCQ8Pb29vH3CEOKq31+1wdPr4jWaz2U6/UeOOWVJSUl5evmrVqqA6I05fX9+hQ4c4BGxws2bNKikpkZ0CAAAhNB8BKisrO3To0FVXXeX9p91uf/HFF7VdhQ4dOXLE6XRSgAY3e/bsxx9/vLa2luvFAgCk07gAfec73/nOd76j7TL178CBA4Jj4Ifinfb+5ZdfUoAAANIZZ5qVRNXV1SaTiQI0uISEhNjYWPaCAQD0gAKkgf3790+ZMmXAOVY42cyZMylAAAA9oABpYP/+/cnJybJTBIDZs2fv3LnTSKeKAQAEKAqQBvbt25eSkiI7RQDIz88/fvx4VVWV7CAAgGBHARort9t9+PBhRoCGo6CgQFGU4uJi2UEAAMGOAjRWhw4dcrlcjAANR3R09LRp03bs2CE7CAAg2FGAxmrfvn1CCArQMBUWFn7xxReyUwAAgh0FaKz2799vsViC6szXY5Gfn793714jXdcCABCIKEBjVVlZOX36dLPZJ5eVNZ7CwsLe3l4OhgcAyEUBGqvKysoZM2bIThEwsrOzQ0ND2QsGAJCLAjRWVVVVaWlpslMEDIvFMnPmTAoQAEAuCtCYtLS0tLW1MQI0IoWFhdu3b/d4PLKDAACCFwVoTCorK4UQFKARKSwsbGtrO3jwoOwgAIDgRQEak8rKSpPJlJqaKjtIIJkzZ44QYvv27bKDAACCFwVoTCorK6dMmWK322UHCSQTJkxITExkGhAAQCIK0JhUVVWx/2sUioqKGAECAEhEARqTvXv3ZmZmyk4ReAoLC8vLyzs6OmQHAQAEKQrQ6B07dqyhoSE9PV12kMBTVFTU29v75Zdfyg4CAAhSFKDRKy8vF0IwAjQK2dnZdrudaUAAAFkoQKNXXl5uMpk4C+IomM3mWbNmMQ0IACALBWj0Kisrk5KSOARsdIqKinbs2MHpEAEAUlCARm/Pnj1ZWVmyUwSqgoKCtra2/fv3yw4CAAhGFKDRq6ioyMjIkJ0iUBUVFQkhmAYEAJCCAjRK9fX1bW1tFKBRi42NTUpKogABAKSgAI3S7t27hRA5OTmygwSwwsJCChAAQAoK0CiVlZXZbLbp06fLDhLAvKdD7Orqkh0EABB0KECjtGfPnszMTFVVZQcJYAUFBZwOEQAgBQVolMrKytj/NUY5OTk2m23Hjh2ygwAAgg4FaDR6enr279+fnZ0tO0hgs1gsubm5xcXFsoMAAIIOBWg0ysvLe3t7GQEau4KCAuZBAwD8jwI0GmVlZYqicBWwsSsoKGhsbKyrq5MdBAAQXChAo1FaWjpt2rSIiAjZQQJeQUGBEIJpQAAAP6MAjUZpaWleXp7sFEaQkJAwYcIEpgEBAPyMAjRibrd79+7dM2fOlB3EIAoKChgBAgD4GQVoxCoqKhwOByNAWsnPz9+5c6fb7ZYdBAAQRChAI7Zz504hRG5uruwgBpGfn9/d3V1RUSE7CAAgiFCARmzXrl0JCQnR0dGygxjErFmzTCYTe8EAAP5EARqxkpKSWbNmyU5hHJGRkampqcyDBgD4EwVoZJxOZ1lZWX5+vuwghpKfn08BAgD4EwVoZMrKyhwOh/fsNdDKrFmzKioqOjs7ZQcBAAQLCtDIFBcXq6rKIWDays/P7+vrKy0tlR0EABAsKEAj8+WXX6anp4eFhckOYijZ2dlWq5W9YAAAv6EAjUxxcfHs2bNlpzAaq9Wam5tbUlIiOwgAIFhQgEagra2turp6zpw5soMY0OzZsylAAAC/oQCNwPbt2z0eT1FRkewgBjR79uyamprm5mbZQQAAQcEsa8UWi0XWqk9mMpmGn2T79u0xMTGZmZmKovg01VgoijKijdKJs846SwhRWlq6ePHiU+4ymUyBuEWDUFW1/7+GYTKZzGaznt8aI6WqqqIoRvrDEyP8xAsUqqoaaaNUVTXYy+T9rDObzR6P5/R7/fCdNeB6hcQCZDZLW/XJFEUZfpJt27bNmzdP53+X3j8mnTy9w5eenh4dHV1SUrJ06dJT7vIWoIDbokGYTCbx7+9X2Vk0YzKZDLZF3m0x0h+eGOEnXqAw2OeDtwAZbIvEUAXIp9vb19c34O3SnuLu7m5Zqz6ZxWIZZhKn01lSUnL33XfrJPmZhISEuN1unYcc0MyZM7dt23Z6clVVbTZbIG7RmZjN5tDQUKfT6XK5ZGfRjMlkcjgcRrqorbd5G+kPT4zkEy9Q2O12l8tlpI2yWq2qqhppi0JCQkJCQnp6egYsIiF9fX74zgoPDz/9RuYADdeOHTscDgczoH3HOw/6TGOVAABoiAI0XJ988sm4ceO4Cpjv5Ofnt7e379u3T3YQAIDxUYCG65NPPpk7d67OJwAFNO8V1rgsPADADyhAw9Ld3V1cXLxw4ULZQYxswoQJiYmJnA8aAOAHFKBh+fzzz51O54IFC2QHMbiCgoIvvvhCdgoAgPFRgIbl448/jo2NzcrKkh3E4AoLC8vLy410+AMAQJ8oQMOycePGCy64wHv6FvhOfn6+y+XisvAAAF/jG31o+/bt279//6JFi2QHMb68vDyr1cpeMACAr1GAhrZx40aLxXLuuefKDmJ83svCU4AAAL5GARra+++/P3fu3MjISNlBgkJRUdE///lP2SkAAAZHARpCY2Pj559//rWvfU12kGAxZ86cpqamQ4cOyQ4CADAyCtAQ3njjDUVRTr9CJ3zEe1n4bdu2yQ4CADAyCtAQ1q9fv3DhwtjYWNlBgsXEiRMTExO3b98uOwgAwMgoQIOpqqoqLi6+/PLLZQcJLnPmzGEaEADApyhAg1m7dm14eDgTgPzsrLPO2rt3b1tbm+wgAADDogCdUU9Pz1/+8perr77abrfLzhJc5s2b19fXxyAQAMB3KEBntG7dumPHjl1//fWygwSdtLS0CRMmfPbZZ7KDAAAMiwI0sN7e3scff/yiiy5KT0+XnSXoKIoyd+5cChAAwHcoQAN788039+/f/6Mf/Uh2kCA1b968Xbt2dXR0yA4CADAmCtAAXC7XL3/5y6985StFRUWyswSp+fPnu93uTz/9VHYQAIAxUYAGsGbNmoMHD65cuVJ2kOCVkZExefLkzZs3yw4CADAmCtCpWltbH3744a9//eszZ86UnSWoLVy4cNOmTbJTAACMiQJ0qvvuu8/tdjP8I93ChQurq6trampkBwEAGBAF6D9s2rRp3bp199xzz6RJk2RnCXbnnnuuoigMAgEAfIEC9H86OjpWrFhRWFj43e9+V3YWiAkTJsyePfu9996THQQAYEAUoP/zs5/9rKWl5fe//72qqrKzQAghLrnkks2bN584cUJ2EACA0VCA/mXjxo0vvvjifffdl5KSIjsL/mXx4sUOh+Ojjz6SHQQAYDQUICGEaGtrW7FixcKFC2+66SbZWfB/MjIypk+f/u6778oOAgAwGgqQEELccccdDofjd7/7naIosrPgPyxbtuzdd9/t6uqSHQQAYCgUILFu3bp33nnnoYcemjJliuwsONXVV1/d1dX15ptvyg4CADCUYC9AtbW1d99999KlS6+66irZWTCA1NTUwsLCl156SXYQAIChBHUB8ng8y5cvt1qtjzzyiOwsOKNrr7128+bNVVVVsoMAAIwjqAvQH//4x48++ug3v/lNdHS07Cw4o6uvvjo6OvqJJ56QHQQAYBzBW4Cqqqp+/vOf33DDDRdffLHsLBhMaGjoLbfcsm7duvr6etlZAAAGEaQFyOl03nLLLRMnTnz44YdlZ8HQfvCDH9jt9gceeEB2EACAQQRpAXrooYd279791FNPhYeHy86CoY0fP/6uu+567bXXPv/8c9lZAABGEIwF6MMPP3zqqafuuOOOoqIi2VkwXDfccENubu4Pf/jDtrY22VkAAAEv6ApQTU3NrbfeumDBghUrVsjOghEwm83PPfdcR0fHd7/7Xc6LCAAYo+AqQJ2dndddd11oaOj//u//csXTgDNt2rQ1a9Z8+eWXV1xxxeHDh2XHAQAEsCAqQE6n88Ybbzx06NCf/vSnmJgY2XEwGvPnz3/ttdeOHDmycOHCVatWNTc3y04EAAhIwVKA3G73D3/4w08++eSZZ57JycmRHQejV1RUtHXr1m9+85tPPPFEXl7eN77xjT/84Q/l5eUej0d2NABAwDDLDuAPPT093//+971znxctWiQ7DsYqMjJy9erVd9xxx+uvv/7uu+/ef//9LpcrMjKyoKAgLy8vMzMzISFh4sSJERERbre7q6vr2LFj7e3tHR0dfX194eHhcXFxycnJNptN9nYAAKQxfgGqqam54YYbKioqnnvuuSVLlsiOA81MmDDh5ptvvvnmmzs6Oj7//PPt27eXlJSsXbv26NGjQ/6u2WyeNWvW4sWLr7zySi6CCwBByMgFqK+v7+WXX77vvvvGjRv31ltv5efny04EnwgPD1+0aFH/2F5LS8uhQ4fa2tq6u7u9944bNy4yMjIkJMRkMrnd7rq6uj179mzevPlXv/rVqlWrLrnkkttuu40/DwAIKoYtQB9//PHq1atLSkouu+yyX/7yl1ztK3jExsbGxsYO8oDp06fPnz9/+fLl7e3tr7766tNPP33xxRdfeOGFd91116xZs/yWEwAgkfaToIuLi2+99dbvfe97jz/+uNPp1Hz5w/H2229fffXVvb29r7322jPPPEP7wYAiIyOXL1++bdu23/3ud1VVVRdddNG3vvWt7du3y84FAPA5jUeATpw48Zvf/ObnP/95UlLSqlWrNmzYsGzZMm1XMRzeWSAbNmywWq3+XzsCi8Viueaaa6688spXX331t7/97ZIlS/Lz86+99tpLL710ONXZu0+trq6uoaGhs7PzxIkTVqvVbrfHxMRMnTo1MTExLCzMD1sBABgRjQtQSUlJcnJycnKyEGLJkiUvv/yylAIEjJTFYrnuuuuuueaav/3tb3/84x//3//7f3fdddfMmTMLCgrS09MnTZpkt9tDQkJOnDjR3t7e2NhYW1t74MCBqqqqQ4cOuVyuQZY8adKktLS01NTUtLS0pKSkhISExMTEqKgov20aAOB0Gheg5ubmiRMnen+Oi4traWnpv6utre3ee+/1/nzRRRd99atf1XbVJwsNDRVCXHfddSZTsJzoqJ+iKJwRZ4xCQkJSU1MrKyuLi4uLi4uH8yvx8fETJ06Mjo72nmG8q6urvr6+vr7e6XR6f9iyZcspq4iKivLOzlZVNSIiwidbgrHh3aR/vEYBYZCX6cn9+61RUVMiI3239jP9P6rGBeiULezr69N2+cN09tlnX3zxxW63W8raYQAJCQkJCQmj/vWwsLDU1NTU1NQzPYBPbQCQS+MCFBsbW1pa6v25paXl5INxoqOjn3zyyf5/njw4pLmpU6f+6U9/Gs4jIyIijh8/7rsk/hcVFeV2uzs7O2UH0YyqqjabzUgXQDWbzVFRUe3t7YPvOwssYWFhDofDSP/XERYWZrVah3NaqQBivE+8mJiYEydOeM95YQxWq1VVVSNtUUhISHh4eFtb24BjIlGLFrnj49vb232awW63n36jxnuICgoKqqqq6uvrPR7Pxo0b58+fr+3yAQAAxk7jESC73X777bevXr3a7XZnZmZeeuml2i4fAABg7LQ/EWJ+fj4n1QUAAHoWdAdJAQAAUIAAAEDQoQABAICgQwECAABBhwIEAACCDgUIAAAEHQoQAAAIOhQgAAAQdChAAAAg6FCAAABA0KEAAQCAoEMBAgAAQYcCBAAAgg4FCAAABB3F4/HIzgAtvfzyy/Hx8eeff77sIDijpqamV1999corr5w0aZLsLDijLVu2HD58+LrrrpMdBIN5+umni4qKCgoKZAfBGVVXV2/YsOHGG2+02+0D3L12rZgwQSxZ4vdcjAAZzttvv/3555/LToHBtLW1rV27trm5WXYQDGbbtm3r16+XnQJDePnll3ft2iU7BQZz8ODBtWvX9vT0DHz39ddLaT+CAgQAAIIQBQgAAAQd5gAZTVNTk9VqjYqKkh0EZ+R0OpubmydMmGC1WmVnwRkdO3bM4XDExcXJDoLBHDlyJDw8PDw8XHYQnFF3d3dbW9ukSZNMJn2NuVCAAABA0NFXHQMAAPADs+wAGLHi4uLnnnuuu7s7Pz9/+fLlp+xGGfDewX8FvjD4c75p06Z169Z1dnZOnTr11ltvnTx5shDijjvuOHDggKIoQojFixd///vflxM9aAz+Gg34cvBW8r9BnvM333zzhRde6P+ny+V6/vnno6OjeSv5X29v7y233PLwww9HRESccpd+v5U8CChdXV3XXXfdvn373G73Aw888MYbbwx57+C/Al8Y/Dk/dOjQtddeW19f73K5XnjhhTvvvNPj8fT19V1zzTUOh0NS5KAz+Gs04MvBW8n/hv+cb9u27X/+5388vJVk+Nvf/vbjH//40ksvbW9vP+UuPX8rsQsswJSUlCQnJycnJ6uqumTJkk8++WTIewf/FfjC4M95S0vLokWL4uPjzWbzueeee+TIESGE97RAjzzyyPLlyx977LGOjg450YPG4K/RgC8HbyX/G+Zz3tPT88ILLyxfvlzwVpIhMTHxqquuslgsp9+l528lClCAaW5unjhxovfnuLi4lpaWIe8d/FfgC4M/5/n5h2hfcAAABMZJREFU+TfccIMQwul0vvLKKwsWLBBCtLe3Z2Rk3HjjjY8//rgQ4plnnvF36CAz+Gs04MvBW8n/hvmcv/zyy5dccklsbKzgrSRDdnZ2UVGRqqqn36XnbyUKUIDx/OdRe319fUPeO/ivwBeG85x/9tln//Vf/xUTE3PTTTcJIdLS0u67776JEydardZrr732n//8p4cjNH1p8NdowJeDt5L/Dec5r6mpKS4uvuSSS7z/5K2kK3r+VqIABZjY2Nj+stzS0uL9P57B7x38V+ALgz/nHo/nqaeeWr9+/cqVK2+88Ubv/zbt379/37593geYzeYB/18KGhr8NRrw5eCt5H/Dec7//ve/n3322f3nmOGtpCt6/laiAAWYgoKCqqqq+vp6j8ezcePG+fPne2+vra11OBwD3numX4HvDP4ylZSUlJeXr1q1KiEhof9XOjo6Vq1adfToUY/H8+abb86bN897DAt8ZPDXaMCXg7eS/w3+MgkhPB7P1q1b586d2/8rvJV0Qv/fSpwIMfAUFxevXbvW7XZnZmbefPPN3nlnl1122UMPPZSVlTXgvQPeCJ8a5GX64osv/vrXv5rN/zoJhd1uf/HFFz0ez9tvv/3uu+96PJ6srKzly5cPfOVkaGeQ1ygzM3PAl4O3kv8N/olXVVW1evXqZ599tr/l8FaS5aqrrnr22Wf7D4PX/7cSBQgAAAQddoEBAICgQwECAABBhwIEAACCDgUIAAAEHQoQAAAIOhQgAAAQdChAAAAg6FCAAOjd888/v3Xr1rE/BgD6UYAA6B0FCIDmKEAAACDoUIAA6Mjrr78+c+bM0NDQpKSkhx9+WAhRWFi4efPmu++++8ILLxRCVFRULF26dMKECeHh4QsWLCguLj79MQAwJK4FBkAvDhw4kJaWtmLFim984xtbtmy58847P/nkk7lz515wwQUXXXTRT37yE1VVMzIyJk2adO+99wohfv7zn3d1de3YscPtdp/8GNnbASAAmGUHAIB/2b9/f29v7/Lly9PS0oqKijIzM6dOnWo2mxVFUVVVVdW+vr4f/OAHX/va15KTk4UQR44cWbFihRDi5MfI3ggAgYECBEAvzjnnnEsvvXTmzJmXXHLJBRdccN1110VERJz8AJPJdMstt2zZsuX111/fsWPHxo0bFUWRlRZAQGMOEAC9sNlsb731VmlpaX5+/ksvvZSQkPDWW2+d/IDOzs5zzjnnxz/+scvluvXWW5988klZUQEEOgoQAL34+OOP77nnnpSUlJ/+9KeffvrpRRddtGbNmpMfsGnTpvLy8u3bt999993z58/v7e2VFRVAoGMXGAC9UFV11apVkZGRF198cXl5+WeffbZ8+XIhhMlkqq6ubmhoiIiIOHHixJ/+9KfFixdv37793nvv7erqqqurmzJlSv9j4uPjZW8HgADAUWAAdOR3v/vdY489VldXFxcX981vfvPBBx+0Wq1r166944475s+fv379+l/84hdPPvmk97CvBx988IorrrDb7f/85z/7H/PGG2/I3ggAAYACBAAAgg5zgAAAQNChAAEAgKBDAQIAAEGHAgQAAIIOBQgAAAQdChAAAAg6FCAAABB0KEAAACDoUIAAAEDQoQABAICg8/8BpojuqmiQ5z8AAAAASUVORK5CYII=" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>slope_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
@@ -373,8 +373,8 @@ <h3>One numerical (one mean)</h3>
 <span class="st">   </span><span class="kw">pull</span>()
 <span class="kw">c</span>(<span class="dt">lower =</span> x_bar <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot),
   <span class="dt">upper =</span> x_bar <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##    lower    upper 
-## 1.122209 8.021791</code></pre>
+<pre><code>## lower upper 
+## 1.122 8.022</code></pre>
 </div>
 <div id="one-categorical-one-proportion-1" class="section level3">
 <h3>One categorical (one proportion)</h3>
@@ -388,8 +388,8 @@ <h3>One categorical (one proportion)</h3>
 <span class="st"> </span><span class="kw">pull</span>()
 <span class="kw">c</span>(<span class="dt">lower =</span> p_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot),
  <span class="dt">upper =</span> p_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##     lower     upper 
-## 0.4194756 0.5125244</code></pre>
+<pre><code>##  lower  upper 
+## 0.4195 0.5125</code></pre>
 </div>
 <div id="one-numerical-variable-one-categorical-2-levels-diff-in-means-1" class="section level3">
 <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
@@ -406,8 +406,8 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">   </span><span class="kw">pull</span>()
 <span class="kw">c</span>(<span class="dt">lower =</span> d_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
   <span class="dt">upper =</span> d_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##     lower     upper 
-## -7.704370  6.213971</code></pre>
+<pre><code>##  lower  upper 
+## -7.704  6.214</code></pre>
 </div>
 <div id="two-categorical-variables-diff-in-proportions" class="section level3">
 <h3>Two categorical variables (diff in proportions)</h3>
@@ -424,8 +424,8 @@ <h3>Two categorical variables (diff in proportions)</h3>
 <span class="st">  </span><span class="kw">pull</span>()
 <span class="kw">c</span>(<span class="dt">lower =</span> d_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
   <span class="dt">upper =</span> d_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##       lower       upper 
-## -0.07149487  0.11258550</code></pre>
+<pre><code>##    lower    upper 
+## -0.07149  0.11259</code></pre>
 </div>
 <div id="two-numerical-vars---slr-1" class="section level3">
 <h3>Two numerical vars - SLR</h3>
@@ -441,8 +441,8 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">pull</span>()
 <span class="kw">c</span>(<span class="dt">lower =</span> slope_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
   <span class="dt">upper =</span> slope_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))   </code></pre></div>
-<pre><code>##     lower     upper 
-## 0.9657595 1.0681384</code></pre>
+<pre><code>##  lower  upper 
+## 0.9658 1.0681</code></pre>
 </div>
 </div>
 </div>
diff --git a/inst/doc/observed_stat_examples.R b/inst/doc/observed_stat_examples.R
index 67be6ff5..4beac9aa 100644
--- a/inst/doc/observed_stat_examples.R
+++ b/inst/doc/observed_stat_examples.R
@@ -35,7 +35,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
 null_distn %>% 
-  visualize(obs_stat = x_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 
@@ -51,7 +52,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
 null_distn %>% 
-  visualize(obs_stat = t_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 
@@ -67,7 +69,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "median")
 null_distn %>% 
-  visualize(obs_stat = x_tilde, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 
@@ -83,7 +86,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
 null_distn %>% 
-  visualize(obs_stat = p_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 
@@ -107,7 +111,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -123,7 +128,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = z_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 
@@ -142,7 +148,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
@@ -158,7 +165,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
@@ -174,7 +182,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -190,7 +199,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = t_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 
@@ -207,7 +217,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -223,7 +234,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
 null_distn %>% 
-  visualize(obs_stat = F_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 
@@ -239,7 +251,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
 null_distn %>% 
-  visualize(obs_stat = slope_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 
@@ -255,7 +268,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
 null_distn %>% 
-  visualize(obs_stat = correlation_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 
@@ -272,7 +286,8 @@ null_distn %>%
 #     generate(reps = 1000, type = "permute") %>%
 #     calculate(stat = "t")
 #  null_distn %>%
-#    visualize(obs_stat = t_hat, direction = "two_sided")
+#    visualize() +
+#      shade_p_value(obs_stat = t_hat, direction = "two_sided")
 #  null_distn %>%
 #    get_p_value(obs_stat = t_hat, direction = "two_sided")
 
@@ -287,9 +302,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( t_hat <- fli_small %>% 
@@ -302,9 +321,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( p_hat <- fli_small %>% 
@@ -317,9 +340,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>%
@@ -332,9 +359,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( t_hat <- fli_small %>%
@@ -347,9 +378,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>% 
@@ -362,9 +397,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( z_hat <- fli_small %>% 
@@ -377,9 +416,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( slope_hat <- fli_small %>% 
@@ -392,9 +435,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between") 
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( correlation_hat <- fli_small %>% 
@@ -407,10 +454,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")  
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ----eval=FALSE, echo=FALSE----------------------------------------------
 #  # **Point estimate**
@@ -424,7 +475,11 @@ boot %>% visualize(endpoints = standard_error_ci, direction = "between")
 #     generate(reps = 1000, type = "bootstrap") %>%
 #     calculate(stat = "t")
 #  ( percentile_ci <- get_ci(boot) )
-#  boot %>% visualize(endpoints = percentile_ci, direction = "between")
+#  boot %>%
+#    visualize() +
+#      shade_confidence_interval(endpoints = percentile_ci)
 #  ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-#  boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+#  boot %>%
+#    visualize() +
+#      shade_confidence_interval(endpoints = standard_error_ci)
 
diff --git a/inst/doc/observed_stat_examples.Rmd b/inst/doc/observed_stat_examples.Rmd
index 3a2f3dc7..30404cda 100644
--- a/inst/doc/observed_stat_examples.Rmd
+++ b/inst/doc/observed_stat_examples.Rmd
@@ -70,7 +70,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
 null_distn %>% 
-  visualize(obs_stat = x_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
@@ -91,7 +92,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
 null_distn %>% 
-  visualize(obs_stat = t_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
@@ -114,7 +116,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "median")
 null_distn %>% 
-  visualize(obs_stat = x_tilde, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
@@ -136,7 +139,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
 null_distn %>% 
-  visualize(obs_stat = p_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
@@ -174,7 +178,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -196,7 +201,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = z_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
@@ -225,7 +231,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -247,7 +254,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -269,7 +277,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -291,7 +300,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = t_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -316,7 +326,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -338,7 +349,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
 null_distn %>% 
-  visualize(obs_stat = F_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 ```
@@ -360,7 +372,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
 null_distn %>% 
-  visualize(obs_stat = slope_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
@@ -382,7 +395,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
 null_distn %>% 
-  visualize(obs_stat = correlation_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
@@ -407,7 +421,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "t")
 null_distn %>% 
-  visualize(obs_stat = t_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -431,9 +446,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical (one mean - standardized)
@@ -452,9 +471,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -474,9 +497,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One categorical variable (standardized proportion $z$)
@@ -499,9 +526,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -520,9 +551,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -542,9 +577,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two categorical variables (z)
@@ -563,9 +602,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -585,9 +628,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between") 
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two numerical vars - correlation
@@ -606,10 +653,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")  
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -631,7 +682,11 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")  
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
diff --git a/inst/doc/observed_stat_examples.html b/inst/doc/observed_stat_examples.html
index 3cba2531..bb6704a4 100644
--- a/inst/doc/observed_stat_examples.html
+++ b/inst/doc/observed_stat_examples.html
@@ -132,10 +132,12 @@ <h3>One numerical variable (mean)</h3>
 <span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">10</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)</code></pre></div>
+<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -177,10 +179,12 @@ <h3>One numerical variable (standardized mean <span class="math inline">\(t\)</s
 <span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">8</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)</code></pre></div>
+<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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eCU4SWFQhEVFVVbW+vbJawCJwTnimGY2NjY+vr6xsZGuWtpISIiwmQy+fECnn7RoUMHs9lcX18vdyEtqNVqQRCam5vlLqQFrVbLsuytW7fkLqQFnueVSqXtWjxtwXzyCbt0qX2Litew/fr7VhXP8z7/H1Tf19vJmL6N5Yooijt37jx27JjFYunRo8fLL78sXcq2tLR0y5YtJpNJr9fn5uYqlUpXjQAAAN7w82dg33zzTUlJycaNG7du3RoVFfXhhx8SQoxG47p16xYsWLB58+ba2toDBw64agQAAPCSnwOsU6dO8+fPDwsLE0VRrVZLV7IvKyvT6XQ6nY7juHHjxp04ccJVIwAAgJf8/BZiYmIiIeTYsWPr16/v2LGjdJnBGzduxMfHSwskJCQYDAZXjZKamhrblWfHjx8/9f4B/i3SlejoaFddHMe56ZUFwzCEEK1WG2pXdAvBuZJoNBq1Wi13FS2wLKtQKEJtDbIsq1QqQ20lsiwriqJGo5G7kBY4jiNudx2yYBiGYRi/fCjTHBbW1LKFV/C8TyNLuyzfqhIEwWl7QE7iGDFiRP/+/ffu3bt+/fpFixY5/P+USnHaKOE4rkuXLtLtyMjIoP33dvNZOsuyofZJO8uyHMdZrdYQ3P2F2lwRQnieFwQh1ApjGCYE1yDHcaIohtpcEUJEUXS1I5MLy7LE7a5DFizL+uu/oZMJF0Wft1iG8fMllP0cYIcPH2ZZNisrS6PRDB8+fPny5YSQuLi48vJyaQGDwRAXF+eqURIdHV1YWGi7G7yzEOvqXHVFRkbWue6VhXQWotFoDMGzEENtrhiGUalUjY2NOAvRGzgL0XvSWYihtsH78yzEpiaHz5ksFqtgNvtWFcuyZp/+lrjIKj9/BqZWq/fs2dPY2CiK4smTJ3v37k0IycjIqKysvHLliiiKxcXFmZmZrhoBAAC85OcjsMGDB1+6dGnhwoXNzc09evSYM2cOIUSj0eTl5RUWFloslrS0tAkTJrhqBAAA8JKfA4xhmOnTp0+fPt2hXa/X6/V6bxoBAAC8QcEvcQC0Jp2/d0d/wvO8SqXycmGr1RpqHy4CgAMEGFApPDzc+zSSqNVq70+jF0WxpqbmzusCgOBBgAGtqqqqVq9eHYiRH3roodZvgwNAqEGAAa0EQWhoaAjEyD6f6QsAwYTrgQEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJV7uAjxjGCY4D8RxnJsa3PTKgmVZ6d9QKyw4cxWErSI4z0Jaj6EmZDf4UKuKYZjQnCu/7RlabZ8M06b/ff79n0tBgPF8kIoMCwtz1cVxnJteWUj/n1UqlSiKctfSQnDmKgi7jCA8C57n1Wp1qK1BlmV5ng+1DV5a40HbG3iJ4ziGYUJtrqQA80tVFoXC3LKF4zjOp7UgRZdva1AQBKftobU1OGU2mz0v5A/19fWuuiIjI930ykKhUCiVSpPJZLFY5K6lheDMlVarDfRDBOFZREREmEwmq9Ua6Ae6IwqFwmw2h9oGr1arBUFobm6Wu5AWtFoty7KhNlc8zyuVSqPR2PahmKYmh0Mwi8Uq+LRP5nmeZVmf9+cKZ42h+PYFAACARwgwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKFFyRGSDIFAoFIUSlUgVofFEUQ+3KwgA0QoABONLpdIQQrVYbuIeora21WCyBGx+gPUCAAThiWVYUxdzc3EAMfs899yxZsiQQIwO0NwgwAOcaGhoCMazJZArEsADtEE7iAAAAKiHAAACASggwAACgEgIMAACohAADAAAq4SxECBSlUslxXIAGD9zIAEALHIFBoISHhyuVygANjgADAByBQQAdPHhw9+7dgRh5w4YNgRgWACiCIzAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqOT/0+iPHj26Z8+e+vr6rl27zp07t3PnzoSQ0tLSLVu2mEwmvV6fm5srfT3IaSMAAIA3/HwE9q9//Wvz5s1Lly7dsmVLampqUVERIcRoNK5bt27BggWbN2+ura09cOCAq0YAAAAv+fkIzGAwPPzww4mJiYSQrKysgwcPEkLKysp0Op10mfZx48bt3r170qRJThulQcxmc1VVlXQ7NjY2mg3S+5w873I2GIZx0ysL6acoQvAHKWxzxTCM3LWELmnFsSzLcVwITlQIbvAsy4ZgVQzDhGBVHMexLOuXqsRWu1+GZdg27JN9+1tRFJ22+3ne9Xq9Xq8nhDQ3N//+978fNmwYIeTGjRvx8fHSAgkJCQaDwVWjpKamZubMmdLtp59++sVHJvq3SFeU0dFueqPd9spFq9XKXYIToTlXIcW24kLznXOO41QqldxVUCM0N3i1Wt32QRrDwhyuIK7gFXwbNlrfNnhBEJy2B+SFw6lTp95///2BAwfm5OSQVuEpleK0URIbG7tz507b7ebrtwJRZGvG2lpXXeHh4QG6wLzPeJ6PiIioq6uzWq1y19KCba4iIyPlriV0SStOo9E0Nja6+s8pF61Wa7FYTCaT50WDSKlUiqJoNpvlLqQFjUbDsmx9fb3chbTAcZxCoWhsbGz7UGKrzcBsMQvNzT4MJR0UNvv0t6IoOn095ecAE0Vx06ZN1dXVS5cu7datm9QYFxdXXl4u3TYYDHFxca4aJQqFIi0tzXa38epN/xbpisVicdUliqKbXllI7ztZrdZQK8w2V66O+oH8e8UJgmC1WkPtJQgJyQ2e53lBEEKtKlEUQ3CuCCEcx/mlKkYQHN7yEwXRt5dc0puH/n255uePl8rKyioqKlatWmVLL0JIRkZGZWXllStXRFEsLi7OzMx01QgAAOAlPx+BnT179tKlS9OmTZPuajSanTt3ajSavLy8wsJCi8WSlpY2YcIEqat1IwAAgJf8HGCzZs2aNWtW63bbyR0eGwEAALyBX+IAAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKnkIsFGjRlmtVvuWq1evTp48OZAlAQAAeMY7bT158uTJkycJIYcOHSosLOQ4ztZVVVV14sSJIFUHAADggvMAq6qq+stf/iLdLi4uZhjG1sWy7Nq1a4NRGgAAgGvOAywnJycnJ4cQ0q9fv0OHDvG888UAAADk4iGZzpw5YzKZfvjhB4f25OTkQFUEAADgBQ8BtmfPnpkzZzY1NTm0i6IYsJIAAAA883AWYn5+/mOPPfbdd9/daCk4xQEAALji4Qjs5s2bS5cu7d27d3CqAQAA8JKHI7Ds7OyKiorglAIAAOA9D0dgr7766pw5c86ePZuRkREWFmZrHzVqVIALAwAAcMdDgGVnZxNCCgsLHdobGxsDVREAAIAXPAQYggoAAEKThwAzGAxO2+Pi4gJQDAAAgLc8BFjHjh2dtgfze2BB+x2Q8PBwV10cx7nplQXLsoSQsLAwQRDkrqUF21xJFYJT0orjeT4sLCzUvlXJsizP86G2wfM8L4qiQqGQu5AWeJ5nGCbU5oplWY7j7H8C0GcWpdLSsoXjOM6nfbK0Q/Btf+7q/4iHsSorK223TSZTSUnJb3/72127dvlQgc+CtoNu/X1tG57n3fTKgud5lUrV3NzscLkA2dnmSqVSyV1L6JJWHMuyzc3NofYSRKlUWq3WUNvgRVEURdFsNstdSAscx7EsG2pzxXGcQqHwS1WixSG/iCAKjK9bLMuyvm3tPgaYw09G9e3bt2PHji+99NKhQ4d8KMI3QfvvbWm1qmxEUXTTKwvp5ZXVag21wmxzFWoHFiFFWnGiKFqt1lB7CUJCcoPneV4QhFCrSorVUKuKEMJxnF+qYgTB4Y0UURB92ydLR2D+3Z/f8Zs8nTp1On36tB8rAAAA8IGHI7CzZ8/a37158+Ybb7zRtWvXQJYEAADgmYcA69u3r0NLXFxckD8DAwAAaM1DgNXV1Tm0hIeH++XkFgAAgLbwEGARERGCIJw4caKiosJisaSnp48YMQIBBgAAsvMQYNevXx83blxZWVlSUhLLstXV1Xq9/vPPP3f1/TAAAIDg8HAW4iuvvMLz/MWLFy9evFhVVXXx4kWGYebPnx+c4gAAAFzxcAR26NChDz/8MCkpSbqblJS0evXq6dOnB74wAAAAd/BjPwAAQCUPATZy5Mj8/PxLly5Jd//1r3/l5+ePHDky8IUBAAC44yHA1q9fTwjp2bNncnJycnKyTqcjhBQVFQWjNAAAANc8/xr93//+d+k0ekJIamrq8OHDcRo9AADIzkOANTc3r1y5sqamZuPGjYSQBx98MCsra+XKlRqNJijlAQAAOOfhLcTFixdv2rSpX79+0t3c3NyPPvpo0aJFgS8MAADAHQ8BtmfPnrfffjsnJ0e6O2fOnK1bt+7ZsyfgdQEAALjlIcAaGhp69Ohh3xIRERFqV28DAIB2yEOAZWdnFxQU1NTUSHdra2tXrFiRlZUV8LoAAADc8nASx8aNG0ePHt21a9c+ffrwPH/u3LkuXbocPnw4OMUBAAC44iHAEhISSktLDxw4cPbsWbPZnJ+fP2HCBIVCEZziAAAAXPEQYIQQjuPGjx8/fvz4IFQDQaZQKFjWzz8nxrKsSqUihOD7ggAQUJ4DDO5WDMNERUUFYmStVhuIYQEA7CHA2i/pCGnTpk1ff/11IMbftGlTIIYFAJAgwNq7pqamhoYGuasAALhjuJwKAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlQISYFarNTc39/bt27aW0tLSuXPn/vKXv9y4cWNzc7ObRgAAAG/4P8AOHDjw2muvXb161dZiNBrXrVu3YMGCzZs319bWHjhwwFUjAACAl/wfYN27d582bZr9VZvLysp0Op1Op+M4bty4cSdOnHDVCAAA4CX/X04lPT2dEMJxnK3lxo0b8fHx0u2EhASDweCqUXL79u2NGzdKtwcNGjSi0z1+L9KpiIgIV108z7vplYV0JeWwsDBRFH0bAVdMlpFGoxEEQaFQMAzj8xoMEJZlFQpFqG3wHMeJoqhUKuUupAWe5xmGCbW5Yv+t7UNZVCpzyxae5zi74xPvSTschU9/KwiC0/ZgXA/M4f+nVIrTRonZbK6oqJBu9+zZk+ncI/A1EkIIz7ucDYZh3PTKQtoaeJ4Ptd0feIPjOJZlpe0qBNdgCG7wLMuKouiXnbIfSfWE2lwxDOOvNSi0nnCG8e21r/RXbfnb1oIx73FxceXl5dJtg8EQFxfnqlESGxu7c+dO293G8gtBKJIQUl9b66orMjLS/pyUUKBQKKKiourq6iwWi28jsCwbExPj36rAS9KKi4iIMJlMVqtV7nJa6NChg9lsrq+vl7uQFtRqtSAIoXa2l1arZVn21q1bchfSAs/zSqXSaDS2fSjGZHJIMIvZIvi0Fnie53ne5zXoNKuC8XImIyOjsrLyypUroigWFxdnZma6agQAAPBSMI7ANBpNXl5eYWGhxWJJS0ubMGGCq0YAAAAvBSrAPvroI/u7er1er9c7LOO0EQAAwBuh9YkoAACAlxBgAABAJQQYAABQKbS+vgBw1wsLCyOEREREiKLIcZz9V/79paGhwecvVwBQBAEGEFTSd+++++67hoaGQIw/YHdlZNEAAAxESURBVMAAhUKBAIP2AAEGIIOPPvqoqqoqECPb/wgAwN0Nn4EBAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJV4uQvwTKFQBOeBtFqtqy6e5930yoJlWUKIRqMRRdG3ERiG8WtFECpUKhXP+/5fm2VZhUIRahs8x3GiKKpUKrkLaUHaO4XaXDEMw3Ecx3FtH8qsUplbtvA8x/m0T5Z2OL7tz13t5SgIMLPZ7Hkhf6ivq3PVFRkZWee6VxYKhSIqKspoNFosFt9GYFk2JibGv1VBKGhqajKZTD7/eYcOHcxmc319vR9Laju1Wi0IQnNzs9yFtKDValmWDbWdA8/zSqXSaDS2fSimqcnhbTqLxSr4tE/meZ5lWZ/3506zCm8hAgAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQiYLrgbVnLMuGhYU5vfKk7YKWgiD4NjguaAkAVEOAhTSe58PCwgwGg88p5X7wULu+LQCA9xBgFCgoKPj555/9Pmz37t0LCwv9PiwAQHDgMzAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqITT6AHuKizLqtVqpVLZlhGUSmVUVJTT3sbGxqamJp8HB/AjBBjAXYVhmOvXr9fU1ARicJ1Op1QqEWAQIhBgAHebv/71rwcOHAjEyMuWLevRo0cgRgbwAT4DAwAAKiHAAACASngL0Q8C97Pu+MF4AABXEGBtFRYWFh4eLncVAADtDgKsrViWraur27p1ayAGHzp0aL9+/QIxMgAA7RBgftDU1PTVV18FYmSdTheIYQEA7gI4iQMAAKiEAAMAACrJ+RZiaWnpli1bTCaTXq/Pzc1ty4/fuMeyrDen86nVajcjuOrlOM73ygAAwFeyBZjRaFy3bt3y5cuTkpJWrVp14MCBSZMmBeixeJ5nWc/HmoqICDe9EW57AQAgyGQLsLKyMp1OJ52kMG7cuN27dwcuwAgh165dO3funPtl3iv4woeRf/3rX0dHR/tUFAAA+I4RRVGWB96/f/9PP/00d+5cQshPP/20ZMmSbdu2SV1Xr14dP368dPvpp59+8ZGJQaqpV1KQHggAgAaN69aZ8vLsW7Q7P+QHDAxyGYIgsKlOfoRTtiMwh+AUBMF2OzIycvHixdLtXr161XeObeNjsSzr1SdVrn9jW6FQmM1mp10MwzAMY1+/f7Es63RwlmUVCkVzc3NbXoK4GrwtbHMViMFtpA81vX/uKpXKYrFYrVYvBw/OOuV53mq1+vdFZNunXalUCoJgsVhad93ptN8R99POcZwoim15agHa2gkhZrM5oFv7nQ7OMAzLst5v7cT1Om29GZgTY6xJid4XIwiCVIlCoVCpVPX19d7/rf0gkc7aZQuwuLi48vJy6bbBYIiLi7N1aTSaxx9/3HbXYDAEu7hWIiMj6+rq5K6iBYVCERUVZTQane5oZBSCc8UwjEqlamxsbGxslLuWFiIiIkwmk5c7mqDp0KGD2Wz2bUcTOGq1WhCE5uZmuQtpQavVSj9lIHchLfA8r1QqjUZj24dimpocTh8wGo2iT8+XYRilUunf/4OynUafkZFRWVl55coVURSLi4szMzPlqgQAAGgk2xGYRqPJy8srLCy0WCxpaWkTJkyQqxIAAKCRnN8D0+v1er1exgIAAIBe+CUOAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKgk228hQhtdvnx5796906dPt/8RE3DKYrG8++67I0eO7NOnj9y1UGDXrl3dunUbMWKE3IVQoLi4+Pbt21OmTJG7kECxlJRYDh+2b1HOmMF26+bDUKWlpX//+99feOEFP5VGiLzfA4O2uHbt2vbt2x955BEEmEcWi2X79u1JSUkIMG/s379/wIABCDBvHD9+/PLly3dxgPH9+/P9+/tlqHPnzu3atcu/AYa3EAEAgEoIMAAAoBI+A6NVc3PzjRs34uPjpQs6gBuiKF6+fLlDhw4ajUbuWihw7do1lUqFy7R64+eff7ZarR07dpS7EArU19ffvn27c+fOfhwTAQYAAFTCW4gAAEAlnIVIk6NHj+7Zs6e+vr5r165z5851OBhfsGBBdXW1dH3VMWPG/Od//qdMZcqstLR0y5YtJpNJr9fn5uYqlUrve9sbbFF3xP2EYNOy+eSTT3bs2GG7azabt23bFhMTY2vx26YlAiUuXbo0Y8aMK1eumM3mHTt2vPrqq/a9giBMnz69qalJrvJCRENDw1NPPfX9999bLJYVK1bs37/f+972BlvUHXE/Idi0XPnqq69Wrlxp3+LHTQtvIVLDYDA8/PDDiYmJPM9nZWVdvnzZvvfGjRuEkLVr1+bm5hYVFYXaNc6DpqysTKfT6XQ6juPGjRt34sQJ73vbG2xRd8T9hGDTcqqxsXHHjh25ubn2jX7ctBBg1NDr9Tk5OYSQ5ubm3//+98OGDbPvvXXrVmpq6uzZszdu3EgI2bx5syxFyk46M1O6nZCQYDAYvO9tb7BF3RH3E4JNy6ndu3ePHTvW4ccW/LhpIcBC2pEjR/Ly8vLy8r799lup5dSpU/PmzYuNjX322Wftl0xJSVm2bFl8fLxSqZwxY8bp06fFdnl+qcOzFgTB+972CVuUl9xPCDat1v73f/+3tLR07NixDu1+3LRwEkdIy87Ozs7Olm6Lorhp06bq6uqlS5d2a/VbZBcvXhRFsWfPnoQQnuc5jgt2raEhLi6uvLxcum0wGBxe+rnvbW+wRd0R9xOCTau1I0eODB48mGUdD5P8uGnhCIwaZWVlFRUVq1atctjX/Pjjj01NTXV1datWrbp586Yoip988smQIUOkM3zam4yMjMrKyitXroiiWFxcnJmZKbVLs+Sqt33CFnVHXE0INi2nRFE8efLkoEGD7Bv9vmnhi8zU2LFjx969e3n+/w6aNRrNzp07CSGPPfbYb37zm7S0tE8//fSzzz4TRbFPnz65ubnt9lcnSktLt2/fbrFY0tLSnnvuOemXSqRZ6tOnj9Pe9glb1B0RRdHphGDTcqqysrKwsPC9996zDye/b1oIMAAAoBLeQgQAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMIERt27bt5MmTbV8G4G6FAAMIUQgwAPcQYAAAQCUEGID89u3bd//994eFhSUlJb311luEkH79+h07dmzRokWjRo0ihFy4cGH8+PEdO3bUarXDhg0rLS1tvQxAe4PfQgSQWXV1dUpKyvz586dOnXr8+PFXX331xIkTgwYNGjly5OjRo1977TWO41JTUzt16vT6668TQpYvX97Q0PD1119bLBb7ZeR+HgDBhuuBAcjs4sWLVqs1Nzc3JSWlf//+aWlpXbt25XmeYRiO4ziOEwTh+eefnzhxok6nI4Rcvnx5/vz5hBD7ZeR+EgAyQIAByGzo0KETJky4//77x44dO3LkyKeeeioyMtJ+AZZl58yZc/z48X379n399dfFxcXt/NJcABJ8BgYgM7Va/ac//am8vFyv13/wwQfdunX705/+ZL9AfX390KFDFy5caDab586d+84778hVKkBIQYAByOzw4cOLFy/u2bPnkiVLvvzyy9GjR7///vv2Cxw9erSioqKkpGTRokWZmZlWq1WuUgFCCt5CBJAZx3GrVq2Kiop65JFHKioqTp06lZubSwhhWbaqqurq1auRkZFGo3HXrl1jxowpKSl5/fXXGxoafvrppy5dutiWSUxMlPt5AASdCAByW79+fY8ePZRKZbdu3V599dWmpiZRFLdt2xYbGztp0iRBEAoKCuLj42NiYqZOnXrhwoX09PT+/fvbLyP3MwCQAU6jBwAAKuEzMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKj0/wAfOva3mL4ppAAAAABJRU5ErkJggg==" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -222,10 +226,12 @@ <h3>One numerical variable (median)</h3>
 <span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">med =</span> <span class="op">-</span><span class="dv">1</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)</code></pre></div>
+<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -267,10 +273,12 @@ <h3>One categorical (one proportion)</h3>
 <span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
+<pre><code>## Setting `type = &quot;simulate&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -294,6 +302,7 @@ <h3>One categorical (one proportion)</h3>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
+<pre><code>## Setting `type = &quot;simulate&quot;` in `generate()`.</code></pre>
 </div>
 <div id="one-categorical-variable-standardized-proportion-z" class="section level3">
 <h3>One categorical variable (standardized proportion <span class="math inline">\(z\)</span>)</h3>
@@ -323,10 +332,12 @@ <h3>Two categorical (2 level) variables</h3>
 <span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
+<pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -368,10 +379,12 @@ <h3>Two categorical (2 level) variables (z)</h3>
 <span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
+<pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -420,8 +433,9 @@ <h3>One categorical (&gt;2 level) - GoF</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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mIQQAABBBBAIHsESICyp62oKQIIIIAAAgikSIAEKEWQFIMAAggggAAC2SNAApQ9bUVNEUAAAQQQQCBFAiRAKYKkGAQQQAABBBDIHgESoOxpK2qKAAIIIIAAAikSsOo5QCkyCSlGn8VSWVkZMjzeAf5nTfTr1y/eWXpNx1NKe3HQgwACCCCAwCkLkADFQahPUy0uLpZdu3bJiRMn4pgjtZNMmTIltQVSGgIIIIAAAh4XIAFKYAVYtWqV7Nu3L4E5UjPp5MmTU1MQpSCAAAIIIICAEeAaIFYEBBBAAAEEEPCcAAmQ55qcgBFAAAEEEECABIh1AAEEEEAAAQQ8J0AC5LkmJ2AEEEAAAQQQIAFiHUAAAQQQQAABzwmQAHmuyQkYAQQQQAABBEiAWAcQQAABBBBAwHMCJECea3ICRgABBBBAAAESINYBBBBAAAEEEPCcAAmQ55qcgBFAAAEEEECABIh1AAEEEEAAAQQ8J0AC5LkmJ2AEEEAAAQQQIAFiHUAAAQQQQAABzwmQAHmuyQkYAQQQQAABBEiAWAcQQAABBBBAwHMCJECea3ICRgABBBBAAAESINYBBBBAAAEEEPCcAAmQ55qcgBFAAAEEEECABIh1AAEEEEAAAQQ8J0AC5LkmJ2AEEEAAAQQQIAFiHUAAAQQQQAABzwmQAHmuyQkYAQQQQAABBEiAWAcQQAABBBBAwHMCJECea3ICRgABBBBAAAESINYBBBBAAAEEEPCcAAmQ55qcgBFAAAEEEEAgbQlQe3u7vPLKK/Laa69JV1dXL/ljx47Jpk2bZP/+/b2Gt7a2yrZt22T79u2i89MhgAACCCCAAAKpEEhLAlRbWyuzZs0yicyGDRvk+uuvF01utNu9e7fMnTtXDhw4ILfffrusW7fODD958qTMmTNHtmzZIqtWrTLjghMnMyH/IYAAAggggAACCQqkJQFau3atTJ8+XW699Va55557ZOTIkfL888+bqq5YsUKWL18uN910kzzyyCPy6KOPSltbm6xZs0YmTpxopv/pT38qzc3NsmPHjgTDY3IEEEAAAQQQQCBUID90UOqHLFy4UHJycgIFnzhxQvQIT0dHh9TU1MjYsWPNuMGDB0tpaakcOnRIDh48KNXV1YF5xo8fL3v37pVJkyYFhi1dulS2bt1q+gsKCkSPLtE5IzBo0CBnCu4u1b9ucIQvMnHfvn2lrKws8gQeHpObmytOrp/ZTKs2+v2K5tNe2U/qwwSZn58vxcXFYcbYM0ht2O5Eb8/CwsLoE7h4bEf3Ohytiz422pwJjOsJ+MILL5ik58orr5SjR49Knz59AjtALbKiokLq6upET5uVl5cHlqKfNVnq2Y0ePVo08dFOv+j+02o9p0nFZy1bNwZe7pyyVVO11Y1QZ2enl4kjxq4++mNB/9GFChQVFTn23Q9dWnYN0W1vXl5eVJ/O9rawQfm6fNZ/J3XbrtsekqDQVUCTQ/Xx+XxZ66N1j9alda++fv16cz3PAw88YH7NNjU1hXzBdCOvvzr0S9tzh6jDS0pKesUyc+bMXv2HDx/u1Z+qHk2ygpedqrKzpZyGhgbHqqpH/bStnUyyHKu8wwXrRkh/JOgRU/1HFyrQr18/cXL9DF1i9gzRH46aIEb16d4Oh+t8nT7rbz7RHxe6k4y1owzn44Vh/h9f2erT5Yt+9CptCdDvfvc7c6fXypUrRU91aTdgwADRJEh3fPol1U6P/gwbNkwGDhxoPpuB/x3+sY99zN/LXwQQQAABBBBAIGmBtFwE/dxzz8nmzZvlZz/7WSD50RprdqkXOuuRIe1eeukl0V9z+m/KlCmyceNGaWlpEb1NXm+FHzdunJmO/xBAAAEEEEAAgVMRSMsRIL2z68iRI+ZOMH9l9fTVzTffLIsWLQrc/q7nG5csWWImmTZtmnkG0OzZs815SL2Nvqqqyj87fxFAAAEEEEAAgaQF0pIAPf300xErOHz4cHPLe319vVRWVgam06NDy5Ytk8bGRnP9jfbTIYAAAggggAACqRBwTVbRM/npGZje/kuHAAIIIIAAAgikUsA1CVAqg6Ks1AnoHVra6Z1ITnV6l53ehhrpKJ8+BJPbVJ3Sp1wEEEDAmwIkQN5s97ijHjFihLkGKx0PRAuXAOlzTPQWeb0Yng4BBBBAAIFUCZAApUrS4nL0GUwzZsxIe4T65ONo14+lvUIsEAEEEEDAGoG03AZvjRaBIIAAAggggIAVAiRAVjQjQSCAAAIIIIBAIgIkQIloMS0CCCCAAAIIWCFAAmRFMxIEAggggAACCCQiQAKUiBbTIoAAAggggIAVAiRAVjQjQSCAAAIIIIBAIgIkQIloMS0CCCCAAAIIWCFAAmRFMxIEAggggAACCCQiQAKUiBbTIoAAAggggIAVAiRAVjQjQSCAAAIIIIBAIgIkQIloMS0CCCCAAAIIWCEQdwJ0+eWXy0cffRQS9GOPPSbXXXddyHAGIIAAAggggAACbhWI+jLUHTt2yPPPP2/q/vLLL8v3vvc96flWcH1L9x/+8AcZNWqUW+OjXggggAACCCCAQIhA1ARo5MiRcuutt0pra6u0t7fLs88+K3l5eYFCCgoK5KyzzpK77747MIwPCCCAAAIIIICA2wWiJkADBgyQbdu2mRhmzJghq1evlrKyMrfHRP0QQAABBBBAAIGoAlEToJ5zrl+/3hwJevfdd83fnuM0KRo6dGjPQXxGAAEEEEAAAQRcKxB3AvTMM8/I/Pnzpb6+PiSYL3zhC/LUU0+FDGcAAggggAACCCDgRoG4E6BFixbJ5z//ebnxxhulf//+vWLhtFgvDnoQQAABBBBAwOUCcSVADQ0NcvToUbn//vvltNNOc3lIVA8BBBBAAAEEEIguENdzgMrLy6Wqqkp27doVvTTGIoAAAggggAACWSAQ1xEgjWP58uVyyy23SE1NjUmG8vP/N6seFTrvvPOyIFyqiAACCCCAAAIIiPwvi4mhodcAnThxQhYuXBgyJRdBh5AwAAEEEEAAAQRcLBB3AnTkyBHp6uoKG0rPhyOGnYCBCCCAAAIIIICAiwTiToDq6uoiJkAlJSXSr18/F4VFVRBAAAEEEEAAgcgCcSdA55xzjjkFFq4oToGFU2EYAggggAACCLhVIO4ESF+Gqi8/9XeNjY3yj3/8Qx5++GFze7x/OH8RQAABBBBAAAG3C8SdAI0ePToklk996lPS3Nws9913n/zyl78MGc8ABBBAAAEEEEDAjQJxPQcoWsX1HWBvvPFGtEkYhwACCCCAAAIIuEog7iNAr776qnR0dAQqr59ra2vlO9/5jkydOjUwnA8IIIAAAggggIDbBeJOgKqrq8NeBK2nwZYtW+b2OKkfAggggAACCCAQEIg7AXr33XdDboPv06eP9HwidKBUPiCAAAIIIIAAAi4WiDsB0veB1dfXy+rVq+XAgQMyZMgQGTdunFx66aVSWFjoihCdeit9bu4pXyrlCp9srURxcbHnE+2ioiLhgaPh12D9EebUdz/8ErNnaEFBgej2K5pPZ/dz3E6GCUnns/0Hrn6ncnJyjFEYAk8PUhftdB3w+XxZaeGLse+OOwF6/fXXRU+D6ROhx44dKx988IEcPnxYrrjiClm3bp3oTirTXWtrqyNVsH0j4AhaCgttb2+Xtra2FJaYXUX17dvXXH/n1PqdXRqhtdXkEJtQFx2i2y7dyUfz8XV/v8J1+uT/bN3xhYsn3DDdyWuMkd5yEG4eLw3TBFp9snU9iFXvuBOgBQsWyCc/+UlZuXKlnHHGGWaF+ec//ylXX321/PjHP5Y77rgj4+uF7ijp7BPQ5095tW39v8K8bBBrjdaNnFfXj3hsdOce1afHzS09y/NCAqRHubwQZ892TfRzNidA4V/e9T+BuM7t6LN+du3aZe740uRHO90w6ymwW2+9VV544YX/lcgnBBBAAAEEEEDA5QJxJUD+LFkToeCuqamp1+3xwePpRwABBBBAAAEE3CYQVwKk1/dMnTrVnObauXOnOWTY0tIiGzdulJ/85Cdy+eWXuy0u6oMAAggggAACCEQUiCsB0rl//vOfmwugL7roIhk8eLD0799frrrqKrnwwgvltttui7gARiCAAAIIIIAAAm4TiPsi6FGjRsnf//53+ctf/iL79u0zd31dcMEFcvHFF7stJuqDAAIIIIAAAghEFYg7AdJS9HZTPeozffp0U2i4a4KiLo2RCCCAAAIIIICACwTiPgWmz/yZO3euPP7446baevFzVVWVPPjgg1wE7YKGpAoIIIAAAgggEL9A3AnQLbfcIm+//bZMnjzZlF7S/fTQFStWmIug169fH/8SmRIBBBBAAAEEEMiwQFynwPQpvJs2bZI9e/bIsGHDTJX11vjZs2dLY2OjeT2GPhCRDgEEEEAAAQQQyAaBuI4A6UMP9Umihw4dCompoaFB6urqQoYzAAEEEEAAAQQQcKtAXAmQvg/k05/+tNx5553y3nvvBWLRp0PrNUD6PjA6BBBAAAEEEEAgWwTiSoA0mMcee0z04YdnnnmmVFRUiF4DNGHCBPOARH0dBh0CCCCAAAIIIJAtAnFdA6TB6IMPt27dKvv37xd9CapeAzRmzBg599xzsyVW6okAAggggAACCBiBuBMgnVqTnnPOOcf8ww8BBBBAAAEEEMhWgbhPgWVrgNQbAQQQQAABBBAIFiABChahHwEEEEAAAQSsFyABsr6JCRABBBBAAAEEggVIgIJF6EcAAQQQQAAB6wVIgKxvYgJEAAEEEEAAgWABEqBgEfoRQAABBBBAwHoBEiDrm5gAEUAAAQQQQCBYgAQoWIR+BBBAAAEEELBegATI+iYmQAQQQAABBBAIFiABChahHwEEEEAAAQSsFyABsr6JCRABBBBAAAEEggVIgIJF6EcAAQQQQAAB6wVIgKxvYgJEAAEEEEAAgWABEqBgEfoRQAABBBBAwHoBEiDrm5gAEUAAAQQQQCBYgAQoWIR+BBBAAAEEELBegATI+iYmQAQQQAABBBAIFiABChahHwEEEEAAAQSsFyABsr6JCRABBBBAAAEEggVIgIJF6EcAAQQQQAAB6wVIgKxvYgJEAAEEEEAAgWABEqBgEfoRQAABBBBAwHqBtCdAzz//vHR0dPSCPXbsmGzatEn279/fa3hra6ts27ZNtm/fLu3t7b3G0YMAAggggAACCCQrkNYE6JlnnpF7771XOjs7A/XdvXu3zJ07Vw4cOCC33367rFu3zow7efKkzJkzR7Zs2SKrVq0y47q6ugLz8QEBBBBAAAEEEEhWID/ZGROZT4/4LFmyRI4fPx4y24oVK2T58uVy/vnny7XXXivz58+X6dOny5o1a2TixImyePFiM8/ChQtlx44dMmnSpJAyGIAAAggggAACCCQikJYESI/4aOJy1VVXyaWXXhqonyZGNTU1MnbsWDNs8ODBUlpaKocOHZKDBw9KdXV1YNrx48fL3r17eyVAS5cula1bt5ppCgoKZMOGDYHp+WCPQHl5ufTt29eegJKIROMvKytLYk77Z8nNzZVBgwbZH2gSEapNTk5OVJ/2yn5SH6bs/Px8KS4uDjPGnkFqw5mF6O1ZWFgYfQIXj+3oXoejddHHRpszgXFFRUUyY8aMkDmOHj0qffr0MV9Q/8iKigqpq6uT2tpa0R2fv9PPmiz17EaPHi36JdUuLy9P9JohJzrdiPiX40T5lBldQK//6nnaNPrU9o3VdU9/LARfO2dfpMlFpNsXp777ydXIPXPpzivWtrGzvS1shX1dPuu/d7pt1wSIJCh0FdDkUH18Pl/W+mjdo3VpSYAiVUC/mME7Nt3I66+O4HE6vKSkpFdRM2fO7NV/+PDhXv2p6tGjS8HLTlXZlBNbQK8Ha2lpiT2hhVPoRkh/JKiB/qMLFejXr580NDSEjmCI+RGpCWJUn6amsFK+Tp/1N5/ojwvdScbaUYYF8sBA/4+vbPXp8kU/epXWi6CD15cBAwZIU/eXr+evNz36M2zYMBk4cKA5EuSfxz/c389fBBBAAAEEEEAgWYGMJkCaXeqFzuvXrzf1f+mll0R/zem/KVOmyMaNG80vf71NXm+FHzduXLJxMh8CCCCAAAIIIBAQyOgpMK3FokWLAre/6/lGvVtMu2nTpplnAM2ePduch5w1a5ZUVVWZcfyHAAIIIIAAAgicikDaEyD/XVv+Sg8fPtzc8l5fXy+VlZX+weai42XLlkljY6O5/kaPFtF5S0CvvdJzz3phvP7LRKfX3US9fiITlWKZCCCAAAKnLOCarKJn8tMzKq/f/tzTwmuf9eJNPSq4efNm81iEdMf/mc98RoYMGZLuxbI8BBBAAIE0CLgmAUpDrCwiSwV27twpf/3rX9Nee30+FQlQ2tlZIAIIIJAWgYxeBJ2WCFkIAggggAACCCAQJEACFARCLwIIIIAAAgjYL0ACZH8bEyECCCCAAAIIBAmQAAWB0IsAAggggAAC9guQANnfxkSIAAIIIIAAAkECJEBBIPQigAACCCCAgP0CJED2tzERIoAAAggggECQAAlQEAi9CCCAAAIIIGC/AAmQ/W1MhAgggAACCCAQJEACFARCLwIIIIAAAgjYL8CrMOxvYyJMUqC8vNy8lDdT76PTF8E2NzcnWXtmQwABBBCIJkACFE2HcZ4WGDp0qHR1dUlbW1vaHQoKCkQTsJaWlrQvmwUigAACXhAgAfJCKxNj0gJvvPGG3H333UnPn+yMkydPliVLliQ7O/MhgAACCMQQ4BqgGECMRgABBBBAAAH7BEiA7GtTIkIAAQQQQACBGAIkQDGAGI0AAggggAAC9gmQANnXpkSEAAIIIIAAAjEESIBiADEaAQQQQAABBOwTIAGyr02JCAEEEEAAAQRiCJAAxQBiNAIIIIAAAgjYJ0ACZF+bEhECCCCAAAIIxBAgAYoBxGgEEEAAAQQQsE+ABMi+NiUiBBBAAAEEEIghQAIUA4jRCCCAAAIIIGCfAAmQfW1KRAgggAACCCAQQ4AEKAYQoxFAAAEEEEDAPgESIPvalIgQQAABBBBAIIYACVAMIEYjgAACCCCAgH0CJED2tSkRIYAAAggggEAMARKgGECMRgABBBBAAAH7BEiA7GtTIkIAAQQQQACBGAIkQDGAGI0AAggggAAC9gmQANnXpkSEAAIIIIAAAjEE8mOMz6rRZWVljtQ3N5c80RFYCo0pUFpaaqYpKiqSvLy8mNN7cYL8/Hxx6ruf7Z4FBQWi269oPp0lJXIyTKA6n9ra3Ol3KicnxxjZHGcysamLdroO+Hy+ZIrI+Dy+GPtuq9bu1tZWR8Bt3wg4gkahKRFoa2sz5XR0dIhT63dKKprBQjQ5xCZ8A+i2S3fy0Xx87e1hZ+7q6sraHV/YgMIM1J287tw1VrpQAU2g1SdrE6AYiZtVCVB7hC9yaLMyBIHsENDER7vOzk5h/Q7fZrpxxiayje7co/r8dx0LLsELCZAe5fJCnMFtm0h/NidAsdJazu0ksiYwLQIIIIAAAghYIUACZEUzEgQCCCCAAAIIJCJAApSIFtMigAACCCCAgBUCJEBWNCNBIIAAAggggEAiAiRAiWgxLQIIIIAAAghYIUACZEUzEgQCCCCAAAIIJCJAApSIFtMigAACCCCAgBUCJEBWNCNBIIAAAggggEAiAiRAiWgxLQIIIIAAAghYIUACZEUzEgQCCCCAAAIIJCJg1aswEgmcaRFws4D//XMl3S+q1K6wsNC8tDGddW5pacnadwCl04llIYBAdgqQAGVnu1FrywVGjBhhIuzbt6/5638rfDrD1hdFNjU1pXORLAsBBBBImwAJUNqoWRAC8Qto8qHdl770JTl27Fj8M6ZoynXr1qX9iFOKqk4xCCCAQFwCXAMUFxMTIZAZAX0TMx0CCCCAQOoFSIBSb0qJCCCAAAIIIOByAU6BubyBqB4CmRDIzc0VvRDbfxF2uuvQ0dEh7e3t6V4sy0MAAQ8JkAB5qLEJFYF4BTT5ycvLy1gC1NraKnV1dfFWl+kQQACBhAVIgBImYwYEvCHwxBNPyFNPPZX2YOfPny/V1dVpXy4LRAABbwmQAHmrvYkWgbgF9BSUHolJd6env+gQQAABpwW4CNppYcpHAAEEEEAAAdcJkAC5rkmoEAIIIIAAAgg4LUAC5LQw5SOAAAIIIICA6wRIgFzXJFQIAQQQQAABBJwWIAFyWpjyEUAAAQQQQMB1AiRArmsSKoQAAggggAACTguQADktTPkIIIAAAggg4DoBEiDXNQkVQgABBBBAAAGnBUiAnBamfAQQQAABBBBwnQAJkOuahAohgAACCCCAgNMCvArDaWHKRwCBrBLIyckR/ZepzufzZWrRLBcBTwmQAHmquQkWAfcL6JvoNQEpLCyMq7K5ublxTxurQC2rsrIyownQRx99JI2NjbGqyngEEDhFARKgUwRkdgQQSK3A2WefbRKaAQMGxF1wItPGU+jjjz8u7777bjyTpnSahQsXSllZWUrLpDAEEAgvQAIU3oWhCCCQIQE9CnP8+HG566670l6DCy+8UBYsWCCvv/667NmzJ+3L//KXv0wClHZ1FuhVARIgr7Y8cSPgYoGOjo6MHIE566yzXKxC1RBAIJUCJECp1KQsBBBAIAUCqb4IO1p5XZK5C75TQEURCCQtQAKUNB0zIoAAAqkVGDRokJSWlpp/qSx5yJAhEYtrG1gjxyOOZQQC9gq4OgFqbW2VXbt2mTsyJkyYIAUFBfa2BJEhgIDnBfT6pzfeeEP+/Oc/p81iwHs1cmWYpeXk5kheXl6YMc4N6uzsdK5wSkYgSMC1CdDJkydl3rx5ct5558n7778va9eulQceeCCjt6cG2dGLAAIIpFzg0KFDsnnz5pSXG6nAqhMNYROg/Lx8KSoqijSbI8P1Ry9JkCO0FBpGwLUJ0Jo1a2TixImyePFiU229PXTHjh0yadKkMGEwCAEEEEAglQJvvfWWHN20KZVFRixLn/102WWXmaP86TzqpEfcurq6zD9NvLyYfOmZlXDXiPmH6Xg1cqrTB3/qTQ+Z6HK6A3MuslOI6J577pHq6mq55JJLTCm/+MUvzJdDjwr5u3vvvVe2bt1qevULtGHDBv+olP/V8uvq6qS9vT3lZccqUJ9xosttaGiINWnKx1dUVEhxcbEcOXIk5WXHKlA3TnpNxIkTJ6SlpSXW5CkfP3DgQPPFrK+vT3nZsQrUZ8H06dNHjh07lpGNssauR2GbmppiVTXl4/v37y/a9hp7ujtd13Wd99J3vWv3a9J15WdDqIsW/J8UzZkbMtyJAbqzLSkpcaLouMt06a4w7vonO6HGrd+3THVOPvm8o7RIis6uihiaa48A1dbWSnl5eaDi+rmmpibQrx/09Jj/14L+1cOnTnXaSH379k2qeF259N+pZLmaheuOIROd1tvJZft/aYTbAOkvMicuCo3HUeuj65WTsUerh8auO2Nte22DcD7R5j/VcXr6I92nQLTOuj5orPG6axul8pe7lpXsd/1UzXX+VH7X49n2tFeUy4kwFc87/TQpvPC8MGOcGeTr3kY6uTMMV2v16fm96vk53PRODdN6pDt2fyxm3xTh9Svp2PZo3Keyb/THEe6vfpejncR1bQIUvFFToOBfCDNnzuwV8+HDh3v1u6VHf83rTjwTv2jdYhCtHmqjK6qTCWy05bt5nCYDegePvh5Bj8jQhQr069dPPvzww9ARDDE/IjWJjbrtiXCEs7m5SZozcBQunc2m22bd7mTiyH4640xmWZoYDR482Jx5yNZtc6wfcJk77hWjRfQQvB6G9nf6ediwYf5e/iKAAAIIIIAAAkkLuDYBmjJlimzcuNFc+6G/XrZv3y7jxo1LOlBmRAABBBBAAAEE/AKuPQU2bdo02bZtm8yePdtcPzNr1iypqop8MZM/IP4igAACCCCAAAKxBFybAOldV8uWLZPGxkZz7Y/20yGAAAIIIIAAAqkQcH1Wkcm7MVIBTBkIIIAAAggg4D4B114D5D4qaoQAAggggAACtgiQANnSksSBAAIIIIAAAnELkADFTcWECCCAAAIIIGCLAAmQLS1JHAgggAACCCAQtwAJUNxUTIgAAggggAACtgi4/i6wRKDdequ8PsX60KFDoi81pQsvoK98cGv7ha9xeobqu4n27t0beKVBepaaXUvR16iw7oRvs6NHj5rXPOjrQiJ1Xd3vGPR95vKQ0TmjRkmu5Y8f0e+XvvKB9Sek+c3ribJ926Ov1IrWufZt8NEqnW3jVqxYIU899ZR5mnW21Z36ZlagubnZPAH9vvvuk+B332W2Ziw9GwT0WWovv/yybNq0KRuqSx1dJHDkyBG55JJL5KGHHpLLLw9NkF1U1aSrwimwpOmYEQEEEEAAAQSyVYAEKFtbjnojgAACCCCAQNICVl0DlLSCwzNOnjxZKisrHV4KxdsoUFBQIDfffLOcd955NoZHTA4LXHrppTJixAiHl0LxNgqUlZWZbY/N6w/XANm45hITAggggAACCEQV4BRYVB5GIoAAAggggICNApwCc7hVW1tbZdeuXaK3eU+YMEH0lAYdAvEIfPTRR/Lmm2/2mnTSpEm9+ulBIFhA15vXXntNLr744l6j9u/fL++8846MHz9eBg4c2GscPQj4Bfbt2yfFxcVy1llnmUE2b4dIgPyt7sDfkydPyrx588z1G++//76sXbtWHnjgAZMMObA4irRMYOfOneYW1JEjRwYiIwEKUPAhjEBLS4t8+9vfNs+26ZkAPfjgg7Jnzx4Z1f1sn4cfflhWrlwpZ555ZpgSGORlgf/85z9y2223yde+9rVAAmTzdogEyMG1fc2aNTJx4kRZvHixWcrChQtlx44dwk7MQXSLin7rrbdkxowZcsMNN1gUFaE4JfDvf/9b7r77bqmoqDD//Mt5++23ZevWrfL000+bxOjJJ5+UJ554Qu666y7/JPxFQP74xz/Kb37zm17rjrLYvB3iGiAHV/yDBw+aw83+ReihZ32yJh0C8QjohkfvxFi9erXorzB9ai0dApEE9KGZ3/zmN2X27Nm9JtHEaOzYsSb50RFsh3rx0PNfgZKSEvn1r39tjhLqJRv+zubtEAmQv5Ud+FtbW2teYeAvury8XI4fP+7v5S8CUQV0w/PKK69IUVGR/Pa3v5VvfOMbUadnpLcFRo8eLWPGjAlBOHz4cK9f9WyHQogY0C1QXV0deF1Tzx9bNm+HOAXm4Kqv7yHR9xT5u46ODtEsmw6BeAQeffRR8/wofVfRZz/7Wfnc5z4nNTU1csYZZ8QzO9MgYATYDrEinIqAzdshjgCdypoRY16900JfhOrv9POwYcP8vfxFIKJAW1ubuWNHkx/tCgsLZfDgwaJHFekQSERg0KBBIduhoUOHJlIE03pUwPbtEAmQgyv2lClTZOPGjaJ3Zhw7dsy8DHXcuHEOLpGibRHQxyXoHYN6Cky7f/3rX+b06QUXXGBLiMSRJgF9/IY+TuG9994TPQq9YcMGueiii9K0dBaTzQK2b4c4Bebg2jlt2jTZtm2buShRf8nPmjVLqqqqHFwiRdsioBch6t2DjzzyiPn3wQcfyJ133in5+XxlbWnjdMWh1/wsWLBA5s+fL/3795fhw4fLddddl67Fs5wsFrB9O8SrMNKwcjY2Npprf9h5pQHbwkWcOHHCXEzf884MC8MkJIcF2tvbRR/MqncW0iGQqICN2yESoETXAqZHAAEEEEAAgawX4BqgrG9CAkAAAQQQQACBRAVIgBIVY3oEEEAAAQQQyHoBEqCsb0ICQAABBBBAAIFEBUiAEhVjegQQQAABBBDIegESoKxvQgJAAAEEEEAAgUQFSIASFWN6BBBwpUBDQ0NC9Up0+oQKZ2IEEHC9AAmQ65uICiKAQCyB5557Tq644opYkwXGJzp9YEY+IICANQIkQNY0JYEg4F0BfdVDU1NT3ACJTh93wUyIAAJZI0AClDVNRUUR8LbA7t27Ze7cuXLZZZfJV7/6VXn11VcNyIsvvijPPPOM1NTUmOEffvihGa7vvJo3b56Z/itf+Yp5L5+OiDS9mYn/EEDAMwIkQJ5pagJFIHsFjhw5IlOnTpXi4mKT5OhrQS6++GLZt2+fnHbaaXLGGWdIaWmpeclnYWGhPPzww+Z9Vx//+MflhhtukObmZrnqqqtk586dYafPXhlqjgACyQrwZsVk5ZgPAQTSJqCnrDSJWbp0qUlgvvjFL8o555wjXV1d5u/EiRPl4MGDJjnSSh09elR+9KMfmZeAar++/HPQoEHyyiuvyE033STB0+s0dAgg4C0BEiBvtTfRIpCVApqwjBgxQkaNGiXV1dVy5ZVXip7WGjBgQNh4NFE6fPiw/P73vzdHiV577TU5efKktLS0hJ2egQgg4D0BToF5r82JGIGsE9A3mOs1P9/97nfNkaBFixaJnt7asmVL2FhWrFghVVVVZno9GnTNNdeYI0dhJ2YgAgh4UoAEyJPNTtAIZJfAgQMHRC9q1sTn2WefNae49BTYQw89ZALRa4L8nR7pueOOO+QHP/iBueZHk6Grr77azOPz+UKm98/HXwQQ8JYACZC32ptoEchagTlz5sjTTz8tnZ2dog8x1Lu99LSYdv379zenvDRR0k5PjdXW1oomPHrt0Ne//nVpbW0NnALrOX1HR4eZh/8QQMBbAiRA3mpvokUgKwU+8YlPyA9/+EO55557pKKiwpzeGjNmjOnXgC655BLJy8uTs88+W/R6n+9///vy5JNPmtNeQ4YMET2FNmvWLNFb6YOn37VrlxnGfwgg4C2BnO67KLq8FTLRIoBANgvokZ3KykpzS3xwHPX19Wacf7g+G0gToPz88Pd7BE/vn4+/CCBgvwAJkP1tTIQIIIAAAgggECTAKbAgEHoRQAABBBBAwH4BEiD725gIEUAAAQQQQCBIgAQoCIReBBBAAAEEELBfgATI/jYmQgQQQAABBBAIEiABCgKhFwEEEEAAAQTsFyABsr+NiRABBBBAAAEEggRIgIJA6EUAAQQQQAAB+wVIgOxvYyJEAAEEEEAAgSCB/wcxcVATA7zqpgAAAABJRU5ErkJggg==" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<p><img 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KQkx818AbRirq++su/Y4dliGj5c69kzVPVAFhIEWFlZWUtOTtO0du3aXbhwwf2nIa2D1Wp1uVzN+Z/7MGS1Wm02m3GX21DXEkgxMTGVlZWtbKZsNpvJZKqoqGg4SNm1S334Yc8Wy4pcbeKdLVVas1gslla2WQkhIiIinE7n1f09XpBYr+vVsJFDiAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAAClJcCeOMKeqqtlsDl7/Lperld0TBAACggBrLqvVGhUVFdRJnD9/3uVyBXUSACAdAqy5FEU5f/58Tk5OMDq/8cYbH3300WD0DACyI8ACwOVyVVVVBaPn2traYHQLAK1AgANM1/XNmzfv2rXL4XAkJyc//vjj7dq1E0IcPHhw48aNNTU1GRkZ2dnZFoulsUYAAPwR4KsQP/vss8LCwrVr177yyiuxsbG/+93vhBDV1dV5eXnz58/fsGFDRUXF9u3bG2sEAMBPAQ6wjh07zp07NzIyUtd1q9UaExMjhCgqKkpJSUlJSdE0bdy4cbt3726sEQAAPwX4EGJiYqIQYteuXWvWrOnQoUNeXp4Q4uzZs/Hx8cYICQkJxh9Uem00VFVVvf3228bjvn37pqWlBbbIpqmqKoSIiIgwmfxaOH6O1hxWq7X5/21oNpt1Xdc0LSAlhQlj4Vut1lAXEmCaphnfAkNdSCCZTCZVVSMjIxsOclks9f45UdXUFtiyAkJRFFlKvSKqGkaroLFtISj1DRs2bMCAAe+8886aNWueeuqpetM2rgj32mioqqratGmT8fjee+/NyMgIRpFNi4iI8HNMRVGCWokQwus2f3Va5YnGYP+MoeUpitLKvmqIf20pXleWPSKiXoBpqhY+n54+SVSq/xRFMb7NhwOn0+m1PcDLvaCgQFXV4cOHR0VFDR06dPny5UKIuLi4Q4cOGSOUlZXFxcU11miIj48vKChwP/XcOWsBmqa1a9fu4sWLfv58ODo6OtgllZeXN/93YFar1eVytbL/PrdarTab7fz5861sZyUmJqaysrKVzZTNZjOZTBUVFQ0HKZcu1fuktNvtrsuXW6awZrJYLK1ssxJCREREOJ1Oh8Phe9SW4vULXYAD1mq1bt269fLly7qu79mzp1evXkKIzMzMkpKSU6dO6bq+Y8eOwYMHN9YIAICfArwHdsstt5w8eXLBggV1dXXJyckzZ84UQkRFRc2bNy83N9fhcPTu3XvChAmNNQIA4KcAB5iiKPfdd999991Xrz0jI6PhqSyvjQAA+CNcztEBAHBFCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJRMoS7AN0VRWn5yiqK08HSbEJBilH8JSElhhZmSgnvLamwQwooUK0WCALPZbC05OWO1RUZGRkRE+DO+yWSqqqoKaknR0dG6rjezE03TdF23WCwBKSlMaJomhIiOjg51IQFmNptb30yZTCZVVb1uzk6rtfaHLZqmmc3mlimsmRRFkaVU/ymKoqpq+MxXYx+AEgTYpUuXWnJymqZZLJbq6mq73e7P+C3wQVNZWelyuZrZidVqdblcdXV1ASkpTFitVpvNVllZ2fyADysxMTGtb6ZsNpvJZPK6OSs1NfVOZjidTuHfBhhyFovFz88Kiaiq6nQ6HQ5HqAv5ntes4hwYAEBKBBgAQEoEGABASgQYAEBKBBgAQEoEGABAShJcRt98qqoaPxjyhzGmyeTvklFVvgQAQAj8KAIsJibmSn+Rd0W/7mrhX6oBAMSPJMCEEEVFRZs2bQpGz3Pnzm19N00AgPD3Ywmwy5cvnzlzJhg9t74f4QOAFDh/AwCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQkingPX700Udbt26trKzs0qXLrFmzOnXqJIQ4ePDgxo0ba2pqMjIysrOzLRZLY40AAPgjwHtg//znPzds2LBkyZKNGzempaXl5+cLIaqrq/Py8ubPn79hw4aKiort27c31ggAgJ8CHGBlZWW33357YmKiyWQaPnz4t99+K4QoKipKSUlJSUnRNG3cuHG7d+9urBEAAD8F+BBiRkZGRkaGEKKuru63v/3tkCFDhBBnz56Nj483RkhISCgrK2us0XD58uU9e/YYj6+99tqEhIRmVqWqcp/qs1gsuq43sxOTyaTruqIoASkpTJhMJiFE6zv4rKpq65spTdMURYmIiGg4SDebnT9sUVVF07SWKaz5JCrVf6qqhs98NfYBGPhzYEKIffv2vfrqqwMHDpw+fXrDabtcrsYaDRUVFQsXLjQeP/DAA3PmzAlGkRKx2WyhLiGsxcTEhLqEwDObzaEuISi8rix7ZGTlD1s0zSTREpD9K7JXiqKEz3x5BoSnAAeYruvr168vLS1dsmRJ165djca4uLhDhw4Zj8vKyuLi4hprNMTHxxcUFBiPIyIizp0718yq2rRp08weQqu8vLyx9ec/q9Xqcrnq6uoCUlKYiIiIsNls58+fb/4ealix2WxVVVWtb6Y0Tbtw4ULDQcqlS/WODNjtdtflyy1TWDNZLJZWtlkJISIiIlwul91uD3Uh37N6awxwgBUVFRUXF+fl5Xnue2ZmZv7qV786depUYmLijh07Bg8e3FijQVVVz8i5dOlSYIuUjq7rzf8s0/8lICWFlVY5X61vpozZ8T5Tut6qDm23ClK8/QIcYIcPHz558mRWVpbxNCoqavPmzVFRUfPmzcvNzXU4HL17954wYYIxqGEjAAB+CnCATZs2bdq0aQ3b3Rd3+GwEAMAf4XKODgCAK0KAAQCkFJTL6BEoxjXHAblMXFVVXdcjIyM9G3Vdr6qqcjqdjb0KAMIWARbWjB9xHz9+3OFwBLxzk8mUlpZWW1tLgAGQEQEmgbVr154/fz7g3bZt23bdunUB7xYAWgbnwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABS8hFgo0aNqnebhtOnT0+ePDmYJQEA4Jv3O3Hs2bNnz549QoidO3fm5uZ6/jvlsWPHdu/e3ULVAQDQCO8BduzYsb/85S/G4x07dijK93+Xqqrq6tWrW6I0AAAa5z3Apk+fPn36dCFE//79d+7caTJxy0QAQHjxkUwHDhyoqan56quv6rX36NEjWBUBAOAHHwG2devWqVOn1tbW1mvXdT1oJQEA4JuPqxBzcnLuvPPOf/zjH2d/qGWKAwCgMT72wMrLy5csWdKrV6+WqQYAAD/52AMbMWJEcXFxy5QCAID/fOyBPfnkkzNnzjx8+HBmZmZkZKS7fdSoUUEuDACApvgIsBEjRgghcnNz67Vfvnw5WBUBAOAHHwFGUAEAwpOPACsrK/PaHhcXF4RiAADwl48A69Chg9d2fgcGAAgtHwFWUlLiflxTU1NYWPiLX/zi9ddfD3JVAAD44CPA6t0yql+/fh06dHjsscd27twZzKoAAPDhiv/QsmPHjvv37w9GKQAA+M/HHtjhw4c9n5aXlz/zzDNdunQJZkkAAPjmI8D69etXryUuLo5zYACAkPMRYJcuXarXEh0d7fn/lgAAhISPALPZbC6Xa/fu3cXFxQ6HIz09fdiwYS0cYDExMc3sQdO0gFTSKlmtVovFEuoqrpKxZm02W6gLCTCz2dz6ZspkMqmq6nVzdkZG1vvHJk3TzGZzyxTWTIqiyFKq/xRF0TQtfPZVGvvhlo8AO3PmzLhx44qKipKSklRVLS0tzcjIeP/99xv7fVgwNNwLvFKxsbEBqaRVunz5csP/e5OF1Wq12WyVlZWt7IeJMTExrW+mbDabyWTyujkrNTX1LidzOp3Cbm+ZwprJYrHYJSnVf6qqOp1Oh8MR6kK+5zWrfFyF+MQTT5hMphMnTpw4ceLYsWMnTpxQFGXu3LnBqA8AAP/52APbuXPn7373u6SkJONpUlLSc889d9999wW/MAAAmnLFvwMDACAc+AiwkSNH5uTknDx50nj6z3/+MycnZ+TIkcEvDACApvgIsDVr1gghunfv3qNHjx49eqSkpAgh8vPzW6I0AAAa5/tu9P/7v/9rXEYvhEhLSxs6dGj4XFsJAPjR8hFgdXV1zz777Llz59auXSuEuPHGG4cPH/7ss89GRUW1SHkAAHjn4xDiokWL1q9f379/f+Npdnb2W2+99dRTTwW/MAAAmuIjwLZu3friiy9Onz7deDpz5sxXXnll69atQa8LAIAm+Qiwqqqq5ORkzxabzSbvjRsAAK2GjwAbMWLEsmXLzp07ZzytqKhYsWLF8OHDg14XAABN8nERx9q1a0ePHt2lS5c+ffqYTKYjR4507ty5oKCgZYoDAKAxPgIsISHh4MGD27dvP3z4sN1uz8nJmTBhQuu79TIAQDo+AkwIoWna+PHjx48f3wLVAADgJ+6FCACQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQUlACzOl0ZmdnX7x40d1y8ODBWbNm/fSnP127dm1dXV0TjQAA+CPwAbZ9+/aFCxeePn3a3VJdXZ2Xlzd//vwNGzZUVFRs3769sUYAAPxkCniP3bp1y8rKys3NdbcUFRWlpKSkpKQIIcaNG7dly5ZJkyZ5bTTGt9vtx44dMx5fc801FoulmSUpitLMHloxVVVNpsC/DVqGqqpCCJPJpOt6qGsJJEVRWt9MqapqzJeXYZpWb1YVRTFWrhQkKtV/YbUKGtsWAv/JlZ6eLoTQNM3dcvbs2fj4eONxQkJCWVlZY42Gc+fOTZ061Xj8wAMPzJkzJ+BFwi06OjrUJTRXbGxsqEsIvOZ/bwtPbdu2bdhoj46u/GGLyWSSaAlIVKr/NE3z/BgPLZfL5bW9Jb561wtPoxSvjYb27duvW7fOeNyxY8cLFy40s4BW8BkdPNXV1Xa7PdRVXCWLxRIZGXnx4sVWtrMSFRVVU1PTymYqMjJS07TKykovw6qr6zU4HA4hyXlxk8nkcDhCXUWAmc1ml8vldDpDXcj3vH5HaIkAi4uLO3TokPG4rKwsLi6usUaDxWK56aab3E89d86uTiv7IAgsp9Mpb4AZ3xDtdnsrW8Uul6v1zVRERISqql7fbIrDUe9Yla7rjX3pDkMSleo/KVZBSxzizMzMLCkpOXXqlK7rO3bsGDx4cGONAAD4qSX2wKKioubNm5ebm+twOHr37j1hwoTGGgEA8FOwAuytt97yfJqRkZGRkVFvHK+NaDHG9WDR0dFRUVFBmkR1dXVtbW2QOgfwIyfr9dNoPiO3jhw5cvbs2WD0P2jQIJPJRIABCBIC7MeuoKDg008/DUbP/fr1s9lswegZAAT3QgQASIoAAwBIiQADAEiJAAMASIkAAwBIiQADAEiJAAMASIkAAwBIiQADAEiJO3EgWCIjIy0WS/D+1FXef4EBEBAEGIIlMjKysrLy/Pnzwei8Q4cOERER3GgR+DEjwBBEu3fv3rJlSzB6nj179oABA4LRMwBZcA4MACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJVOoC/AtNja2mT2YTBLMJq6UyWRSVVUI0aZNm1DXEmCaprXKmVIUxevm7IyOrvlhi8mkmS2WlimsmVRVtUhSqv8URWwaWOQAAAnoSURBVNE0zdi+woHL5fLaLsEn+4ULF5rZQ/MjEGHI4XDU1tbabLaLFy/quh7qcgIpJiamsrKylc2UzWYzmUxeN2elqqreJ6XD4dTr6lqmsGayWCx1kpTqv4iICKfT6XA4Ql3I97xmVbgELAAAV4QAAwBIiQADAEiJAAMASIkAAwBIiQADAEiJAAMASIkAAwBIiQADAEiJAAMASEmCW0kBDXXu3NlsNht3uWzfvn3A+3c6nRUVFQHvFkAAEWCQUnR09Pnz5/fu3RuMzpOTk/v27RuMngEEEAEGWZWVlf32t78NRs9jxowhwIDwxzkwAICU2AMD6jNOrUVERASpf13XW98fcAAtjwAD6uvevbsQIiYmJniTqKioCKs/WwJkRIAB9amqqut6dnZ2MDq/9tprFy9eHIyegR8bAgzwrqqqKhjd1tTUBKNb4EeIizgAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABS4k4cQKtiNps1TQte/3a73el0Bq9/wH8EGNCiLBaLECIqKsrlcjU2jslkio6Ovrr+rVbrVVbmn9ra2kuXLgV1EoCfCDCgRXXo0EEIUVZWVltbG4z+k5OTN2/evH379mB0vnTp0uTk5GD0DFwFAgwIgfXr1x87diwYPW/ZsiUY3QJhiIs4AABSYg8MgL/i4uIsFkvbtm2v4rWqqiqK4vW1juho/mMGVyGUAXbw4MGNGzfW1NRkZGRkZ2cbJ7cBhK3o6OgzZ84cOXIksN22PXq03ok1RVFUNfDHh5q4cAYyClmAVVdX5+XlLV++PCkpaeXKldu3b580aVKoigHgpxMnTvz6178ObJ83fP31z37YYjKZgvGNtra2Vtf1gHeLUAlZgBUVFaWkpKSkpAghxo0bt2XLFgIMgOHYsZKKTz4JYIeJiYkpKSlmszmAfRpUVTWy1m63y5iOmqY1/OGgoiiapgVkJ1jXdbvd3vx+vFJCtcTffffdb775ZtasWUKIb775ZvHixa+99pox6PTp0+PHjzceP/DAA3PmzAlJhQBahv33v6+86y7Plujc1ZbJd4eqHoQbl8ulpnn5/UbI9sDqBafnsek2bdosWrTIeNyzZ8/KyspmTktVVf/vTaAoisVisdvtfh4uVxRFUZTgHVtXVTUgnWuaput6va4C1blXQe1cURRjEiaTKeA/qAr5OjWbzVf9pbUFFvvVfes1mUyqqtbV1TUc5Gwws464WD0p8Sqm0oQgLRljZcm+KdVbpxaLxeVyORyO5vfvcrmaf+sWl8vVxlt7yAIsLi7u0KFDxuOysrK4uDj3oKioqLs8vo6VlZW1ZGGaplkslurq6uDt9oaE1Wp1uVxePz7kZbVabTZbZWWljIdumhATE9P6Zspms5lMJq938VBqauodq6qpqdElud9HmzZtWt+tSdq1a1dXV1dVVRXqQnwI2e/AMjMzS0pKTp06pev6jh07Bg8eHKpKAAAyCtkeWFRU1Lx583Jzcx0OR+/evSdMmBCqSgAAMgrl78AyMjIyMjJCWAAAQF7cSgoAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACClkN0LMWxVVFRs3rx50qRJ3bp1C3Ut8OHo0aM7d+6cOXOmycQ/24W7v/3tb+fPn8/Kymo4yPnll/bf/96zxXzHHVrfvi1VGur7zW9+k5ycPGTIkFAX4gObfX0XL17ctGnTgAEDCLDwV1JSsmnTpuzsbAIs/O3du/f48eNeA0zr2VPLyWn5ktCYbdu2DR06NPwDjEOIAAApEWAAAClxDqw+h8Px3XffxcXFRUREhLoW+FBdXV1eXt6pUyfjLyEQzsrLy+12e3x8fKgLgW/fffed1WqNjY0NdSE+EGAAAClxCBEAICWu3fqBgwcPbty4saamJiMjIzs722KxhLoiNGr+/PmlpaXGwcMxY8b87Gc/C3VFqM/pdM6cOfOFF15o0+b//6Eum1jYariywn8TI8C+V11dnZeXt3z58qSkpJUrV27fvn3SpEmhLgre6bp+6tSpN998k0/AsLV9+/aCgoLTp0+7W9jEwlbDlSXFJsYhxO8VFRWlpKSkpKRomjZu3Ljdu3eHuiI06uzZs0KI1atXZ2dn5+fnt77/dG8FunXrlpWVZTab3S1sYmGr4cqSYhMjwL539uxZ9yVSCQkJZWVloa0HTbhw4UJaWtqMGTPWrl0rhNiwYUOoK0J96enpAwYM0DTN3cImFrYariwpNjEC7Hv1Lsh0uVyhqgQ+paamLl26ND4+3mKx3H///fv37+d62vDHJiYRKTYxAux7cXFx7q+EZWVlcXFxoa0HTThx4sTx48eNxyaTyfObI8IWm5hEpNjECLDvZWZmlpSUnDp1Stf1HTt2DB48ONQVoVGXLl1auXJleXm5rut/+MMfBg0axG+Zwx+bmESk2MS4CvF7UVFR8+bNy83NdTgcvXv3njBhQqgrQqOuu+66iRMnLly4UNf1Pn36ZGdnh7oi+MYmJhEpNjHuxAEAkBKHEAEAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAgDD12muv7dmzp/njAK0VAQaEKQIMaBoBBgCQEgEGhN62bduuv/76yMjIpKSkF154QQjRv3//Xbt2PfXUU6NGjRJCfPHFF+PHj+/QoUNMTMyQIUMOHjzYcBzgx4Z7IQIhVlpampqaOnfu3Hvuuefjjz9+8sknd+/effPNN48cOXL06NELFy7UNC0tLa1jx45PP/20EGL58uVVVVV///vfHQ6H5zihng+gpXE3eiDETpw44XQ6s7OzU1NTBwwY0Lt37y5duphMJkVRNE3TNM3lcj3yyCMTJ05MSUkRQnz77bdz584VQniOE+qZAEKAAANC7NZbb50wYcL1118/duzYkSNH/uQnP2nTpo3nCKqqzpw58+OPP962bdvf//73HTt2hOE/MwEtj3NgQIhZrdY//vGPhw4dysjIeOONN7p27frHP/7Rc4TKyspbb711wYIFdrt91qxZ69atC1WpQFghwIAQKygoWLRoUffu3RcvXrx3797Ro0e/+uqrniN89NFHxcXFhYWFTz311ODBg51OZ6hKBcIKhxCBENM0beXKlbGxsf/2b/9WXFy8b98+499vVVU9duzY6dOn27RpU11d/frrr48ZM6awsPDpp5+uqqr65ptvOnfu7B4nMTEx1PMBtDgdQKitWbMmOTnZYrF07dr1ySefrK2t1XX9tddeu+aaayZNmuRyuZYtWxYfH9++fft77rnniy++SE9PHzBggOc4oZ4DIAS4jB4AICXOgQEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApPT/AJpEawY0oMHuAAAAAElFTkSuQmCC" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -465,8 +479,9 @@ <h3>Two categorical (&gt;2 level) variables</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -510,8 +525,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -555,8 +571,9 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -602,8 +619,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -647,8 +665,9 @@ <h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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EBpAlIcAQQQQAABBOwTIAGyr82oMQIIIIAAAgikKUAClCYgxRFAAAEEEEDAPgESIPvajBojgAACCCCAQJoCJEBpAlIcAQQQQAABBOwTIAGyr82oMQIIIIAAAgikKUAClCYgxRFAAAEEEEDAPgESIPvajBojgAACCCCAQJoCJEBpAlIcAQQQQAABBOwTIAGyr82oMQIIIIAAAgikKUAClCYgxRFAAAEEEEDAPgESIPvajBojgAACCCCAQJoCJEBpAlIcAQQQQAABBOwTIAGyr82oMQIIIIAAAgikKUAClCYgxRFAAAEEEEDAPgESIPvajBojgAACCCCAQJoCJEBpAlIcAQQQQAABBOwTIAGyr82oMQIIIIAAAgikKUAClCYgxRFAAAEEEEDAPgESIPvajBojgAACCCCAQJoCxWmW91XxgoICX9Un6JUJqrfGFYlEgt58xOdzgaCuXz5nb3P1aK820+WsYKASoKqqqqxAsmB/xVpYWCilpaUxY8dFhwep07iSYw1CfBqT02ZBiCc+huLiYikqKoofZP17bav49c36gKIBOMtf0OLStgnyNsPW9goVtr5NCFQCtGfPnqxsI0pKSqS8vDwr07ZpouFwWOrr62NV1hVe/0KhUGxYEN6UlZWZPUDxsQYhLl2Ota2CtndLkx+Nq7GxMQjNFItBv3SCtgxqkqp/QYtLEztdDhsaGmLtF4Q3ugzqNt7W9oqUl7TaDMH66d5qqHyIAAIIIIAAAgh8JUACxJKAAAIIIIAAAnknQAKUd01OwAgggAACCCBAAsQygAACCCCAAAJ5J0AClHdNTsAIIIAAAgggQALEMoAAAggggAACeSdAApR3TU7ACCCAAAIIIEACxDKAAAIIIIAAAnknQAKUd01OwAgggAACCCBAAsQygAACCCCAAAJ5J0AClHdNTsAIIIAAAgggQALEMoAAAggggAACeSdAApR3TU7ACCCAAAIIIEACxDKAAAIIIIAAAnknQAKUd01OwAgggAACCCBAAsQygAACCCCAAAJ5J0AClHdNTsAIIIAAAgggQALEMoAAAggggAACeSdAApR3TU7ACCCAAAIIIEACxDKAAAIIIIAAAnknQAKUd01OwAgggAACCCBAAsQygAACCCCAAAJ5J0AClHdNTsAIIIAAAgggQALEMoAAAggggAACeSdAApR3TU7ACCCAAAIIIEACxDKAAAIIIIAAAnknQAKUd01OwAgggAACCCBAAsQygAACCCCAAAJ5J0AClHdNTsAIIIAAAgggQALEMoAAAggggAACeSdAApR3TU7ACCCAAAIIIEACxDKAAAIIIIAAAnkn4FkC1NDQIG+++aa88847EolEEqB37NghL730kmzYsCFh+IEDB2TlypWyatUq0fJ0CCCAAAIIIIBAJgQ8SYCqq6tl/PjxJpF5/vnn5dJLLxVNbrRbt26dTJ48WTZu3CjXXXedLFy40Ayvra2VSZMmybJly+SJJ54wnyUnTmZE/iGAAAIIIIAAAikKeJIAPf300zJmzBi55ppr5KabbpK+ffvKyy+/bKo6d+5cmTVrllx55ZXyyCOPyKOPPir19fUyf/58GTZsmBn/gQcekP3798vq1atTDI/REUAAAQQQQACBpgLFTQdlfsi0adOkoKAgNuEvvvhCdA9PKBSSzZs3y8CBA81n3bt3l4qKCtmyZYts2rRJRo0aFSszZMgQWb9+vQwfPjw2TJOp1157zfQXFxfLK6+8EvuMN5kXKCwslPLy8tiEnTZV+yB1TlzxsQYhPo2rqKgoCKE0iaG0tLTJofUmI1k2QNsriMugNkPQ4tKYgrh+2b4tDB3ku8mTby7dODnd0qVLTdJz7rnnyrZt26R9+/YJyVFVVZXs2rVL9LBZZWWlU8y812QpvtM9RJ07dzaDdMOuSVU2Ov3i18SMLiKNjY0xBmflCNqhSSdJiI81FrTFb3Q5DofDFkfQfNU1Lm2rIC6HQVsGdZuhP5iCFpcumUFcv2zfFh5se+dJAuRsthYtWmTO55kzZ4506NBB9u3b12RF0L1C+utA4eNXEh3erl07Z1Lm9bzzzkvo37p1a0J/pnpKSkpIgKKY4XAk4WR0XeH1T9smSJ3GpF+mQTvxXpdjbaugJQoal24r4rcXQVgeNVkI2jKo23VNgIIWl5PYBTEu3R7aGlck/PXOl+a2CZ4lQI8//ri50mvevHmih7q069q1q0mC9ITosrIyM0z3/vTs2VO6detm9gSZgdF/Ovzwww93enlFAAEEEEAAAQTaLOD6JOizzz5bvvzyyyYzeuyxx2TixIlNhscPeOGFF+TVV1+VBx98MJb86Of6S0APY+meIe2WL19uDmnpYa0RI0bIkiVLpK6uTvQyeb0UfvDgwWY8/iGAAAIIIIAAAukItLoHSK+6cq7Wev311+WOO+5IOHlNdzk/99xz0q9fv1broFd2ff755+ZKMGfEcePGyVVXXSXTp0+PXf6uu9puvvlmM8rIkSPNPYAmTJhgDrPoZfR9+vRxivOKAAIIIIAAAgi0WaDVBEgvV9dL1/UQlR4DXLx4ccJVJHrs/cgjj5Sf/exnrVbgmWeeafHz3r17m0ve9+zZI506dYqNp3uHZs6cKXv37jXn/mg/HQIIIIAAAgggkAmBVrMKPUdH78Ss3dixY+XJJ580Jy9nYsbJ04hPfuI/69ixY3wv7xFAAAEEEEAAgbQFWk2A4qeu5+nonqBPPvkkdhdn53O9ouuwww5zenlFAAEEEEAAAQR8LeA6AVqwYIFcfvnlooeqkruLL75YnnrqqeTB9COAAAIIIIAAAr4UcJ0A6cnK559/vlxxxRXSpUuXhGB0DxAdAggggAACCCBgi4CrBKimpsbctXn27Nly6KGH2hIb9UQAAQQQQAABBJoVcHUfIH0khV6Cvnbt2mYnwkAEEEAAAQQQQMAmAVd7gDQgfWL71VdfbZ7jpclQ/GXpuldowIABNsVNXRFAAAEEEEAgjwVcJ0B6DpA+xV2f7J7ccRJ0sgj9CCCAAAIIIOBnAdcJkN7JuaWHKDpPjPVzoNQNAQQQQAABBBBwBFwnQPow0pYSIH1Kuz6/iw4BBBBAAAEEELBBwHUC1L9/f3MIrLmgOATWnArDEEAAAQQQQMCvAq4TIH0Yqj781On0GV1///vf5f777xe9PJ4OAQQQQAABBBCwRcB1AnT88cc3ienUU0+V/fv3y+233y6/+c1vmnzOgGAJFBQUJDwMt7CwUJKHZSPi+MQ7G9NnmggggAAC+SfgOgFqiUafAfbcc8+19DHDAySgCU9ZWVmTiOJvidDkwwwM0GfQkQRlAJJJIIAAAgjEBFwnQG+99ZaEQqFYQX1fXV0tv/jFL+T000+PDedNcAV27twpb734omcBlpSUyFlnneXZ/JgRAggggED+CLhOgEaNGtXsSdB6GGzmzJn5I5bHkepVgOFw2DMBL+flWVDMCAEEEEDAFwKuE6BPPvmkyWXw7du3T7gjtC8iohIIIIAAAggggMBBBFwnQPo8sD179siTTz4pGzdulB49esjgwYPljDPOkNLS0oPMho8RQAABBBBAAAH/CLhOgN59913Rw2B6R+iBAwfK9u3bZevWrXLOOefIwoULpby83D9RURMEEEAAAQQQQKAVAVdPg9fyU6dOlW9961vy6aefyjvvvCNbtmwx9wF6//335de//nUrs+AjBBBAAAEEEEDAXwKuEiC918/atWvNFV+9evUyEej9X/QQ2DXXXCNLly71V1TUBgEEEEAAAQQQaEXAVQKk93/RK4A0EUru9u3bl3B5fPLn9COAAAIIIIAAAn4TcJUA6fk9eq+f66+/XtasWWOSobq6OlmyZInce++9cvbZZ/stLuqDAAIIIIAAAgi0KOAqAdLSDz30kDkB+uSTT5bu3btLly5dZPTo0XLSSSfJtdde2+IM+AABBBBAAAEEEPCbgOurwPr16ydvv/22vPLKK6InPuteoUGDBskpp5zit5ioDwIIIIAAAggg0KqA6wRIp6LPgdK9PmPGjDETbe6coFbnxocIIIAAAggggIAPBFwfAtN7/kyePFn+8Ic/mGrryc99+vSRe+65h5OgfdCQVAEBBBBAAAEE3Au4ToCuvvpq+eijj+Tb3/62mXq7du1k7ty55iToRYsWuZ8jYyKAAAIIIIAAAjkWcHUIrL6+Xl566SV57733pGfPnqbKemn8hAkTZO/evebxGBdeeGGOQ2H2CCCAAAIIIICAOwFXe4D0pocNDQ3m7s/Jk62pqZFdu3YlD6YfAQQQQAABBBDwrYCrBKikpETOPPNMueGGG8yjMJxo9O7Qeg6QPg+MDgEEEEAAAQQQsEXA1SEwDeaxxx6T8847T4444gjRJ8PrYTG9GeLEiRPN4zD8EHDnzp2zUg3dA0aXO4Hi4mIpKiryrALOnc9LS0s9m6cXM9K4nNi8mJ+X8/B6GfEiNm2roC2DzrY0iHFpbE58XiwfXsxDl0GNydb2ChW2/r3hOgHSGx+uWLFCNmzYIP/4xz/MhvSEE06Q4447zot2cDWP3bt3uxov1ZF0D5jeAoAuNwKhUEgaGxs9m7m2tT76RZP8IHW6HKulxhakTpMfr5cRL/z0Sydoy6D+kNG/oMWlSYIuh3qqSJA6XQY1CbK1vSLlJa02h+sESKeiEP379zd/rU6VDxFAAAEEEEAAAR8LuDoHyMf1p2oIIIAAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCxAApQyGQUQQAABBBBAwHYBEiDbW5D6I4AAAggggEDKAiRAKZNRAAEEEEAAAQRsFyABsr0FqT8CCCCAAAIIpCzgeQL08ssvSygUSqjojh075KWXXpINGzYkDD9w4ICsXLlSVq1aJQ0NDQmf0YMAAggggAACCLRVwNMEaMGCBXLbbbdJY2NjrL7r1q2TyZMny8aNG+W6666ThQsXms9qa2tl0qRJsmzZMnniiSfMZ5FIJFaONwgggAACCCCAQFsFittaMJVyusfn5ptvlp07dzYpNnfuXJk1a5aceOKJcskll8jll18uY8aMkfnz58uwYcNkxowZpsy0adNk9erVMnz48CbTYAACCCCAAAIIIJCKgCcJkO7x0cRl9OjRcsYZZ8Tqp4nR5s2bZeDAgWZY9+7dpaKiQrZs2SKbNm2SUaNGxcYdMmSIrF+/PiEBuuWWW2T58uVmnOLiYlmyZElsfN4ER6CkpET0z6uuoKDAzKq8vNyrWXoyH42rqKjIk3l5PROvlxEv4tP2Ctoy6LgFMa4grl+2bwtD0bygta71T1srmcJnZWVlMnbs2CYltm3bJu3btxcHWUeoqqqSXbt2SXV1tVRWVsbK6HtNluK7wYMHm4RJh+mGva6uLv7jjL0vLCwUTbDociMQDofFy8OfTpIQf6g2N5Fndq66HKtl0DonLi+XES8MdTkM2jKo23ptr6DFpcuDsxx6sWx4NQ/bt4UH297l9Fu9uRVc9wrpr4Pkz3R4u3btEtr9/PPPT+jfunVrQn+mevTXpe6ZosuNgG4svdxg6oZMv0yDduK9Lse6HgUtUdC4vF5GvFgTNFkI2jKo23X9MRm0uLStghqXbg9tba9IuLTVVTWnCVDXrl1l3759old76V4i7XTvT8+ePaVbt27mvVN7HX744Yc7vbzmkYBuXPTP607nGbRkwWtD5ocAAgj4VSCnCZBmzHqi86JFi+Tiiy825/N07txZ9G/EiBHmnB59/fLLL82l8HfccYdfHalXFgScPTGlpa1n8VmYtZmkLp+6x6S+vj5bs2C6CCCAAAI5EshpAqQxT58+PXb5u37h6dVi2o0cOdLcA2jChAnm2Or48eOlT58+5jP+5YeA7i7XvTAffvhhwt5Ar6Lv27evdOjQwavZMR8EEEAAAQ8FPE+AVqxYkRBe7969zSXve/bskU6dOsU+01/fM2fOlL1795pzf7SfLj8FampqRE+Y97rTQ64kQF6rMz8EEEDAGwHfZBXxyU986B07dozv5T0CCCCAAAIIIJC2gKd3gk67tkwAAQQQQAABBBDIgAAJUAYQmQQCCCCAAAII2CVAAmRXe1FbBBBAAAEEEMiAAAlQBhCZBAIIIIAAAgjYJUACZFd7UVsEEEAAAQQQyIAACVAGEJkEAggggAACCNglQAJkV3tRWwQQQAABBBDIgAAJUAYQmQQCCCCAAAII2CVAAmRXe1FbBBBAAAEEEMiAAAlQBhCZBAIIIIAAAgjYJUACZFd7UVsEEEAAAQQQyIAACVAGEJkEAggggAACCNglQAJkV3tRWwQQQAABBBDIgAAJUAYQmQQCCCCAAAII2CVAAmRXe1FbBBBAAAEEEMiAAAlQBhCZBAIIIIAAAgjYJUACZFd7UVsEEEAAAQQQyIAACVAGEJkEAggggAACCNglQAJkV3tRWwQQQAABBBDIgAAJUAYQmQQCCCCAAAII2CVAAmRXe1FbBBBAAAEEEMiAAAlQBhCZBAIIIIAAAgjYJUACZFd7UVsEEEAAAQQQyIAACVAGEJkEAggggAACCNglQAJkV3tRWwQQQAABBBDIgAAJUAYQmQQCCCCAAAII2CVAAmRXe1FbBBBAAAEEEMiAAAlQBhCZBAIIIIAAAgjYJUACZFd7UVsEEEAAAQQQyIAACVAGEJkEAggggAACCNglUGxXdVuvbefOnVsfoY2fFhQUtLEkxWwXKCwslNLSUtvDMPXXWPQvEokEIp74IIqLi6WoqCh+kPXvg7TsOY3hbEuDsk7Fx6WxOfE5w21/1WVQY7K1vRoPsk0IVAK0e/furCxvJSUlUlZWlpVpM1F/C4TDYamvr/d3JV3WTpfjUCgUuARIkx+Nq7Gx0aWEHaPpl05Qlj1HXJNU/QtaXJok6HLY0NDghBqIV10GNQmytb0i5SWttgOHwFrl4UMEEEAAAQQQCKIACVAQW5WYEEAAAQQQQKBVARKgVnn4EAEEEEAAAQSCKBCoc4CC2EDElDuByspKc1xfj+3notOTlWtra3Mxa+aJAAIIBF4gN1v2wLMSYBAE9MTGmpoa2bhxo+fh6BWNRx99tOfzZYYIIIBAvgiQAOVLSxNnmwT06odt27a1qWw6hYJ2OW06FpRFAAEEsiHAOUDZUGWaCCCAAAIIIOBrARIgXzcPlUMAAQQQQACBbAiQAGVDlWkigAACCCCAgK8FSIB83TxUDgEEEEAAAQSyIUAClA1VpokAAggggAACvhYgAfJ181A5BBBAAAEEEMiGAAlQNlSZJgIIIIAAAgj4WoAEyNfNQ+UQQAABBBBAIBsCJEDZUGWaCCCAAAIIIOBrARIgXzcPlUMAAQQQQACBbAiQAGVDlWkigAACCCCAgK8FSIB83TxUDgEEEEAAAQSyIUAClA1VpokAAggggAACvhYgAfJ181A5BBBAAAEEEMiGAAlQNlSZJgIZEigoKMjQlER0WpmcXsYqxoQQQACBHAgU52CezBIBBA4i0LFjRzNGu3btDjJmah8XF7tf5evr6yUUCqU2A8ZGAAEELBFwvzW0JCCqiUAQBIqKikwY7777rjQ0NHge0qBBgzyfJzNEAAEEvBQgAfJSm3khkKLA9u3b5cCBAymWSn/0SCSS/kSYAgIIIOBjAc4B8nHjUDUEEEAAAQQQyI4ACVB2XJkqAggggAACCPhYgATIx41D1RBAAAEEEEAgOwIkQNlxZaoIIIAAAggg4GMBToL2ceNQNQRyJVBYWGjuGaSvuejC4TCX4OcCnnkikEcCJEB51NiEikAqAnoPIL0XkNddWVmZ6G0AuAeR1/LMD4H8EiAByq/2JloEXAt89NFH8sEHH7geP1Mj9u/fX4444ohMTY7pIIAAAs0K5Gb/drNVYSACCCCAAAIIIOCNgK/3AOkN4NauXWvORRg6dKiUlJR4o8JcEEDACgGebWZFM1FJBHwp4NsEqLa2VqZMmSIDBgyQzz77TJ5++mmZM2cOD3P05WJEpRDInECnTp3MOUAVFRWuJ6rnDWWq07tg6+NHcnE3bH1Wm/MYFI0nlWe3ZSJ+Pfm8rq4uE5NiGgj4XsC3CdD8+fNl2LBhMmPGDIM4bdo0Wb16tQwfPtz3qFQQAQTaLqBXnunJ1//85z/bPpE2ltTk6+ijj5bS0tI2TiG9YpqA7NixQz7++OP0JtSG0oceeqj06tWrDSUpYrOAJtktXe3pXA2azfUhl1d8FkR/5fjyoT833XSTjBo1Sk477TSzbD388MPmEJjuFXK62267TVasWGF6tRGff/5556OMv+r0d+3alZMHU3bt2tXMt6amJuNxtTTBcJ9jRGoTfwkWnTRU2t97X0tFMj5cD2/o09Bz9VTy8vJysxcgF8/i0sO9+qe/xnUD4XWnsTc2NuZkedd5a5eLPRG6nuvGXq9Ay8WmUff+aHvn4uo7jTt+71O2ljldr3Nhm614bJ+utkVLCZAXsWVz+xaqKJOyY/u0GIZv9wBVV1dLZWVlrOL6fvPmzbF+faOHx5wVVl+z+UWljdSxY8eE+bvt0YVL/9K5rFe/DLt06eJ2lmmPt0MKmkyjsLKDlJ40IDbcOf8imxuzsNpF7b38Pa7LksbkxFWao98IGntxBpMfXQZdb2yi4xZ47O4sWJHoF6R2bt113chkwqLuUahm1gCnhtl71dg1eo1dl0NNQr3swllOTnSboUlmKHqIMblLaflMLpxmfya2ZenUP52yaYb+1XdTC9sZrZfaZHM51G1SOt+NrcWv9W7t4LhvE6DklV+BdG9AfDdu3Lj4Xtm6dWtCv196OnToIHo+g+7atqdrumOwoaE+IQbnfi379++3JywXNdU9brq8ffHFFy7GtmcU/RGxb9++rG7MvNbQjXOPHj1k7969OdljlM14O3fuLLt3787mLDyftu7d07h27twZ+4HheSWyMEP9vmrfvr14uZc+C2E0mWRVVZXZE23Xd9fXYRzs3EDfXgbfrVs3c8jJCUUPP/Xs2dPp5RUBBBBAAAEEEGizgG8ToBEjRsiSJUvMrzrNPletWiWDBw9uc6AURAABBBBAAAEEHAHfHgIbOXKkrFy5UiZMmGCOUY4fP1769Gn5ZCYnIF4RQAABBBBAAIGDCfg2AdIT5WbOnGmO7eu5P9pPhwACCCCAAAIIZELA91lFW6+8ygQO00AAAQQQQACBYAr49hygYHITFQIIIIAAAgj4QYAEyA+tQB0QQAABBBBAwFMBEiBPuZkZAggggAACCPhBgATID61AHRBAAAEEEEDAUwESIE+5mRkCCCCAAAII+EHA91eBpYLk10vl9S7WW7ZsEX3Egi1d49kjJfpwtcTqHh999lrc7Qic5+f41T2x8u779EncGpM+wiRInT5zx3m2T1Di0pjWr19vnht4sNve2xazPscoaOuWPopFH1mk20J9fERQOt0W6rIYtPb6/PPPzWOBOnXqZGVTHWwZ8+3T4K3UbqHSc+fOlaeeesrczbqFURjsI4GLLrpIjjrqKLnzzjt9VCuq0pyAPq/t5JNPlrvvvlvOO++85kZhmI8EFi9eLNdcc428+eab5plgPqoaVWlG4IYbbpANGzbIwoULm/nU/kEcArO/DYkAAQQQQAABBFIUIAFKEYzREUAAAQQQQMB+gaL/i3b2h+HvCPS8iyOOOEIGDRrk74pSOyOg7XX88cebw2CQ+FtAz70oLS2VYcOGWXWOnb9Vs1c7XbcOOeQQc9gyaOfLZE8td1PW9urbt68MGDAgd5XI4pw5ByiLuEwaAQQQQAABBPwpwCEwf7YLtUIAAQQQQACBLAoE6jL4LDq5nrSeMa+XUQ8ZMkS6devWbLkD0cvL165dK7r7fujQoVJSUtLseAzMrkBDQ4O8/fbb0q5dOxk4cKBpj+Q5fvnll/Kvf/0rYfDw4cMT+unJvkAq7eBmHcx+jfN3DnqZ+0cffZQAoNu65tabdevWRe+28fXtNo455hjp0qVLQll6sivw8ssvyxlnnJFwCb+bdSgI32McAsvgsnXPPffIe++9J/369TOXvM+bN8+c+xM/i9raWpkyZYo5pvrZZ5+J3rtkzpw5zX75xpfjfWYFqqur5cc//rGccsopsn//fnMvmd///vemPeLntGzZMrnvvvvMcXBn+OzZs523vHok4LYd3KyDHlU5b2ezYsUK+etf/xqLX7dzeruCRYsWxYbpm1AoJOecc45885vfjA3/3ve+JyeccEKsnzfZFViwYIHobVpeeeWV2LbPzToUmO+xCF1GBP773/9GLrjggkj05mVmen/6058it99+e5NpR79kI9EFLDZ86tSpkTfeeCPWzxtvBO69997I7373u9jMfv7zn0eef/75WL/z5uGHH4489thjTi+vORJw0w5u18EchZCXs62rq4tceumlkddff71J/P/5z38il112WZPhDMi+QHTvd+TGG2+M6PfPqaeeGtF20s7tOhSU7zHOAcpQgv3hhx+awyh61rx2eghM71Cb3G3atMl85gxvaTznc16zIzBt2jT5/ve/H5u4/kLVXzXJXXQjbe4I/eSTT8qaNWskuo1IHoV+DwTctIPbddCD6jKL/wlEf2SYPTq6pzW50zbt1auXvPjii/KXv/zF7IlNHof+7AjoXcb1kOT999+fMAO361BQvsdIgBKav+09ety7qqoqNoHKykrZuXNnrN95o4de9DOna2k853NesyOgl047514tXbpUNm/eLOeee26TmelGWu9aq4cq//jHP8pPf/rTJuMwIPsCbtrB7TqY/doyBxXQHxXPPvusTJo0qVmQjRs3mrsM792717xecsklsmPHjmbHZWBmBXR7Nnbs2ITzfnQObtehoHyPcRJ0hpYrfeaIZtVOp8e39eTa5M7teMnl6M+OgJ6X8MQTT5jzsJp79tejjz4q+hwc3bOnj1r4zne+Y5Il/eVK552Am3Zg3fKuPdzMSffs6GNKunfv3uzoP/zhD0X/KioqzOd6Uq2W0fOA6HIj4HYdcjtebqJwP1f2ALm3anVMvbmXPvTU6fT9YYcd5vTGXvXKsOTxevbsGfucN94JPP744+YZbXqyeu/evZvMuL6+3lzR5xzW1L1GujHXXz903gm4bQe366B3Nc/vOb3wwgty/vnnt4igD4iOvwJMbxareyDocifgdh0KyvcYCVCGljW9nF0vl/7000/N1Q3RE2rNrx+dvO7WdXbtjhgxQpYsWSLRk87MsFWrVsngwYMzVAsm41ZAN86vvvqqPPjgg01+oeolvNo+eohMr9DTQ2Da/fvf/zaHNbmjt1vlzIx3sHZw2qu1dTAzNWEqbgU0sdF20Tuqx3d6np3eJkS75cuXy0MPPWTe65WYeihaL8emy51Aa+tQfNsF5XuMy+AzuKzppZ+6N0HvY6F7FGbNmmWOsd51113my3TGjBkmObr11ltNsqR7FsaPHy8XX3xxBmvBpNwI6BPfP//884TbD4wbN06uuuoqcy7QHXfcISeeeKK5T9Ajjzwies+g7du3iz4dubkTOt3Mk3HaLqD3a2qpHfTcLae9WloH2z5nSrZFQM/Zil5lJM8880xCcb3/2W233WYuia+pqRG9pYReJq/r1tlnny1XXnmlOdycUIierApoMhN/GXxL61B82+kpHkH4HiMByvCipV+U+uunufNJ4melJ/7pOUI8Dydexb/v9YROPWFdb+hGlzsBN+3gdh3MXRTMOV5A9/7oOSV6Yi6dPwTcrkO2f4+RAPljeaMWCCCAAAIIIOChAOcAeYjNrBBAAAEEEEDAHwIkQP5oB2qBAAIIIIAAAh4KkAB5iM2sEEAAAQQQQMAfAiRA/mgHaoEAAggggAACHgqQAHmIzawQQAABBBBAwB8CJED+aAdqgQACaQrofWVS6VIdP5VpMy4CCPhfgATI/21EDRFA4CACemfvc8455yBjff1xquN/XZJ3CCAQFAESoKC0JHEgkMcC+hiaffv2uRZIdXzXE2ZEBBCwRoAEyJqmoqII5LfAunXrZPLkyXLWWWeZp4i/9dZbBuS1116TBQsWyObNm83w3bt3m+H6PL4pU6aY8X/wgx+YZ/DpBy2NbwrxDwEE8kaABChvmppAEbBXQJ/bdvrpp0t5eblJcvSRJPpMtvfff18OPfRQ6dWrl1RUVJgHEJeWlsr9998vEydOlKOOOkouu+wy0cctjB49WtasWdPs+PbKUHMEEGirQHFbC1IOAQQQ8EpAD1lpEqMPYNSE57vf/a70799fIpGIeR02bJhs2rTJJEdap23btsmvfvUrmTp1qqmiJkOHHHKIvPnmm+aBm8njexUH80EAAf8IkAD5py2oCQIItCCgCcvRRx8t/fr1k1GjRok+AV4Pa3Xt2rXZEpoobd26VZ599lmzl+idd96R2tpaqaura3Z8BiKAQP4JcAgs/9qciBGwTqBDhw6i5/z88pe/NHuCpk+fbg5vLVu2rNlY5s6dK3369DHj696giy66yOw5anZkBiKAQF4KkADlZbMTNAJ2CWzcuFH0pGZNfBYvXmwOcekhsPvuu88EoucEOZ3u6bn++uvlrrvuMuf8aDJ04YUXmjLhcLjJ+E45XhFAIL8ESIDyq72JFgFrBSZNmiTPPPOMNDY2it7EUK/20sNi2nXp0sUc8tJESTs9NFZdXS2a8Oi5Qz/5yU/kwIEDsUNg8eOHQiFThn8IIJBfAiRA+dXeRIuAlQLHHHOM3H333XLTTTdJVVWVObx1wgknmH4N6LTTTpOioiI59thjRc/3ufPOO+XPf/6zOezVo0cP0UNo48ePF72UPnn8tWvXmmH8QwCB/BIoiF5FEcmvkIkWAQRsFtA9O506dTKXxCfHsWfPHvOZM1zvDaQJUHFx89d7JI/vlOMVAQSCL0ACFPw2JkIEEEAAAQQQSBLgEFgSCL0IIIAAAgggEHwBEqDgtzERIoAAAggggECSAAlQEgi9CCCAAAIIIBB8ARKg4LcxESKAAAIIIIBAkgAJUBIIvQgggAACCCAQfAESoOC3MREigAACCCCAQJIACVASCL0IIIAAAgggEHwBEqDgtzERIoAAAggggECSwP8HAQSpi2KlrJ4AAAAASUVORK5CYII=" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -692,8 +711,9 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -737,8 +757,9 @@ <h3>Two numerical vars - correlation</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
 null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
@@ -787,9 +808,6 @@ <h3>One numerical (one mean)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -806,12 +824,11 @@ <h3>One numerical (one mean)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> x_bar) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -828,8 +845,10 @@ <h3>One numerical (one mean)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="one-numerical-one-mean---standardized" class="section level3">
 <h3>One numerical (one mean - standardized)</h3>
@@ -856,9 +875,6 @@ <h3>One numerical (one mean - standardized)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -875,12 +891,11 @@ <h3>One numerical (one mean - standardized)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -897,8 +912,10 @@ <h3>One numerical (one mean - standardized)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="one-categorical-one-proportion-1" class="section level3">
 <h3>One categorical (one proportion)</h3>
@@ -925,9 +942,6 @@ <h3>One categorical (one proportion)</h3>
 <span class="st"> </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st"> </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -944,12 +958,11 @@ <h3>One categorical (one proportion)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> p_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -966,8 +979,10 @@ <h3>One categorical (one proportion)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="one-categorical-variable-standardized-proportion-z-1" class="section level3">
 <h3>One categorical variable (standardized proportion <span class="math inline">\(z\)</span>)</h3>
@@ -998,9 +1013,6 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1017,12 +1029,11 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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AACCCCAQNEKkAAVbdcSGAIIIIAAAgikEyABSifDcAQQQAABBBAoWgESoKLtWgJDAAEEEEAAgXQCJEDpZBiOAAII5CgQ5AmxOTaFyRFAYBiBoroKbJhYGY0AAghkFNAVgbpqpba2NuN0w42sr68fbpK043WlTNhXy6RtDCMQKGIBEqAi7lxCQwCB3AR08z/txdm5c6edOHEitw/7MPX06dPdZc8+VEUVCCAwjAAJ0DBAjEYAgdITOHr0qB07diz0wKdOnUoCFLo6MyxVAc4BKtWeJ24EEEAAAQRKWIAEqIQ7n9ARQAABBBAoVQESoFLteeJGAAEEEECghAVIgEq48wkdAQQQQACBUhUgASrVniduBBBAAAEESliABKiEO5/QEUAAAQQQKFUBEqBS7XniRgABBBBAoIQFSIBKuPMJHQEEEEAAgVIVIAEq1Z4nbgQQQAABBEpYgASohDuf0BFAAAEEEChVARKgUu154kYAAQQQQKCEBUiASrjzCR0BBBBAAIFSFSABKtWeJ24EEEAAAQRKWIAEqIQ7n9ARQAABBBAoVQESoFLteeJGAAEEEECghAVIgEq48wkdAQQQQACBUhUgASrVniduBBBAAAEESliABKiEO5/QEUAAAQQQKFWByrAD37Rpk11++eVWWfm/sz5+/Li98MILA5oxd+5c976jo8O2b99uZWVlNnv2bKuqqhowHW8QQAABBBBAAIGRCISaAD3yyCO2evVqu/TSSxMJ0LZt2+y+++6zs88+O9F+JUCnTp2ypUuX2owZM2zfvn22fv16W7VqlUuGEhPyAgEEEEAAAQQQGIFAKAlQd3e3rVixwo4cOTKkia+++qpdc801dtNNNw0Yt27dOpszZ44tX77cDV+2bJlt3brVvL1DAybmDQIIIIAAAgggkINAKAlQT0+PS1yuvPJKd/gruX1KgJTUrF271qZOnWoXXXSR28uza9cuW7BgQWLSWbNm2Y4dOwYkQNqj9PLLL7tpKioq7Pbbb09MH/UXOgQ4evToqDczY/vKy8vdYcm4x1FdXZ3oi67enpQxl/cfho3qIVgdIlbRMtXX15ey/XEYqOXJ+9fV1RWHJgfSRm+9CqTyLCuNQhuybGraybRe6HvBjyKPVEXbPm/9SzU+32GqO+7fFZ7dqFGjrLe3N1+SnD6v3CNTCSUBqqmpcXt5UjVECZCKkqCHHnrItOfnnnvusQMHDiS+lDReC9qePXv0MlFefPFFe/rpp917LSSf/vSnE+Oi/kILhVziXBSDVtBiiMOLoay3O2WX+LkxTTkDHwZ6GxofqipIFTJWAqe/pVyisKxFoQ1+LAN+xVFmqZdJbTeCXF5Vt/7Ffd1WXxbiB6TOI85UQkmAMjVgzZo1NnbsWNfBCxcutGuvvdYlOsrck7M3HUarq6sbUJUOqyWX/fv3J7+N9Otx48bZsWPHIt3G4RrX1NRk+qXe0tIy3KSRHa8NS0NDg7W2tro2ptsD1NP/y6W9vT2ScWgDqXWjs7Mz9F9YfoLoR4x+Ifr1q93PtoVZl7Z76stCFu0VLXQb8o2/trbWfYf4sTexJ82e4UOHDgWaAGmd0Lrd1taWL0fBPq8YJk6caM3Nze77IsyGKEHVnqd0JfV+vXRT+zxcK9ju3bsT2a1WukmTJrm9P/pyPXr0aGKOej158uTEe14ggAACCCCAAAIjFShoAqRdYrqy65lnnnHtf+mll9yJ0hdeeKHNmzfPNm7c6H51Hz582LZs2WIzZ84caZx8DgEEEIi8gLaJ2iupH4OF+FeIwxSR7xQaWLQCBT0Epl33usrrwQcfdP+0O/Guu+5yJ33Nnz/fNm/ebIsXL3YbhEWLFtmUKVOKtiMIDAEEENDhAu0Z1/3Rwi461FJfXx/6YYqw42R+CHgCoSdA3knLXgPe9a532QMPPODOI0k+o14bgpUrV7pjn1ox9Z6CAAIIFLuAfgj+8Y9/DD3Ms846y6ZPnx76fJkhAoUSiExWMWbMmJQGjY2NKYczEAEEEEAAAQQQGKlAQc8BGmmj+RwCCCCAAAIIIJCPAAlQPnp8FgEEEEAAAQRiKUACFMtuo9EIIIAAAgggkI8ACVA+enwWAQQQQAABBGIpQAIUy26j0QgggAACCCCQjwAJUD56fBYBBBBAAAEEYilAAhTLbqPRCCCAAAIIIJCPAAlQPnp8FgEEEEAAAQRiKUACFMtuo9EIIIAAAgggkI9AZO4EnU8QfBYBBPwV0EMx9ay+MIvmV1FR4ebb1dUV5qyZFwIIlKAACVAJdjohIzCcgBIRPZCzEA/lbGpqGq55jEcAAQTyFiABypuQChAoToH9+/fbn/70p9CDu+SSS0x7oCgIIIBAkAKcAxSkLnUjgAACCCCAQCQFSIAi2S00CgEEEEAAAQSCFCABClKXuhFAAAEEEEAgkgIkQJHsFhqFAAIIIIAAAkEKkAAFqUvdCCCAAAIIIBBJARKgSHYLjUIAAQQQQACBIAVIgILUpW4EEEAAAQQQiKQACVAku4VGIYAAAggggECQAiRAQepSNwIIIIAAAghEUoAEKJLdQqMQQAABBBBAIEgBEqAgdakbAQQQQAABBCIpQAIUyW6hUQgggAACCCAQpAAJUJC61I0AAggggAACkRQoqqfBjxs3LpLIqRqlp13Hqb2pYqioqLCysrLYx1FZWZmIobO3x2z30GjLy8uturp66IgIDFEfqGiZ6uvri0CLaEKcBbScR3l5z9ZW64W3jcr2M+mmk0eqom24t/6lGp/vMC8GbaPiWjyfxsbG0LdP3d3dGdniq5oirGPHjqUYGs1BWnHi1N5Uik1NTdbV1WUtLS2pRsdimDZsDQ0N1tra6trbpQQoRent7bXOzs4UYwo/SBuYuro61xdqpx+ltrbWj2qoI4YCWs6VBEV1ec+WVMtwT0+PWy+y/Uy66dKtV9qGe1/w6T6bz3AlPlq329ra8qmmoJ9VDBMnTnQx6PsizFJTU5NxdqnT2owfYSQCCCCAAAIIIBBvARKgePcfrUcAAQQQQACBEQiQAI0AjY8ggAACCCCAQLwFSIDi3X+0HgEEEEAAAQRGIEACNAI0PoIAAggggAAC8RYgAYp3/9F6BBBAAAEEEBiBAAnQCND4CAIIIIAAAgjEW4AEKN79R+sRQAABBBBAYAQCJEAjQOMjCCCAAAIIIBBvARKgePcfrUcAAQQQQACBEQiQAI0AjY8ggAACCCCAQLwFSIDi3X+0HgEEEEAAAQRGIEACNAI0PoIAAggggAAC8RbIOgH6wAc+YMePHx8S7Q9/+EO74YYbhgxnAAIIIIAAAgggEFWBykwN27p1q23atMlN8l//9V/29a9/3WpraxMf6enpsX//93+3qVOnJobxAgEEEEAAAQQQiLpAxgTo7LPPtk9+8pPW0dFhXV1d9vjjj1tFRUUipqqqKjvrrLPsc5/7XGIYLxBAAAEEEEAAgagLZEyAJkyYYJs3b3YxXHPNNbZ27VpraGiIeky0DwEEEEAAAQQQyCiQMQFK/uSGDRvcnqDXXnvN/U0ep6ToTW96U/IgXiOAAAIIIIAAApEVyDoBeuSRR+yWW26x5ubmIcF87GMfs5///OdDhjMAAQQQQAABBBCIokDWCdBtt91mH/rQh+wTn/iEjR8/fkAsHBYbwMEbBBBAAAEEEIi4QFYJUGtrqx08eNC+8Y1v2GmnnRbxkGgeAggggAACCCCQWSCr+wCNHj3apkyZYtu3b89cG2MRQAABBBBAAIEYCGS1B0hx3H333XbHHXfYnj17XDJUWfnGR7VXaMaMGTEIlyYigAACCCCAAAJmb2Qxw2joHKCWlhZbtmzZkCk5CXoICQMQQAABBBBAIMICWSdAr7/+uvX19aUMJfnmiCknYCACCCCAAAIIIBAhgawToKNHj6ZNgOrq6mzcuHERCoumIIAAAggggAAC6QWyToCmT5/uDoGlqiqXQ2B6ttjll19uyecQHT582J599ln3WI1p06YlZqFHcOjE67KyMps9e7bp0RsUBBBAAAEEEEAgX4GsrgLTTPQw1P/+7/9O/Hv66aftX/7lX+wd73iHuzw+m4boZopf+cpXTA9R9cpzzz1nN998s73yyit255132qOPPupGnTp1ypYsWWJPPPGEPfzww25cukNwXl38RQABBBBAAAEEshHIeg/QueeeO6S+9773vXby5En76le/at///veHjPcGdHd324oVK+zIkSPeoMTf1atXuyvMLrjgArv++uvd3aavuuoqW7dunc2ZM8eWL1/uptXJ13o6/dy5cxOf5QUCCCCAAAIIIDASgaz3AKWrXM8A++Mf/5hutBuuPT5KXO6///4B0ykx0mX1559/vhs+adIkq6+vt71799quXbts1qxZien1eseOHYn3vEAAAQQQQAABBEYqkPUeoN///vemhMUren3gwAH7p3/6J7vsssu8wSn/1tTUmJ4mP7jo7tKjRo1y5/h448aMGWM64Vp16waMXtFrJUvJ5fbbb7df//rXbpDODxouEUv+bBRen3766VFoRl5t0LlcOgk+7kWJt0pnb/8yvntoNLrSMcpx6vCw1jO/Coeb/ZKMXz3ecu79jV8Eb7RY26fk803fGJPbq3RXOmsbrnNUgy76nox7mTBhQugh6FSaTCXrBGjBggUpT4LWYbCVK1dmmkfacVqoks8H0oRKrGpra23wOA0fvEJ+9KMftYsvvtjVX15ebnpkR1yKvnB1+DDORc+AU/8Nt5BFOUZtvJQ4tLe3u2Z29b5xflpyu/t6e62rqyt5UKCv1S4l9To3Lsz5ekHpogdKaQpoeVPSkPyDN44SikGJ/ODvmJHE0tu//qcq+s4JMgHS91p1dXVi+5SqDVEfphgaGxvtxIkTvvSFn/FmnQC99tprQy6DV1aaT3atjFAoutrL+/WqvT+TJ0+2pqYmtyfIC1bDzzjjDO+t+/u+971vwPv9+/cPeB/lN4o37gmQkjhtJOMch1ZOJdteDOkSoN7+DWmYXwhqlxKg//mf/3HrR9jLMglQ2OLRmZ+Wcy1/YS7vQUSv7yYlLn7EkW6PqLYbQSZAikH1e9unIJyCrlMxKAHSj8ywf8x5eUW6GLM+B0iHoLQQ/OQnP7EvfvGL9t3vftd+85vfWGdnZ7q6hx0uGJ3ovGHDBjftU0895e4npHsKzZs3zzZu3OjQdJn8li1bbObMmcPWyQQIIIAAAggggMBwAlnvAfrDH/5gOgymO0LrpOVDhw6Z9rhcccUV7tJ1HbYaSdEjNrzL3/WrQ1eLqcyfP982b95sixcvdr9GFi1a5J5BNpJ58BkEEEAAAQQQQCBZIOsE6NZbb7V3v/vddu+999pb3vIWtzdI9wW67rrr3P2APvvZzybXm/a17h+UXM4880x3yXtzc7ONHTs2MUp7h3RuUVtbmzv3R+8pCCCAAAIIIICAHwJZHQLT8UfdkVlXfCn5UdFxSR2S+uQnP2m//e1v825LcvKTXJmOHZL8JIvwGgEEEEAAAQTyFcgqAdKhKZ3/k+pELJ3E7MdJZvkGwucRQAABBBBAAIFsBbJKgHR+z2WXXWY6zLVt2zaXDOmMbp2k/K//+q/2gQ98INv5MR0CCCCAAAIIIFBwgawSILXye9/7njsBWvfd0R2bx48fb1deeaVddNFF9qlPfarggdAABBBAAAEEEEAgW4GszyyeOnWqe2L7f/7nf9rLL7/sblZ44YUX2iWXXJLtvJgOAQQQQAABBBCIhEDWCZBaq5sKaa+PHlaqkuqcIDeC/xBAAAEEEEAAgQgLZH0ITPf8ufnmm+1HP/qRC0cnP0+ZMsX++Z//mZOgI9zBNA0BBBBAAAEEhgpknQDdcccd9te//tXe8573uFr0XK7Vq1e7k6C9OzkPrZ4hCCCAAAIIIIBA9ASyOgSmx1386le/shdffNE9p0th6NJ43aVZNypcu3atuyFi9MKjRQgggAACCCCAwFCBrBIg3fRQDzHbu3dvIgHyqtLTcPWgUgoCCCCAQHwFvBvO6q9+4Hrvw4yIe8qFqc28sjoEpqdSv//977e77rrLPZ3aY9PdoXUOkJ4HRkEAAQQQiK+AHnitH7v619PTk3jtDQv6b3V1tVVUVMQXkJbHTiCrPUCK6oc//KEtXLjQ3vrWt5pWFB0W080Qb7jhBvc4jNhFToMRQAABBAYI6I7/mzZtGjAsjDfa26SHbVMQCFMg6wRINz7Ug0x37txpegiqdpGed955ds4554TZXuaFAAIIIIAAAgjkLZB1AqQ5KemZPn26+5f3nKkAAQQQQAABBBAokEBW5wAVqG3MFgEEEEAAAQQQCESABCgQVipFAAEEEEAAgSgLkABFuXdoGwIIIIAAAggEIkACFAgrlSKAAAIIIIBAlAVIgKLcO7QNAQQQQAABBAIRIAEKhJVKEUAAAQQQQCDKAiRAUe4d2oYAAggggAACgQiQAAXCSqUIIIAAAgggEGUBEqAo9w5tQwABBBBAAIFABEiAAmGlUgQQQAABBBCIsgAJUJR7h7YhgAACCCCAQCACJECBsFIpAggggAACCERZIKeHoUY5ELWttrY26k1MtK+ioiJW7U00POlFWVmZxT0OPeC3srIy0RcVvT1JEb7x0ov1jSHBvlK7KAiUmoDfy71f663qSVX0nZNuXKrpcx02ePuU6+ejML2+I1Sqq6vd90WYbRpueWIrG2ZvMC8EEEAAAQQQiIRAUe0Bam9vjwRqNo2oq6uzOLU3VUwNDQ3W09MT6zi8X1heX3Sl2QPU19fnYk3lEMQwza+qqiqIqqkTgcgK9Pb2+raeaf3xa71VPamKthtB7gHS3mntQfG2T6naEPVhikGls7PTurq6Qm1uTU1NxvmxBygjDyMRQAABBBBAoBgFSICKsVeJCQEEEEAAAQQyCpAAZeRhJAIIIIAAAggUowAJUDH2KjEhgAACCCCAQEYBEqCMPIxEAAEEEEAAgWIUIAEqxl4lJgQQQAABBBDIKEAClJGHkQgggAACCCBQjAIkQMXYq8SEAAIIIIAAAhkFSIAy8jASAQQQQAABBIpRgASoGHuVmBBAAAEEEEAgowAJUEYeRiKAAAIIIIBAMQqQABVjrxITAggggAACCGQUIAHKyMNIBBBAAAEEEChGARKgYuxVYkIAAQQQQACBjAIkQBl5GIkAAggggAACxShAAlSMvUpMCCCAAAIIIJBRgAQoIw8jEUAAAQQQQKAYBUiAirFXiQkBBBBAAAEEMgqQAGXkYSQCCCCAAAIIFKMACVAx9ioxIYAAAggggEBGARKgjDyMRAABBBBAAIFiFCABKsZeJSYEEEAAAQQQyChAApSRh5EIIIAAAgggUIwCJEDF2KvEhAACCCCAAAIZBSozjmUkAggggAACIQlUVlZaebk/v8vLysqsoqIi65b39vZaT09P1tMzYfwFSIDi34dEgAACCMRaQImPSldXl3V3d/sSS0dHR9b11NbWumSJBChrsqKYsOAJ0PHjx+2FF14YgDl37lz3Xgvw9u3bTZn87NmzraqqasB0vEEAAQQQKB6BnTt32t69e0MPaNasWdbU1BT6fJlhYQUKngBt27bN7rvvPjv77LMTEkqATp06ZUuXLrUZM2bYvn37bP369bZq1SqXDCUm5AUCCCCAAAIIIDACgYInQK+++qpdc801dtNNNw1o/rp162zOnDm2fPlyN3zZsmW2detW8/YODZiYNwgggAACCCCAQA4CkUiAlNSsXbvWpk6dahdddJHby7Nr1y5bsGBBIhTtotyxY8eABOh3v/udvfbaa24anTh39dVXJ6aP+gudnFdfXx/1ZmZsn8x17D7OcejwanIMXb2pT4Is/7/pMoL4OFLtoiCAQHgC3rYg1RzTrY/a9qUbl6qeXIdpG6tTP+K8jfVOatd5VlE7jSUSCZAWCiVBDz30kGnPzz333GMHDhyw0aNHJ5YXvd6zZ0/ivV7osNimTZvcMH2J3XjjjQPGR/3NmDFjot7EYdunRK66unrY6aI+QU1NjWtiZ2/qEzC1EhdDnFHvB9qHQKEEtI6nu2rM+xIf3DZtw4NMgLz5edsn730c/zY0NITe7JMnT2acZ8EToDVr1tjYsWPdpY8LFy60a6+91iU6WhCTz8jXlQF1dXUDgtG5Q8ll//79yW8j/XrcuHF27NixSLdxuMbppEFdtdHS0jLcpJEdrw2bVszW1lbXxnR7gLr7L48dbmXyM0i1S7+YKAggEI6Avm86OztTziz5uyh5An3nBJkA6Ye9vvfa2tqSZxur14ph4sSJdvjwYfd9EWbjlThm2nvmzw0XRhiRFrbdu3cn7vugX9iTJk1ye3/05Xr06NFEzXo9efLkxHteIIAAAggggAACIxUoaAKk44G6suuZZ55x7X/ppZfsyJEjduGFF9q8efNs48aN1t7e7jLHLVu22MyZM0caJ59DAAEEEEAAAQQSAgU9BKZdh7rK68EHH3T/Dh06ZHfddZc7KXX+/Pm2efNmW7x4sdtDtGjRIpsyZUqi4bxAAAEEEEAAAQRGKlDQBEiNfte73mUPPPCAO49EJzp7x1N13HDlypXu2KeOgeo9BQEEEEAAAQQQ8EMgMllFuiuiGhsb/YiTOhBAAAEEEEAAgYRAQc8BSrSCFwgggAACCCCAQIgCJEAhYjMrBBBAAAEEEIiGAAlQNPqBViCAAAIIIIBAiAIkQCFiMysEEEAAAQQQiIZAZE6CjgYHrUAgtYDuzBzmozC8qyFTt4ahCCCAAAL5CpAA5SvI50tCoL39lLtDeVjB6pYQyc/CC2u+zAcBBBAoFQESoFLpaeLMS6C5ucV2HzicVx25fHjatGkkQLmAMS0CCCCQowDnAOUIxuQIIIAAAgggEH8BEqD49yERIIAAAggggECOAiRAOYIxOQIIIIAAAgjEX4AEKP59SAQIIIAAAgggkKMACVCOYEyOAAIIIIAAAvEXIAGKfx8SAQIIIIAAAgjkKEAClCMYkyOAAAIIIIBA/AVIgOLfh0SAAAIIIIAAAjkKkADlCMbkCCCAAAIIIBB/ARKg+PchESCAAAIIIIBAjgIkQDmCMTkCCCCAAAIIxF+AZ4HFvw+JAAEEEEAgD4GamhorLy+36urqlLVoXKoyZswYKysrSzUqp2G9vb3W1taW02eYOH8BEqD8DakBAQQQQCDGAnV1ddbX12ft7e0po+jp6Uk5/MiRI3knQLW1tTZx4kQ7ceKEKRGihCdAAhSeNXNCAAEEEIioQHNzs23dujVl68a+qcpqGyqGjLv11luHDMt1wJw5c+zLX/5yrh9jeh8EUu/X86FiqkAAAQQQQAABBKIqQAIU1Z6hXQgggAACCCAQmAAJUGC0VIwAAggggAACURUgAYpqz9AuBBBAAAEEEAhMoKhOgh43blxgUH5XXFVVZXFqb6r4Kyoq3BUQcY+jsrIy0Redvf1Xe+xOFS3DEEAAgeAEdEn94KJL7LWd1TYqrsW7TUBjY6O70i7MOLq7uzPOLr6qKcI6duxYiqHRHKSkIU7tTaXY1NRkXV1d1tLSkmp0LIbp/h4NDQ3W2trq2tulBIiCAAIIhCyg7ejgy+CV+OgS/TjfI0gx6DJ/xaDvizCL7u+UqXAILJMO4xBAAAEEEECgKAVIgIqyWwkKAQQQQAABBDIJkABl0mEcAggggAACCBSlAAlQUXYrQSGAAAIIIIBAJoGiOgk6U6CMi6eAroxI9yBCvyLiKjC/JKkHAQQQiI8ACVB8+qrkWqrLJ+vr62337t2m5/SEUbr7eBhhGM7MAwEEECi0AAlQoXuA+Q8r8NOf/tSefPLJYafzZYKKchv7+Q/7UhWVIIAAAghEV4BzgKLbN7QMAQQQQAABBAISIAEKCJZqEUAAAQQQQCC6AiRA0e0bWoYAAggggAACAQmQAAUES7UIIIAAAgggEF0BEqDo9g0tQwABBBBAAIGABEiAAoKlWgQQQAABBBCIrgAJUHT7hpYhgAACCCCAQEACJEABwVItAggggAACCERXgAQoun1DyxBAAAEEEEAgIAESoIBgqRYBBBBAAAEEoitAAhTdvqFlCCCAAAIIIBCQAAlQQLBUiwACCCCAAALRFSABim7f0DIEEEAAAQQQCEiABCggWKpFAAEEEEAAgegKkABFt29oGQIIIIAAAggEJEACFBAs1SKAAAIIIIBAdAVIgKLbN7QMAQQQQAABBAISIAEKCJZqEUAAAQQQQCC6ApXRbRoti4JAbW2tjRs3Lm1TqqqqrL6+Pu14RiCAAAIIpBd485vfbH19fTZp0qS0EzU0NKQd58eI5uZmO3XqlB9VxaoOEqBYdVf4ja2oqLDe3l679957Q5/5mDFjbMmSJaHPlxkigAACYQmMGjXKysrKbM2aNdbW1hbWbBPz+fu//3tTG/RjN4ii2JTgNTY2ur+D59HV1WXHjx8fPDiU95FOgDo6Omz79u1u4Zg9e7ZpbwMlfAElQBs3bgx9xqeffjoJUOjqzBABBAoh8MQTT9ihQ4dCn7USoAMHDtiePXtCn/eZZ55pEyZMCH2+3gwjmwBpd9zSpUttxowZtm/fPlu/fr2tWrXKJUNe48P8W15e7uu8lRVr70o2xcugs5nW72k0bwoCCCCAQPEKPPnkk/aTn/wk9AA//vGP29VXXx36fL0ZRjYBWrdunc2ZM8eWL1/u2rps2TLbunWrzZ0712t7aH+VqJx22mm+zy/bOnt6erJOlnxvZH+F3d3dQVRLnQgggAACCBRMoKz/2FxfweaeYcZf+MIXbMGCBXbppZe6qR544AF3CEx7hbzyla98xZ5++mn3trKy0h577DFvlO9/VX+hihKQQs5fcb/++uuhh6+9bhMnTrSWlhZrb28PZf5dvT326b88PmRejXUT7M3jpw4ZHtQAHe7VP8WtQ5BhF50PoMRbx+fDLt65CGH1eXJ8Ws+qq6sL6q7+7uzsTG5WKK8Vt+I/efJkKPNLnon2NNfV1bm4C/GDS8ucvgp12kWqsufITjvefmzIqNVvW5j3kQGdf6OTnA8fPuzWuSEzCXhAU1OTOwH6xIkTAc9paPU6L0jLXVBF/SnfdKVw3+rpWvR/w3VMcvTo0Ymp9HrwMUodHvMOI+lvuoU3UUkeL7RR8vNwkNqrL5hsiuarjUKhclXNf/z48UOaqhjUpiC/oGWkq8yCvNJMiZYXQ2dv/96uvwwJ1c6pa7T/N/mdQ0cEOCS5XcPNRsmSn8tILvMerm3Zjh+8fhVqec8ndn1W/0b6Je4ZFCp2zb9vbJ/brma7fcq2f4ebLh/3VHUrmdN67a3bqabxhg3nvvrEPns2RQKk7aL3Wa+ukfyVtS76SFX8dkk1j5qaGtO/IIp81Bfptk+KfaTry3DtHW4ZjmwCNDhBEJB+ISSXj3zkI8lvbf/+/QPeR/mNLi0/dmzoL4oot3lw2/TLQXsItIcmrkUbF/36am1tdSFoD1CqouRav9CiWLSu6HCq+qEQew78MtEvNbW/EHud/IpBy5LiiOqykm2cxbB90t5jrbfeup1t7Kmm6+hMvWdI/exHApRqnhqmxEHfe4W4Oixdm3IdrhjUF7rUPux1e7ikLrI3QtSX69GjRxPWej158uTEe14ggAACCCCAAAIjFYhsAjRv3jx36bXOA1CWvWXLFps5c+ZI4+RzCCCAAAIIIIBAQiCyh8Dmz59vmzdvtsWLF7tj6osWLbIpU6YkGs4LBBBAAAEEEEBgpAKRTYB03HDlypXu2KeOgeo9BQEEEEAAAQQQ8EMg8lmFLpOjIIAAAggggAACfgpE9hwgP4OkLgQQQAABBBBAIFmABChZg9cIIIAAAgggUBICJEAl0c0EiQACCCCAAALJAiRAyRq8RgABBBBAAIGSECABKoluJkgEEEAAAQQQSBaI/FVgyY0d7nWcLpXXM0ri1N5U9n/961/dM4PifKWebmOvZwV5fVHWV27njjp9SLhn1Y1PTDNkZIEHaFnasWOHe5ZQkA8WDDpMPf9Kjybx+iLo+QVRvx5vs3fvXpswYUIQ1YdWZzFsn/785z+7Bwrr8ST5lin9D0Puf+bFkGr0DL4gi/ccsDivE3r8hbZPY8eOdf0RpNfguvWYoEwlsk+Dz9RoxkVD4MMf/rC9853vtK997WvRaFCJtuIvf/mLXXHFFbZmzRq75JJLSlQhGmHfe++99vDDD9vWrVuj0aASboXWife85z22YsWKElYofOgvvviiXXfddfbTn/7UZs2aVfgGJbWAQ2BJGLxEAAEEEEAAgdIQIAEqjX4mSgQQQAABBBBIEqj4Un9Jes9LBLIW0PHV8847j2e0ZS0WzIQ6j2nUqFF28cUXu/OAgpkLtWYjoHXijDPO4MHN2WAFPI3On7ngggvszDPPDHhOVJ9JQNsnnSeq7ZMf52Nlmleu4zgHKFcxpkcAAQQQQACB2AtwCCz2XUgACCCAAAIIIJCrQFFdBp9r8Ew/coHjx4/b888/P+Cqo46ODtu+fXv/1aJlNnv27NAveRx5NPH/5GuvvWb79u1LBKLLsKdOnZp4z4vgBVj+gzfOZg7aNr3wwgsDJp07d+6A97wJXmDTpk12+eWXD7itxeHDh+3ZZ5+1s846y6ZNmxZ8I4aZAwnQMECMHirQ3t5uOnVMx9i9y65PnTplS5cutRkzZrgv4vXr19uqVatcMjS0Bob4LfCDH/zAXn/9dXevDdV9/vnnkwD5jZyhPpb/DDghj9q2bZvdd999dvbZZyfmTAKUoAjlxSOPPGKrV6+2Sy+9NJEAPffcc+6WBAsWLLDvfOc7tmTJEtOtVApZSIAKqR/DeevmYp/73OfcybZjxoxJRLBu3TqbM2eOLV++3A1btmyZuxcKG54EUaAvXn31VfvGN75hb33rWwOdD5WnFmD5T+1SiKFaF6655hq76aabCjH7kp5nd3e3S3KOHDkyxEEJ0d133+1OTL/++uvtlltusauuusoKefNWzgEa0k0MyCRw8uRJ+/znP2+LFy8eMNmuXbsG3ORKN7zS3T8pwQuoT44ePWqHDh1yN+Hbs2dP8DNlDgMEWP4HcBT0jRIgXW20du1a094g3WGcEo6A7iCuH73333//gBkqMdJ2SXumVSZNmmT19fXurukDJgz5DQlQyOBxn925557rLn0fHMeBAwds9OjRicF6nepXQGICXvgm8Kc//cl0/ok29toAaS/c448/7lv9VDS8AMv/8EZhTaEE6JlnnrGamhp76KGH7DOf+UxYsy75+chce98GP7rj4MGD7lYdOj/UKzqCoB9uhSwcAiukfsTnrUcs/Md//IdrpRKaTLuUdf8Tffl6RRl/XV2d95a/PgoM7pcbb7zRHn30URs3bpybi8590GMxtHuZEo4Ay384ztnMRcu+njulcxQXLlxo1157rdv78Ja3vCWbjzNNAAKD1w/NQt8RtbW1Acwt+yrZA5S9VclNqQf9KUvXv+EeeNrU1DQgm1dmP3ny5JIzCyPgwf3S3NxseginV3QekE6I1kNeKeEIsPyH4zzcXDo7O2337t0u+dG0Or9Eh1u0h45SOAFdlXrixAm3p9prRRS+I0iAvN7g7xAB/WL6u7/7O/dPD7PLVObNm2cbN240XSGmSx23bNnC3XAzgeUxbnC/tLS02B133GG6EknnO2iv3fve977El0Aes+KjWQqw/GcJFfBk+nGgq091CEzlpZdecofiL7zwwoDnTPWZBHRITBfJbNiwwU321FNPuT3W3l7rTJ8NchyHwILULaG658+fb5s3b3YnR2vX86JFi3hERkj9//a3v909bfnWW291u5V1uHLlypUhzZ3ZSIDlPxrLgc4x0TlwDz74oPunCwPuuuuuIeekRKO1pdWK2267ze688053uF7fEStWrCg4AI/CKHgXFFcD2tra3Lk/g0+CK64ooxmNDnnpJnDJJ6NHs6XF2yqW/+j0rfaMal1IPvE2Oq0r3ZbokL3O0YpCIQGKQi/QBgQQQAABBBAIVYBzgELlZmYIIIAAAgggEAUBEqAo9AJtQAABBBBAAIFQBUiAQuVmZggggAACCCAQBQESoCj0Am1AAAEEEEAAgVAFSIBC5WZmCCCAAAIIIBAFARKgKPQCbUAAgbwFWltbc6oj1+lzqpyJEUAg8gIkQJHvIhqIAALDCfzyl7+0K664YrjJEuNznT7xQV4ggEDRCJAAFU1XEggCpSvwwgsvuGcNZSuQ6/TZ1st0CCAQHwESoPj0FS1FoKQFnnvuObv55pvtb/7mb+zjH/+4/f73v3cev/vd7+yRRx5xT/zWcO/BsI899pgtXbrUTa9n2ulZdSrppncj+Q8BBEpGgASoZLqaQBGIr4Cebn/ZZZdZbW2tS370eINLLrnEXn75ZTvttNNMD4itr6+3iy++2D0B/P7777cbbrjB3va2t9lNN91kJ0+etCuvvNK2bduWcvr4ytByBBAYqQAPQx2pHJ9DAIHQBHTISknMl7/8ZZfA/O3f/q1Nnz7d+vr63F89aXrXrl0uOVKjDh48aN/+9rdND4hVUTI0ceJE95Tw22+/3T2ZOnl6NxH/IYBASQmQAJVUdxMsAvEUUIKjp95PnTrVFixYYB/84AdNh7UmTJiQMiAlSvv377d/+7d/c3uJnn/+eTt16pS1t7ennJ6BCCBQegIcAiu9PidiBGIn0NDQ4M75+drXvub2BN12223u8NYTTzyRMpbVq1fblClTTNNrb9BHP/pRt+co5cQMRACBkhQgASrJbidoBOIl8Morr5hOalbi8/jjj7ukRofA7rvvPheIzgnyivb0fPazn7Vvfetb7pwfJUPXXXed+0xvb++Q6b3P8RcBBEpLgASotPqbaBGIrcCSJUvsF7/4hfX09JhuYqirvXRYTGX8+PHukJcSJRUdGjtw4IAp4dG5Q//wD/9gHR0diUNgydN3d3e7z/AfAgiUlgAJUGn1N9EiEEuBd7zjHXbPPffYF77wBRszZow7vHXeeee59wro0ksvtYqKCps2bZrpfJ9vfvOb9rOf/cwd9jr99NNNh9AWLVpkupR+8PTbt293w/gPAQRKS6Cs/yqKvtIKmWgRQCDOAtqzM3bsWHdJ/OA4mpub3Thv+J49e0wJUGVl6us9Bk/vfY6/CCBQ/AIkQMXfx0SIAAIIIIAAAoMEOAQ2CIS3CCCAAAIIIFD8AiRAxd/HRIgAAggggAACgwRIgAaB8BYBBBBAAAEEil+ABKj4+5gIEUAAAQQQQGCQAAnQIBDeIoAAAggggEDxC5AAFX8fEyECCCCAAAIIDBIgARoEwlsEEEAAAQQQKH4BEqDi72MiRAABBBBAAIFBAv8fjKb/3UC9XkIAAAAASUVORK5CYII=" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1039,8 +1050,10 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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MWSoT8CCCCAAAIIZK0ACVDWNi2BIYAAAggggEAsARKgWDL0RwABBBBAAIGsFSABytqmJTAEEEAAAQQQiCVAAhRLhv4IIIBAkgLWBelJfo3REUAgAwJZdRdYBvyYJQIIZJGA3hGod60UFhZ2Kari4uJOf1/vlHH6bplOV5YvIuBhARIgDzceVUcAgdQK6MP/9CjO9u3b5eTJk6mdeAJTGzJkiLntOYFRGQUBBLooQALURUC+jgAC2Sdw9OhROXbsmOOBDRo0iATIcXVm6FcBrgHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VIAHya8sTNwIIIIAAAj4WIAHyceMTOgIIIIAAAn4VCDod+KpVq+Tyyy+XYPDTWZ84cUK2bNnSphqjR482n+vr62XTpk2Sk5MjI0eOlLy8vDbj8QEBBBBAAAEEEOiMgKMJ0PLly2XBggUybty4cAK0ceNGWbRokQwcODBcf02AamtrZcaMGTJ06FDZu3evLFu2TObPn2+SofCIdCCAAAIIIIAAAp0QcCQBampqkrlz58qRI0eiqvjRRx/JpEmTZOrUqW2GLV26VEaNGiWzZ882/WfOnCkbNmwQ6+hQm5H5gAACCCCAAAIIJCHgSAIUCoVM4nL11Veb01+R9dMESJOaJUuWyKBBg+TCCy80R3l27NghEyZMCI86YsQI2bZtW5sESI8offDBB2acQCAgd955Z3h8t3foKcDu3bu7vZpx65ebm2tOS3o9jvz8fNF/diWvtZ3ygu4+9aqniLXoMtXS0mIXhif66fJk/WtsbPREndNRSWu9Sse0E52mG+qQaF1jjafrhf4uJFNycu3Xn7y8/IxsrzUGr/9W6LKkpVu3btLc3JxMc3R5XM094hVHEqCCggJzlMeuIpoAadEk6OmnnxY98vPoo4/K/v372yxw+iO7e/fuNpPYunWrvPnmm6afLiTf//732wx38wddKNTFy0Vj0BU0G+KItaHMbd2AxhrmtrazNjRuq1ei9dFlSRM4/evnovFneplzQx1SsQwkG0dzi33CFAhkZnut9dd/Xl+3tS0zcQ2vXkccrziSAMWrwOLFi6W8vNw08MSJE+W6664ziY5uACKzNz2NVlRU1GZSelotsuzbty/yo6u7Kyoq5NixY66uY0eVq6ysFN1Tr66u7mhU1w7XDUtJSYm55syukroCNTfZ7xXajZ+JfrqB1HWjoaHB8T2sVMarOzG6h5jpH/9UxtSZael2T9syk0WPiGa6Dl2Nv7Cw0PyGJHM0sSlkf+Sxrq5ODh061NUqJf19XSd03a6pqUn6u275gsbQq1cvqaqqMr8XTtZLd871yFOsktHb4HUF27lzZzi71ZWuT58+5uiP/rgePXo0XG/t7tevX/gzHQgggAACCCCAQGcFMpoA6SExvbNr/fr1pv7vv/++uVB62LBhMnbsWFm5cqVo5n348GFZt26dDB8+vLNx8j0EEEDA9QK6TdSjktZ1aU7/zcRpCtc3ChXMWoGMngLTQ/d6l9eTTz5p/ukhxjlz5piLvsaPHy9r166VKVOmmA3C5MmTZcCAAVnbEASGAAII6OkCPTKuz0dzuuipluLiYsdPUzgdJ/NDwBJwPAGyLlq2KnDBBRfIE088Ya4j0QudNSnSohuCefPmmXOfumLqZwoCCCCQ7QK6I/jee+85HuaZZ54pQ4YMcXy+zBCBTAm4JqsoKyuzNSgtLbXtT08EEEAAAQQQQKCzAhm9BqizleZ7CCCAAAIIIIBAVwRIgLqix3cRQAABBBBAwJMCJECebDYqjQACCCCAAAJdESAB6ooe30UAAQQQQAABTwqQAHmy2ag0AggggAACCHRFgASoK3p8FwEEEEAAAQQ8KUAC5Mlmo9IIIIAAAggg0BUBEqCu6PFdBBBAAAEEEPCkAAmQJ5uNSiOAAAIIIIBAVwRc8yTorgTBdxFAILUC+lJM67U0qZ1y7Knp/AKBgJlvY2Nj7BEZggACCKRAgAQoBYhMAoFsE9BERF/ImYmXclZWVmYbJ/EggIALBUiAXNgoVAkBNwjs27dPPv74Y8erMmbMGNEjUBQEEEAgnQJcA5ROXaaNAAIIIIAAAq4UIAFyZbNQKQQQQAABBBBIpwAJUDp1mTYCCCCAAAIIuFKABMiVzUKlEEAAAQQQQCCdAiRA6dRl2ggggAACCCDgSgESIFc2C5VCAAEEEEAAgXQKkAClU5dpI4AAAggggIArBUiAXNksVAoBBBBAAAEE0ilAApROXaaNAAIIIIAAAq4UIAFyZbNQKQQQQAABBBBIpwAJUDp1mTYCCCCAAAIIuFKABMiVzUKlEEAAAQQQQCCdAiRA6dRl2ggggAACCCDgSoGseht8RUWFK5HtKqVvu/ZSfe1iCAQCkpOT4/k4gsGgFBQUiNRER5nf2k55wfzoAS7qo22gRZeplpYWF9WMqnhRID8/X3Jzc0X/ernoemFtoxKNIzdkP6ZuHzKxvbZi0G2UV4u1fSotLXV8+9TU1BSXzbuqNmEdO3bMpq87e+nK5KX62ilWVlZKY2OjVFdX2w32RD/d0JeUlEh9fb1tfRta42tp/jTBsB3BBT11A1NUVGTaorm5OSU1KiwsTMl0mIj3BBoaGkzyo3+9XHQZDoVCZr1INI6mUKPtqLp9yMT2WhMfXbdramz2zmxr6r6eGkOvXr1MDPp74WQxO7ZxZsgpsDg4DEIAAQQQQACB7BQgAcrOdiUqBBBAAAEEEIgjQAIUB4dBCCCAAAIIIJCdAiRA2dmuRIUAAggggAACcQRIgOLgMAgBBBBAAAEEslOABCg725WoEEAAAQQQQCCOAAlQHBwGIYAAAggggEB2CpAAZWe7EhUCCCCAAAIIxBEgAYqDwyAEEEAAAQQQyE4BEqDsbFeiQgABBBBAAIE4AiRAcXAYhAACCCCAAALZKUAClJ3tSlQIIIAAAgggEEeABCgODoMQQAABBBBAIDsFEk6ArrjiCjlx4kSUwlNPPSW33HJLVH96IIAAAggggAACbhUIxqvYhg0bZNWqVWaUf/7zn/LTn/5UCgsLw18JhULy17/+VQYNGhTuRwcCCCCAAAIIIOB2gbgJ0MCBA+Xuu++W+vp6aWxslJdeekkCgUA4pry8PDnzzDPlvvvuC/ejAwEEEEAAAQQQcLtA3ASoZ8+esnbtWhPDpEmTZMmSJVJSUuL2mKgfAggggAACCCAQVyBuAhT5zRUrVpgjQbt27TJ/I4dpUvS5z30ushfdCCCAAAIIIICAawUSToCWL18ut912m1RVVUUFc9NNN8mf//znqP70QAABBBBAAAEE3CiQcAI0a9Ys+drXvibf+ta3pEePHm1i4bRYGw4+IIAAAggggIDLBRJKgI4fPy4HDx6URx55RHr37u3ykKgeAggggAACCCAQXyCh5wB1795dBgwYIJs2bYo/NYYigAACCCCAAAIeEEjoCJDG8dBDD8ldd90lu3fvNslQMPjZV/Wo0NChQz0QLlVEAAEEEEAAAQREPstiOtDQa4Cqq6tl5syZUWNyEXQUCT0QQAABBBBAwMUCCSdABw4ckJaWFttQIh+OaDsCPRFAAAEEEEAAARcJJJwAHT16NGYCVFRUJBUVFS4Ki6oggAACCCCAAAKxBRJOgIYMGWJOgdlNKplTYPpuscsvv1wiryE6fPiwvP322+a1GoMHDw7PQl/BoRde5+TkyMiRI0VfvUFBAAEEEEAAAQS6KpDQXWA6E30Z6r/+9a/wvzfffFN+9atfyVlnnWVuj0+kIvowxQcffFD0JapW2bx5s0yfPl0+/PBDueeee+SFF14wg2pra2XatGmyevVqeeaZZ8ywWKfgrGnxFwEEEEAAAQQQSEQg4SNA55xzTtT0LrnkEjl16pT85Cc/kd/97ndRw60eTU1NMnfuXDly5IjVK/x3wYIF5g6z888/X26++WbztOlrrrlGli5dKqNGjZLZs2ebcfXia307/ejRo8PfpQMBBBBAAAEEEOiMQMJHgGJNXN8B9t5778UabPrrER9NXB577LE242lipLfVn3feeaZ/nz59pLi4WPbs2SM7duyQESNGhMfX7m3btoU/04EAAggggAACCHRWIOEjQG+99ZZowmIV7d6/f7/8+Mc/lssuu8zqbfu3oKBA9G3y7Ys+Xbpbt27mGh9rWFlZmegF1zptfQCjVbRbk6XIcuedd8rf//5300uvD+ooEYv8rhu6+/bt64ZqdKkOei2XXgTv9aKJt9RER1FYWCj5wYLoAS7ro6eHdT1LVeF0c6okvTcda322/novgs9qrNunyOtNPxti39XYFLAdUFRULJncXuvvpNdLz549HQ9BL6WJVxJOgCZMmGB7EbSeBps3b168ecQcprfPR14PpCNqYqU/Ou2Haf/2K+SNN94oF110kZl+bm6u6Cs7vFL0B1dPH3q56DvgtP06WsjcHKNeYK+JQ0NDg201mxobJaelywdKbacdr6fWS5N6vTausbUOThe96YHiTwFd3jRpiNzh9aKExqCJfPvfmHixNIXs17XGxoaM/L7o71p+fr7U1dXFq7arh2kMpaWlcvLkyaTawomgEk6Adu3aFXUbvGalyWTX7QPSjFBR9G4va+9Vj/7069dPKisrzZEg6zva//TTT7c+mr+XXnppm8/79u1r89nNHzRerydAmsTpRtLLcejKqcl2rI19U2uClyOfHfl0apnSemkC9N///tesH07N15oPCZAl4b+/ui7o8hdrnfCKiP42NTc3JxWHru92JVPbOY1Bd4a8vI3VGDQB0iTO6Z05K6+wa1Ptl/CurZ6C0mz62Weflf/93/+Vxx9/XF599dWYe86xZhjZX2H0QucVK1aY3mvWrDHPE9JnCo0dO1ZWrlxp0PQ2+XXr1snw4cMjv043AggggAACCCDQKYGEjwC9++67oqfB9InQetHyoUOHRI+4XHnllebWdT1t1Zmir9iwbn/XvQ69W0zL+PHjZe3atTJlyhSzNzJ58mTzDrLOzIPvIIAAAggggAACkQIJJ0B33HGHfPnLX5aFCxfKaaedZo4G6XOBrr/+evM8oHvvvTdyujG79flBkaV///7mlveqqiopLy8PD9KjQ3ptUU1Njbn2Rz9TEEAAAQQQQACBVAgkdApMzz/qE5n1ji9NfrToeUk9JXX33XfLa6+91uW6RCY/kRPTc4ckP5EidCOAAAIIIIBAVwUSSoD01JRe/2N3IZZexOz1i+W6isj3EUAAAQQQQMBbAgklQHp9z2WXXSZ6mmvjxo0mGdIruvUi5V//+tdyxRVXeCtqaosAAggggAACvhZIKAFSod/+9rfmAmh97o4+sblHjx5y9dVXy4UXXijf+973fI1I8AgggAACCCDgLYGEryweNGiQeWP7P/7xD/nggw/MwwqHDRsmY8aM8VbE1BYBBBBAAAEEfC+QcAKkUvpQIT3qoy8r1WJ3TZAZwH8IIIAAAggggICLBRI+BabP/Jk+fbr84Q9/MOHoxc8DBgyQX/7yl1wE7eIGpmoIIIAAAgggEC2QcAJ01113ySeffCIXX3yxmYq+l2vBggXmImjrSc7Rk6cPAggggAACCCDgPoGEToHpiyJfeeUV2bp1q3lPl4aht8brU5r1QYVLliwxD0R0X3jUCAEEEEAAAQQQiBZIKAHShx7qS8z27NkTToCsSekb2PVFpRQEEEAAAe8KWA+c1b+6g2t9djIininnpDbzSugUmL6V+itf+YrMmTPHvJ3aYtOnQ+s1QPo+MAoCCCCAgHcF9IXXurOr/0Ktb0W3up36m5+fL4FAwLuA1NxzAgkdAdKonnrqKZk4caKcccYZoiuKnhbThyHecsst5nUYnoucCiOAAAIItBHQJ/6vWrWqTT8nPujRJn3ZNgUBJwUSToD0wYf6ItPt27eLvgRVD5Gee+65cvbZZztZX+aFAAIIIIAAAgh0WSDhBEjnpEnPkCFDzL8uz5kJIIAAAggggAACGRJI6BqgDNWN2SKAAAIIIIAAAmkRIAFKCysTRQABBBBAAAE3C5AAubl1qBsCCCCAAAIIpEWABCgtrEwUAQQQQAABBNwsQALk5tahbggggAACCCCQFgESoLSwMlEEEEAAAQQQcLMACZCbW4e6IYAAAggggEBaBEiA0sLKRBFAAAEEEEDAzQIkQG5uHeqGAAIIIIAAAmkRIAFKCysTRQABBBBAAAE3C5AAubl1qBsCCCCAAAIIpEWABCgtrEwUAQQQQAABBNwskNTLUN0ciNatsLDQ7VUM1y8QCHiqvuGKR3Tk5OSI1+PQF/wGg0EJBAMRkX3WGWgdrjE6XbReFAT8JpDq5d7aRiXq2CIh21EztZ2ztk9e+m1rD2htP/Pz8x3flna0PLGVbd9afEYAAQQQQACBrBfIqiNAdXV1nmmwoqIi8VJ97WBLSkokFAp5Og5rDyvUZL/nF2pultzWGJ0uLS0tkpeX5/RsmR8CGRVobl3fdJuSiqLrj65HyUwvFGq2nXWmtnPm6HTrEWgv/1ZoDFoaGhqksbHR1jddPQsKCuJOmiNAcXkYiAACCCCAAALZKEAClI2tSkwIIIAAAgggEFeABCguDwMRQAABBBBAIBsFSICysVWJCQEEEEAAAQTiCpAAxeVhIAIIIIAAAghkowAJUDa2KjEhgAACCCCAQFwBEqC4PAxEAAEEEEAAgWwUIAHKxlYlJgQQQAABBBCIK0ACFJeHgQgggAACCCCQjQIkQNnYqsSEAAIIIIAAAnEFSIDi8jAQAQQQQAABBLJRgAQoG1uVmBBAAAEEEEAgrgAJUFweBiKAAAIIIIBANgqQAGVjqxITAggggAACCMQVIAGKy8NABBBAAAEEEMhGARKgbGxVYkIAAQQQQACBuAIkQHF5GIgAAggggAAC2ShAApSNrUpMCCCAAAIIIBBXgAQoLg8DEUAAAQQQQCAbBUiAsrFViQkBBBBAAAEE4gqQAMXlYSACCCCAAAIIZKMACVA2tioxIYAAAggggEBcARKguDwMRAABBBBAAIFsFCABysZWJSYEEEAAAQQQiCsQjDuUgQgggAACCDgkEAwGJTc3NfvlOTk5EggEEq55c3OzNIUaEx6fEb0vQALk/TYkAgQQQMDTApr4aGlsbJSmpqaUxFJfX5/wdAoLC5NKlhKeMCO6WiDjCdCJEydky5YtbZBGjx5tPusCvGnTJtFMfuTIkZKXl9dmPD4ggAACCGSPwPbt22XPnj2OBzRixAiprKx0fL7MMLMCGU+ANm7cKIsWLZKBAweGJTQBqq2tlRkzZsjQoUNl7969smzZMpk/f75JhsIj0oEAAggggAACCHRCIOMJ0EcffSSTJk2SqVOntqn+0qVLZdSoUTJ79mzTf+bMmbJhwwaxjg61GZkPCCCAAAIIIIBAEgKuSIA0qVmyZIkMGjRILrzwQnOUZ8eOHTJhwoRwKHqIctu2bW0SoDfeeEN27dplxtEL56699trw+G7v0IvziouL3V7NuPVTcz137+U49PSqxmBdg9A+4GBrO8Ua1n7cVH7WelEQQMA5gU+3BfYXTWdqO6fbWL30w8vbWOuidr3Oym2XsbgiAdJFXJOgp59+WvTIz6OPPir79++X7t27h5d+7d69e3f4s3boabFVq1aZfrqA3nrrrW2Gu/1DWVmZ26vYYf00kcvPz+9wPLePkF9gH0Nea2z5Qfthbo+J+iGAQOICnyYb9uu6buMyub0uKChIPBCXjllSUuJ4zU6dOhV3nhlPgBYvXizl5eXm1seJEyfKddddZxId/WENhULhyuudAUVFReHP2qHXDkWWffv2RX50dXdFRYUcO3bM1XXsqHJ60aDetVFdXd3RqK4drhs9XTFPnbRfUfRatKbAZ8uhU4FovXSPiYIAAs4I6O+Nru92RX9IM/H7ojv2+rtXU1NjVy1P9NMYevXqJYcPHza/F05WWhPHeEfPUvPAhU5G1NDQIDt37gw/90Gz7D59+pijP/rjevTo0fCUtbtfv37hz3QggAACCCCAAAKdFchoAqTnA/XOrvXr15v6v//++3LkyBEZNmyYjB07VlauXCl1dXUmc1y3bp0MHz68s3HyPQQQQAABBBBAICyQ0VNgetGZ3uX15JNPmn+HDh2SOXPmmItOx48fL2vXrpUpU6aYI0STJ0+WAQMGhCtOBwIIIIAAAggg0FmBjCZAWukLLrhAnnjiCXMdiV7obN39oucN582bZ8596jlQ/UxBAAEEEEAAAQRSIeCarCLWFfalpaWpiJNpIIAAAggggAACYYGMXgMUrgUdCCCAAAIIIICAgwIkQA5iMysEEEAAAQQQcIcACZA72oFaIIAAAggggICDAiRADmIzKwQQQAABBBBwh4BrLoJ2Bwe1QKCtQH7rs6ryMvAqDOtuyLa14RMCCCCAQKoESIBSJcl0slJAX8Fy5LDzryzRR0JEvgsvK3EJCgEEEMigAAlQBvGZtfsFTra+A+jdd991vKKDBw8mAXJcnRkigICfBLgGyE+tTawIIIAAAgggYARIgFgQEEAAAQQQQMB3AiRAvmtyAkYAAQQQQAABEiCWAQQQQAABBBDwnQAJkO+anIARQAABBBBAgASIZQABBBBAAAEEfCdAAuS7JidgBBBAAAEEECABYhlAAAEEEEAAAd8JkAD5rskJGAEEEEAAAQRIgFgGEEAAAQQQQMB3AiRAvmtyAkYAAQQQQAAB3gXGMoAAAggg4GuBgoICyc3Nlfz8PFuH/Px8KSsrsx2Wip7Nzc1SU1OTikkxjSQESICSwGJUBBBAAIHsEygqKpKWlhapq6u3Da6urk6OHDliO6yrPQsLC6VXr15y8uRJ0USI4pwACZBz1swJAQQQQMClAlVVVbJx0wbp/YXCqBquWbNGXnnpV1H9U9Fj1KhR8sADD6RiUkwjSQGuAUoSjNERQAABBBBAwPsCJEDeb0MiQAABBBBAAIEkBUiAkgRjdAQQQAABBBDwvgAJkPfbkAgQQAABBBBAIEmBrLoIuqKiIsnwMzd6Xl6eeKm+dlKBQEBycnI8H0cwGBS9DVa4C9WumemHAAIOCNjdZq/bV93O6jbKq0Vj0FJaWmrutHMyjqampriz866qTVjHjh2z6evOXpr8eKm+doqVlZXS2Ngo1dXVdoM90U+f/VFSUiL19fa3v3oiCCqJAAKeF9DtaPvb4DXx0Vv0vfyMII1Bb/PXGPT3wslidmzjzJBTYHFwGIQAAggggAAC2SlAApSd7UpUCCCAAAIIIBBHgAQoDg6DEEAAAQQQQCA7BUiAsrNdiQoBBBBAAAEE4ghk1UXQceJkkEcF9M4IvVA5nYW7wNKpy7QRQAABdwqQALmzXahVq4DePllcXCw7d+4UfU9POktVfXqnn866M20EEEAAgeQFSICSN+MbDgs899xz8vrrr6d1rkVXDZOCkV9M6zyYOAIIIICAewTSe27BPXFSEwQQQAABBBBAICxAAhSmoAMBBBBAAAEE/CJAAuSXliZOBBBAAAEEEAgLkACFKehAAAEEEEAAAb8IkAD5paWJEwEEEEAAAQTCAiRAYQo6EEAAAQQQQMAvAiRAfmlp4kQAAQQQQACBsAAJUJiCDgQQQAABBBDwiwAJkF9amjgRQAABBBBAICxAAhSmoAMBBBBAAAEE/CJAAuSXliZOBBBAAAEEEAgLkACFKehAAAEEEEAAAb8IkAD5paWJEwEEEEAAAQTCAiRAYQo6EEAAAQQQQMAvAiRAfmlp4kQAAQQQQACBsAAJUJiCDgQQQAABBBDwiwAJkF9amjgRQAABBBBAICxAAhSmoAMBBBBAAAEE/CIQ9EugxNk5gcLCQqmoqIj55by8PCkuLo45nAEIIIAAArEFPv/5z0tLS4v06dMn5kglJSUxh6ViQFVVldTW1qZiUp6aBgmQp5rL+coGAgFpbm6WhQsXOj7zsrIymTZtmuPzZYYIIICAUwLdunWTnJwcWbx4sdTU1Dg12/B8vv3tb4vWQXd201E0Nk3wSktLzd/282hsbJQTJ0607+3IZ1cnQPX19bJp0yazcIwcOVL0aAPFeQFNgFauXOn4jPv27UsC5Lg6M0QAgUwIrF69Wg4dOuT4rDUB2r9/v+zevdvxeffv31969uzp+HytGbo2AdLDcTNmzJChQ4fK3r17ZdmyZTJ//nyTDFmVd/Jvbm5uSuetWbEeXUmkWBl0IuOmehydNwUBBBBAIHsFXn/9dXn22WcdD/D222+Xa6+91vH5WjN0bQK0dOlSGTVqlMyePdvUdebMmbJhwwYZPXq0VXfH/mqi0rt375TPL9FphkKhhJOllFeydYJNTU3pmCzTRAABBBBAIGMCOa3n5loyNvc4M77//vtlwoQJMm7cODPWE088YU6B6VEhqzz44IPy5ptvmo/BYFBefPFFa1DK/+r0M1U0Acnk/DXuAwcOOB6+HnXr1auXVFdXS11dXVrnv+zQu/LP459EzePsM0ZLcyiqd9p76Ole/adx6ylIp4teD6CJt56fd7pY1yKku83t4tL1LD8/P6Pu2t4NDQ121UtrP41b4z916lRa52M3cT3SXFRUZOLOxA6XLnP6U3jy1AnZsf/tqCpe3L2/fKPX+VH9U9FDr7/Ri5wPHz5s1rlUTDOZaVRWVpoLoE+ePJnM11Iyrl4XpMtduopeRqO+sUrmftVj1ej/+us5ye7du4fH0u725yj19Jh1Gkn/arDpKrpRSuXpIK2v/sAkUnS+ulHIVK6q8+/Ro0dUVTUGrVM6f6DVSO8yS+edZppoFdQUiByPClH+p8cZUpGXmbvctF6J2mqylMplJJl5R6t1rk/79StTy3tXYtfv6r/O/ohbBpmKXeffUt5itquJbp8619rR3+qKe/TUxCRzuv4ksg5Z7lUNp+T/2SRABQUFtttAu/l2pp9a600fdiXVLnbz0Pj0XzqK2mpiHWv7pLF3dn3pqL4dLcOuTYDaJwgKpHsIkeWGG26I/Cj79u1r89nNH/TW8mPHjrm5ih3WTfcc9AiBHqHxatGNi+591dXaH2E6cuSIhILO7xEn46nrip5O1XbIxJGDZOoab1zdU9P6Z+KoU7x6JTNMlyWNQ/fmvVyyYfukR491p/j4cZs9mxiNUxOy34nWo5GZaFNNHPR3LxN3h8UgSrq3xqBtobfaO71ud5TUufZBiPrjevTo0TC2dvfr1y/8mQ4EEEAAAQQQQKCzAq5NgMaOHWtuvbYy73Xr1snw4cM7GyffQwABBBBAAAEEwgKuPQU2fvx4Wbt2rUyZMsWcU588ebIMGDAgXHE6EEAAAQQQQACBzgq4NgHS84bz5s0z5z71HKh+piCAAAIIIIAAAqkQcH1WobfJURBAAAEEEEAAgVQKuPYaoFQGybQQQAABBBBAAIFIARKgSA26EUAAAQQQQMAXAiRAvmhmgkQAAQQQQACBSAESoEgNuhFAAAEEEEDAFwIkQL5oZoJEAAEEEEAAgUgB198FFlnZjrq9dKu8vqPES/W1s//kk0/MO4O8fKeevqdG3xV0WlG5nNMc/Rj8orwCCQbcvZrosrRt2zbzLqF0vljQbhlIZT99/5W+msTL64W+3mbPnj3Ss2fPVNI4Pq1s2D79+9//Ni8U1teTJFoKc1rknG59o0Y/rbA8I8ul9R4wL68T+voL3T6Vl5eb9ojCTWMPfU1QvOLat8HHqzTD3CHw9a9/Xb70pS/Jww8/7I4K+bQW//nPf+TKK6+UxYsXy5gxY3yq4I6wFy5cKM8884xs2LDBHRXycS10nbj44otl7ty5PlbIfOhbt26V66+/Xp577jkZMWJE5isUUQNOgUVg0IkAAggggAAC/hAgAfJHOxMlAggggAACCEQIBH7UWiI+04lAwgJ6fvXcc8/lHW0Ji6VnRL2OqVu3bnLRRReZ64DSMxemmoiArhOnn346L25OBCvN4+j1M+eff770798/zXNi8vEEdPuk14nq9imZ67HiTTNVw7gGKFWSTAcBBBBAAAEEPCPAKTDPNBUVRQABBBBAAIFUCbj7/t5URcl0Ui5w4sQJeeedd9rcdVRfXy+bNm0SPeQ5cuRIx295THmQHprgrl27ZO/eveEa623YgwYNCn+mI/0CLP/pN05kDrpt2rJlS5tRR48e3eYzH9IvsGrVKrn88svbPD7g8OHD8vbbb8uZZ54pgwcPTn8lOpgDCVAHQAyOFqirqxO9dEzPsVu3XdfW1sqMGTNk6NCh5od42bJlMn/+fJMMRU+BPqkW+P3vfy8HDhwwz9rQaZ933nkkQKlGjjM9lv84OA4P2rhxoyxatEgGDhwYnjMJUJjCkY7ly5fLggULZNy4ceEEaPPmzeaRBBMmTJDf/OY3Mm3aNNFHqWSykABlUt+D89aHi913333mYtuysrJwBEuXLpVRo0bJ7NmzTb+ZM2eaZ6Gw4QkTpbXjo48+kkceeUTOOOOMtM6HidsLsPzbu2Sir64LkyZNkqlTp2Zi9r6eZ1NTk0lyjhw5EuWgCdFDDz1kLky/+eab5bbbbpNrrrlGMvnwVq4BimomesQTOHXqlPzwhz+UKVOmtBltx44dbR5ypQ+80qd/UtIvoG1y9OhROXTokHkI3+7du9M/U+bQRoDlvw1HRj9oAqR3Gy1ZskT0aJA+YZzijIA+QVx3eh977LE2M9TESLdLemRaS58+faS4uNg8Nb3NiA5/IAFyGNzrszvnnHPMre/t49i/f79079493Fu77fYCwiPQkTKBjz/+WPT6E93Y6wZIj8K99NJLKZs+E+pYgOW/YyOnxtAEaP369VJQUCBPP/20/OAHP3Bq1r6fj5rr0bf2r+44ePCgeVSHXh9qFT2DoDtumSycAsukvsvnra9Y+Nvf/mZqqQlNvEPK+vwT/fG1imb8RUVF1kf+plCgfbvceuut8sILL0hFRYWZi177oK/F0MPLFGcEWP6dcU5kLrrs63un9BrFiRMnynXXXWeOPpx22mmJfJ1x0iDQfv3QWehvRGFhYRrmlvgkOQKUuJXvxszLywtf69PRC08rKyvbZPOa2ffr1893Zk4E3L5dqqqqRF/CaRW9DkgviNaXvFKcEWD5d8a5o7k0NDTIzp07TfKj4+r1JXq6RY/QUTInoHelnjx50hyptmrhht8IEiCrNfgbJaB7TN/85jfNP32ZXbwyduxYWblypegdYnqr47p163gabjywLgxr3y7V1dVy1113id6JpNc76FG7Sy+9NPwj0IVZ8dUEBVj+E4RK82i6c6B3n+opMC3vv/++ORU/bNiwNM+ZyccT0FNiepPMihUrzGhr1qwxR6yto9bxvpvOYZwCS6euj6Y9fvx4Wbt2rbk4Wg89T548mVdkONT+X/ziF83blu+44w5zWFlPV86bN8+huTMbFWD5d8dyoNeY6DVwTz75pPmnNwbMmTMn6poUd9TWX7WYNWuW3HPPPeZ0vf5GzJ07N+MAvAoj402QXRWoqakx1/60vwguu6J0ZzR6yksfAhd5Mbo7a5q9tWL5d0/b6pFRXRciL7x1T+38WxM9Za/XaLmhkAC5oRWoAwIIIIAAAgg4KsA1QI5yMzMEEEAAAQQQcIMACZAbWoE6IIAAAggggICjAiRAjnIzMwQQQAABBBBwgwAJkBtagToggAACCCCAgKMCJECOcjMzBBBAAAEEEHCDAAmQG1qBOiCAQJcFjh8/ntQ0kh0/qYkzMgIIuF6ABMj1TUQFEUCgI4GXX35Zrrzyyo5GCw9PdvzwF+lAAIGsESABypqmJBAE/CuwZcsW866hRAWSHT/R6TIeAgh4R4AEyDttRU0R8LXA5s2bZfr06fLVr35Vbr/9dnnrrbeMxxtvvCHLly83b/zW/taLYV988UWZMWOGGV/faafvqtMSa3wzkP8QQMA3AiRAvmlqAkXAuwL6dvvLLrtMCgsLTfKjrzcYM2aMfPDBB9K7d2/RF8QWFxfLRRddZN4A/thjj8ktt9wiX/jCF2Tq1Kly6tQpufrqq2Xjxo2243tXhpojgEBnBXgZamfl+B4CCDgmoKesNIl54IEHTALzjW98Q4YMGSItLS3mr75peseOHSY50kodPHhQfvGLX4i+IFaLJkO9evUybwm/8847zZupI8c3I/EfAgj4SoAEyFfNTbAIeFNAExx96/2gQYNkwoQJctVVV4me1urZs6dtQJoo7du3T/7yl7+Yo0TvvPOO1NbWSl1dne349EQAAf8JcArMf21OxAh4TqCkpMRc8/Pwww+bI0GzZs0yp7dWr15tG8uCBQtkwIABouPr0aAbb7zRHDmyHZmeCCDgSwESIF82O0Ej4C2BDz/8UPSiZk18XnrpJZPU6CmwRYsWmUD0miCr6JGee++9V37+85+ba340Gbr++uvNd5qbm6PGt77HXwQQ8JcACZC/2ptoEfCswLRp0+T555+XUCgk+hBDvdtLT4tp6dGjhznlpYmSFj01tn//ftGER68d+s53viP19fXhU2CR4zc1NZnv8B8CCPhLgATIX+1NtAh4UuCss86SRx99VO6//34pKyszp7fOPfdc81kDGjdunAQCARk8eLDo9T4/+9nP5E9/+pM57dW3b1/RU2iTJ08WvZW+/fibNm0y/fgPAQT8JZDTehdFi79CJloEEPCygB7ZKS8vN7fEt4+jqqrKDLP67969WzQBCgbt7/doP771Pf4igED2C5AAZX8bEyECCCCAAAIItBPgFFg7ED4igAACCCCAQPYLkABlfxsTIQIIIIAAAgi0EyABagfCRwQQQAABBBDIfgESoOxvYyJEAAEEEEAAgXYCJEDtQPiIAAIIIIAAAtkvQAKU/W1MhAgggAACCCDQToAEqB0IHxFAAAEEEEAg+wVIgLK/jYkQAQQQQAABBNoJ/H80mdapzG6jCwAAAABJRU5ErkJggg==" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="one-numerical-variable-one-categorical-2-levels-t-1" class="section level3">
 <h3>One numerical variable, one categorical (2 levels) (t)</h3>
@@ -1067,9 +1080,6 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1086,12 +1096,11 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1108,8 +1117,10 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-diff-in-proportions" class="section level3">
 <h3>Two categorical variables (diff in proportions)</h3>
@@ -1136,9 +1147,6 @@ <h3>Two categorical variables (diff in proportions)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1155,12 +1163,11 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1177,8 +1184,10 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-z" class="section level3">
 <h3>Two categorical variables (z)</h3>
@@ -1205,9 +1214,6 @@ <h3>Two categorical variables (z)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1224,12 +1230,11 @@ <h3>Two categorical variables (z)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> z_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1246,8 +1251,10 @@ <h3>Two categorical variables (z)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---slr-1" class="section level3">
 <h3>Two numerical vars - SLR</h3>
@@ -1274,9 +1281,6 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1293,12 +1297,11 @@ <h3>Two numerical vars - SLR</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> slope_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1315,8 +1318,10 @@ <h3>Two numerical vars - SLR</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>) </code></pre></div>
-<p><img 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BTSiiPbCCCAAAIIIOBegADIvR1TIoAAAggggEBIBQiAQlpxZBsBBBBAAAEE3AsQALm3Y0oEEEAAAQQQCKkAAVBIK45sI4AAAggggIB7AQIg93ZMiQACCCCAAAIhFSAACmnFkW0EEEAAAQQQcC9AAOTejikRQAABBBBAIKQCBEAhrTiyjQACCCCAAALuBQiA3NsxJQIIIIAAAgiEVKDY63wvX75czjvvPCku/u+iDx06JK+99lqvbEyZMsV0t7W1yYYNG6SgoEAmTZokJSUlvcajAwEEEEAAAQQQcCPgaQC0dOlSWbBggZxzzjmJAGj9+vWycOFCGTNmTCL/GgC1tLTIddddJ+PGjZOdO3fKE088IfPnzzfBUGJEviCAAAIIIIAAAi4EPAmAOjs75fbbb5f9+/f3yeJbb70ll112mVxzzTW9hi1ZskQmT54sc+bMMf1nz54ta9euFevsUK+R6UAAAQQQQAABBBwIeBIAdXV1mcDl4osvNpe/kvOnAZAGNYsXL5axY8fKmWeeac7ybN26VaZPn54YdcKECbJ58+ZeAdBzzz0n27ZtM+MUFhb2CaISE/vwxbpcV1VV5cPSP1hkRWH7Bx1J39TLugyZ1Nv113zPz3VG8jihlonkn0A+108npQjrulxUXCRO9jdFRUWif9rEIOrJ2pYHDBggZWVlUS+ulJaWmrrVY2/Uk5ZV1+F0635PT0/W4nsSAOkKp2d50iUNgDRpEPTYY4+Jnvm59957Zffu3VJTU5OYRL9v37490a1fXnzxRVmxYoXppwHH9ddf32u4nx3WTqWiosLPbEh5d3Pa5ed7J6/l9euAlbaAeehp1WEeZsUsHAr4uT5pUGAdMB1m29fRNd9O9jfW+h217TZbJZSXl2cbHJlhWrd6TMwVAEShwNZ6nG7db21tzVpETwKgbDl49NFHZeDAgWaHc+mll8rll19uAh3dmJOjV72MptF7cvrhD3+Y3Cm7du3q1e1nR2VlpVRXV8vevXv9zIbUtdWnXb565lo50k6YoadG4e3t6c82ZZgk8L21TCR/BHTHrTdBeJ10H9PR0SG6fYQttbe1O9rf6AFS1/GmpqawFdVxfrWsQ4cOlQMHDpj6dTyDkE2gZ0N0+9F1OepJj7Va3nTHWj35osMzJV/P8esB891330382tKNcfjw4ebsj66sdXV1iXzr95EjRya6+YIAAggggAACCLgV8DUA0qhc7+xas2aNyf/rr79uGkqfccYZMnXqVFm2bJk5S7Fv3z5ZvXq1jB8/3m05mQ4BBBBAAAEEEEgI+HoJTK/d6V1eixYtMn/vv/++zJ0717QlmTZtmqxatUpmzpxpzhDNmDFDRo8encg4XxBAAAEEEEAAAbcCngdAK1eu7JXXiRMnysMPPywHDx40jZ6tBk3aMG/evHnS2Nho2v7EqaFeLyA6EEAAAQQQQCDvAp4HQJlKUFtbm3aQNiQmIYAAAggggAAC+RTwtQ1QPgvCvBBAAAEEEEAAAbsCBEB2pRgPAQQQQAABBCIjQAAUmaqkIAgggAACCCBgV4AAyK4U4yGAAAIIIIBAZAQIgCJTlRQEAQQQQAABBOwKBOYuMLsZZrx4CehjEaxHI3hdcr+W63U5WR4CCCAQRwECoDjWeojKrC8vJBAJUYWRVQQQQCAkAgRAIamouGZTgx99RUq6F931t8lZZ53V34tg/ggggAACPgkQAPkEz2LtC+hbjf14Y3V3d7f9TDImAggggECoBGgEHarqIrMIIIAAAgggkA8BAqB8KDIPBBBAAAEEEAiVAAFQqKqLzCKAAAIIIIBAPgQIgPKhyDwQQAABBBBAIFQCBEChqi4yiwACCCCAAAL5ECAAyoci80AAAQQQQACBUAkQAIWqusgsAggggAACCORDgAAoH4rMAwEEEEAAAQRCJUAAFKrqIrMIIIAAAgggkA8BAqB8KDIPBBBAAAEEEAiVAAFQqKqLzCKAAAIIIIBAPgQIgPKhyDwQQAABBBBAIFQCBEChqi4yiwACCCCAAAL5ECAAyoci80AAAQQQQACBUAkQAIWqusgsAggggAACCORDgAAoH4rMAwEEEEAAAQRCJVAcqtzmyGxBQUGOMbwbbOXF+vRuyb2X5NXyvVpO79LRhQACCYHDuz8n26GOa/0l5hHRL8kuyd8jWlxTrLjVrZt6jVQAVFtbG5j1ubj4v7R+56mquTOtSVFRkZSWlqYd5qanrnz5nJ+bPDBNdAT8XJ902y0sDN/J8eLiEnGyv9Ey6p+1r4rO2tO3JNbBsaqqSnp6evqOELE+WqclJSWxKavWb7p1v7Mz/fHPqu5IBUD19fVWuXz/rKyslOrqavE7T41tjWkturq6pL29Pe0wNz01+Mnn/Kw8xGHnbJWVzw8E9CDVH+vTB0tI/23AgAGiO81cO870U/vbt7Ojw9H+Rg+Qut02NTX5m3EPlq5lLS8vl0OHDknHYaeoJw302traYlFWPdZqedMda8vKyrJWdfh+5mQtDgMRQAABBBBAAIHcAgRAuY0YAwEEEEAAAQQiJkAAFLEKpTgIIIAAAgggkFuAACi3EWMggAACCCCAQMQECIAiVqEUBwEEEEAAAQRyCxAA5TZiDAQQQAABBBCImAABUMQqlOIggAACCCCAQG4BAqDcRoyBAAIIIIAAAhETIACKWIVSHAQQQAABBBDILUAAlNuIMRBAAAEEEEAgYgIEQBGrUIqDAAIIIIAAArkFCIByGzEGAggggAACCERMgAAoYhVKcRBAAAEEEEAgtwABUG4jxkAAAQQQQACBiAkQAEWsQikOAggggAACCOQWIADKbcQYCCCAAAIIIBAxAQKgiFUoxUEAAQQQQACB3AK2A6Dzzz9fDh061GeOv/rVr+Tqq6/u058eCCCAAAIIIIBAUAWKs2Vs7dq1snz5cjPKiy++KHfddZeUl5cnJunq6pKnn35axo4dm+jHFwQQQAABBBBAIOgCWQOgMWPGyE033SRtbW3S0dEhzz33nBQVFSXKVFJSIscdd5zceuutiX58QQABBBBAAAEEgi6QNQAaMmSIrFq1ypThsssuk8WLF0tVVVXQy0T+EEAAAQQQQACBrAJZA6DkKZ955hlzJui9994zn8nDNCg6+uijk3vxHQEEEEAAAQQQCKyA7QBo6dKl8pWvfEXq6+v7FOZzn/uc/P73v+/Tnx4IIIAAAggggEAQBWwHQDfccINcccUVcv3118vgwYN7lYXLYr046EAAAQQQQACBgAvYCoAaGhpk7969cvfdd8uwYcMCXiSyhwACCCCAAAIIZBew9RygmpoaGT16tGzYsCH73BiKAAIIIIAAAgiEQMDWGSAtx5133ik33nijbN++3QRDxcUfTKpnhcaNGxeC4pJFBBBAAAEEEEBA5IMoJoeGtgE6ePCgzJ49u8+YThpB64MVzzvvPEkOoPbt2ycvvfSSeabQiSeemJi/Pn9IzzoVFBTIpEmTRJ87REIAAQQQQAABBI5UwNYlMF3Inj17pKWlJe3fb3/7W1v50DvJ7rjjDtEnSFvplVdekVmzZsmbb74pN998szz11FNmkC7r2muvlRUrVsjjjz9uhvX09FiT8YkAAggggAACCLgWsH0GqK6uTjIFIAMGDJBBgwZlzERnZ6fcfvvtsn///j7jLFiwwFxeO/300+Wqq64yt9pfcsklsmTJEpk8ebLMmTPHTKNnnvTVHFOmTOkzD3oggAACCCCAAAJOBGwHQCeddJK5BJZu5rkugekZHw1cLr74YnP5y5qHBkbapui0004zvYYPHy4VFRWyY8cO2bp1q0yfPt0aVSZMmCCbN2/uFQBt2rRJ9PKZJr1MdsoppyTG9/uLdYmvrKzM16yUdpemXb56FRbaPgGYdh7JPfM9P523zpMUX4F8rp9OFHW5fi3bST5Tx9U8O9nf6GuNdD/lZJrUZYal23qFkzajCGPdOnXW8salrLoO67Ei3Xqcq65tB0D6MtTkS1eNjY3y8ssvy4MPPmhuj89WQZoxfZVGatJb6ysrK3sd6Gpra0XPNu3evVv07jMr6XcNlpLTQw89lHhZqyJoQBS0lPrMJK/zd+DQB5cbk5ddXFLc68W2ycPcfrd2Mm6nZzoELAHdoSW/eNnq78Wn7kv0L2yppLSkzzPa7JRBf3TGJenxhRRNgXTH2ubm5qyFtb2Vpzu78olPfEJ0AT/4wQ/kkUceybqgdAP1gJkcVOk4elZId3ypw7S/XmpLTt/73vfklltuMb10h6kBVVCS7lQ0uHv//fd9zdKB1gNpl9/Z0Wnac6Ud6KJnaWmptLe3u5gy+ySpdZ59bIZGRUAvt2s7QK+T7nt0X6N/YUttbe2O9oEa5OlZAj+cvbbVsuoBUn9ch7FunXrp8Uf3x3Epq5bXuhqUbKXrd7YA33YAlDzT5O/6DrCnn346uZft7/qy1aamJvNuMev0la6gI0eOlKFDh5qV1ZqZ9j/mmGOsTvOp0+uflXbt2mV99f2zu7vb5CE1wPM6Y13/y0fqcvUAk6lNV+q4drrzPT87y2ScaApoEKI/aHTn5UfS0+b53DY8K8PhbdrJ/sYqp5NpPCtLnhdkXQrR/XIcyqvrb1zKmu1Ym+tMru0AaN26db2iSY0s9TLV97//fTn33HNdra6aOW3orC9a1XZEL7zwgmlMrQ2qp06dKsuWLTOfhw4dktWrV8tdd93lajlMhAAC4RGwfrHpdu910jPP1dXVpp2ItWP1Og8sDwEEvBGwHQBpg2R9DlBq0stg8+bNS+1tu1ufL2Td/q5Rut4tpmnatGmyatUqmTlzptkZzZgxwzyA0faMGREBBEIr0Nraan70eF0AbWuo+zQSAghEX8B2APTee+/1OS2sbVxynWJKJVy5cmWvXscee6y55V3fMj9w4MDEMJ2vBlba2FrbgThdTmJGfEEAAQQQQAABBFIEbAdA+stIg5TFixebhxaOGDFCxo8fb25r1wawR5qSg5/keenpaBICCCCAAAIIIJBPAdsB0D//+U/zXB59IrQ+t0fvbtJGxxdeeKF5erNft6zmE4N5IYAAAggggEA8BGw/Ce9rX/uanHXWWfKf//xHNm7caB5WqM8B2rJli/z4xz+OhxalRAABBBBAAIFICNgKgPRZP/pSUr3ja9SoUabgepuqXgK76aab5G9/+1skMCgEAggggAACCMRDwFYAZD0vIt1TFfU5PnF42FI8VgdKiQACCCCAQDwEbAVA2r5Hn/WjT11ev369uRtMb1PV5/Q88MADcv7558dDi1IigAACCCCAQCQEbAVAWtKf/exnog2gP/axj4m+tFQfK64vNz3zzDPlW9/6ViQwKAQCCCCAAAIIxEPA9l1gY8eOlZdeekn+8pe/mIbPelbojDPOkLPPPjseUpQSAQQQQAABBCIjYDsA0hLr+7r0rM8ll1xiANK1CYqMDAVBAAEEEEAAgcgK2L4Eps/8mTVrlvz61782GNr4efTo0XL//ffTCDqyqwcFQwABBBBAIJoCtgOgG2+8Ud555x35+Mc/biT09RQLFiwwjaD1ZaYkBBBAAAEEEEAgLAK2LoG1t7fLn//8Z9m0aZOMHDnSlE1vjdcXleq7uvT1GFdeeWVYykw+EUAAAQQQQCDmArbOAOlDDzs6OszTn1O9GhoapK6uLrU33QgggAACCCCAQGAFbAVAJSUl8qlPfUrmzp1rXoVhlUafDq1tgPR9YCQEEEAAAQQQQCAsArYCIC3Mr371K9GHH374wx+W2tpa0TZAkyZNMg9I1NdhkBBAAAEEEEAAgbAI2GoDpIXRBx+uXLlS3njjDXn11VdF2wCdeuqpcvLJJ4elrOQTAQQQQAABBBAwArYDIB1bg56TTjrJ/OGHAAIIIIAAAgiEVcD2JbCwFpB8I4AAAggggAACqQIEQKkidCOAAAIIIIBA5AUIgCJfxRQQAQQQQAABBFIFCIBSRehGAAEEEEAAgcgLEABFvoopIAIIIIAAAgikChAApYrQjQACCCCAAAKRFyAAinwVU0AEEEAAAQQQSBUgAEoVoRsBBBBAAAEEIi9AABT5KqaACCCAAAIIIJAqQACUKkI3AggggAACCERewNGrMIKuMWjQoMBksaioyOTF7zzVN3WnNdH8lZaWph3mpqe+JiWf83OTB6ZBIF8CJSUl0tPTk6/ZeTIfzbOT/U1BQYF5vVEctlstq6bq6urQ1aublcfav4dtHXZbVq3fdOt+Z2dn1llGKgA6cOBA1sJ6ObCystJsbH7nqaGtIW2xu7q6pL29Pe0wNz11J5rP+Vl5KC6O1CpqFYvPgAt0dHRId3f6Hw9Bzbrm2cn+RgMm3W6bmpqCWqS85UvLWlZWJo2NjaJOUU9VVVXS1tYWi7LqsVbLm27d1zrPlrgElk2HYQgggAACCCAQSQECoEhWK4VCAAEEEEAAgWwCBEDZdBiGAAIIIIAAApEUIACKZLVSKAQQQAABBBDIJkAAlE2HYQgggAACCCAQSQECoEhWK4VCAAEEEEAAgWwCBEDZdBiGAAIIIIAAApEUIACKZLVSKAQQQAABBBDIJkAAlE2HYQgggAACCCAQSQECoEhWK4VCAAEEEEAAgWwCBEDZdBiGAAIIIIAAApEUIACKZLVSKAQQQAABBBDIJkAAlE2HYQgggAACCCAQSQECoEhWK4VCAAEEEEAAgWwCBEDZdBiGAAIIIIAAApEUKI5kqSgUAggg4EKgtLRUenp6pKyszHy6mMURT9LZ2Sn6R0IAgf4VIADqX1/mjgACIRIoKSmRgoICeffdd6WlpcXznB933HFSXMxu2XN4FhhLAba0WFY7hUYAgWwCO3fulAMHDmQbpV+GfehDHyIA6hdZZopAXwHaAPU1oQ8CCCCAAAIIRFyAACjiFUzxEEAAAQQQQKCvAJfA+prQJ0lA20Nou4hcqbCwULQBKQkBBBBAAIEwCBAAhaGWfMxjUVGR6J8f7SE0qBo4cKCPpWfRCCCAAAJRFSAAimrN5rFcelvwmjVr8jhHe7OqqKiQc889197IjIUAAggggIADAdoAOcBiVAQQQAABBBCIhgABUDTqkVIggAACCCCAgAMBAiAHWIyKAAIIIIAAAtEQIACKRj1SCgQQQAABBBBwIOB7I+hDhw7Ja6+91ivLU6ZMMd1tbW2yYcMG82j6SZMm2bodu9eM6EAAAQQQQAABBNII+B4ArV+/XhYuXChjxoxJZE8DIH0Pz3XXXSfjxo0TfSz9E088IfPnzzfBUGJEviCAAAIIIIAAAi4EfA+A3nrrLbnsssvkmmuu6ZX9JUuWyOTJk2XOnDmm/+zZs2Xt2rVinR3qNTIdCCCAAAIIIICAA4FABEAa1CxevFjGjh0rZ555pjnLs3XrVpk+fXqiKBMmTJDNmzf3CoCampqko6PDjKNPLNa/oCQrL9anX/nye/l+lZvlIhA7gcO7PyfbuzWu9Rllr+QyJn+PepnjUFarjNankzoNRACkGdYg6LHHHhM983PvvffK7t27paamJlEW/b59+/ZEt3655ZZbZPny5aZfcXGxbNq0qdfwIHSMGDHC12w0HSoS2dE3CyWlJaIPGrSTuru77YzGOAggkAcB3Zfpn9NUVlYmbvY3yftZp8sM2/hDhw4NW5bJr02BdOt+c3Nz1qmdb2VZZ+d84KOPPmped6CvPbj00kvl8ssvN4GOvn6hq6srMcPOzk4ZMGBAolu/XHvttXLhhReafhr9+fG6hl4ZSurQnZHmt76+Pqmv918bWxrSLrSrs0u0kXmupPWgdUNCAAFvBHS/p/s7p0nPhjvZB+q2rYGWnf2A07wEbXwtqwZ6DQ0NvY4rQctnvvJTXl5uro4kH0PzNe+gzUfLqn/pjrUaF2T7oe9rANTe3i7vvvuuDB482JjqyzSHDx9uzv5opF5XV5ew1u/HHHNMolu/6OWy5LRr167kTl+/6wanqbW11dd8tB02Tpf0rI6djUNXIAKgdIL0Q6B/BPTVM3a2zdSld3d1O9rfWC859nsflVqO/ui2yqrHHKvZRH8sJyjz1MA2LmXVY62ecEi3Hmv/bMnXn/a6UuqdXdZ7pl5//XXZv3+/nHHGGTJ16lRZtmyZKdS+fftk9erVMn78+GxlYRgCCCCAAAIIIGBLwNczQHp2Qe/yWrRokfl7//33Ze7cuea07LRp02TVqlUyc+ZMcwZixowZMnr0aFuFYiQEEEAAAQQQQCCbgK8BkGZs4sSJ8vDDD8vBgwfNNVoNijTpKbx58+ZJY2OjaUvjplGgmRH/IYAAAggggAACKQK+B0BWfmpra62vvT6rq6t7ddOBAAIIIIAAAggcqYCvbYCONPNMjwACCCCAAAIIuBEgAHKjxjQIIIAAAgggEGoBAqBQVx+ZRwABBBBAAAE3AgRAbtSYBgEEEEAAAQRCLUAAFOrqI/MIIIAAAggg4EaAAMiNGtMggAACCCCAQKgFCIBCXX1kHgEEEEAAAQTcCBAAuVFjGgQQQAABBBAItQABUKirj8wjgAACCCCAgBsBAiA3akyDAAIIIIAAAqEWIAAKdfWReQQQQAABBBBwI0AA5EaNaRBAAAEEEEAg1AIEQKGuPjKPAAIIIIAAAm4ECIDcqDENAggggAACCIRagAAo1NVH5hFAAAEEEEDAjUCxm4mYBgEEEEAg/wLFxcVSWFgoJSUljmeu09bU1NieTpejf0VFRYlpOjs7pbm5OdHNFwSiLEAAFOXapWwIIBAqAQ18uru7RQMRp6mzq0uampqcTpYYv6KiwgRQBEAJEr5EXIAAKOIVTPEQQCBcArt27ZJ//etfWTM9fEyZFBQU9Bpn46uvypfm/rRXPycdV1xxhcyePdvJJIyLQKgFaAMU6uoj8wgggAACCCDgRoAAyI0a0yCAAAIIIIBAqAUIgEJdfWQeAQQQQAABBNwIEAC5UWMaBBBAAAEEEAi1AAFQqKuPzCOAAAIIIICAGwECIDdqTIMAAggggAACoRYgAAp19ZF5BBBAAAEEEHAjQADkRo1pEEAAAQQQQCDUApF6EGJtbW1gKkMfS6/J7zxVNad/oqw+/r60tDSnV+rD1nJOwAgIIBBqAb/3Wf2Bp6/80FRVVWWetN0fywjSPPX4Yz1VPEj56o+8aFn1OJVuve06/HT0bClSAVBjY2O2sno6TB8rryug33lqbk3/Xh9dMdrb23Oa6MplBXM5R2YEBBAIvYDf+6z+ANR9cXl5uXlViJvXjPRHnvpznpWVlWb/3tHR0Z+LCcS89Virx6h0663We7YUqQBI36ETlNTT02Oy4neeunuCYxKUuiEfCCCQWcDvfVbmnLkfYpVJ98vWd/dzC/6UVjnjUlatETdlpQ1Q8NdlcogAAggggAACeRYgAMozKLNDAAEEEEAAgeALEAAFv47IIQIIIIAAAgjkWSBSbYDybBOY2R1JQ+SynvR3euldEXonWK5k3T2RazyGI4AAAgggECYBAqAQ1FZNTY2UlZW5ymldSYbb4IuLbM/TTeMyV5llIgQQQAABBDwSIADyCPpIFqMt+v/yl7/IokWLnM9mWI0UXj25z3Tbtm2TTS9v69M/tcfpp58uQ4YMSe1NNwIIIIAAAqEWIAAKSfW1tbVJQ0OD49wWVRZKdZqpujr1OUDZHxKlk3H2Jw0evRBAAAEEQi9AI+jQVyEFQAABBBBAAAGnAgRATsUYHwEEEEAAAQRCL0AAFPoqpAAIIIAAAggg4FSAAMipGOMjgAACCCCAQOgFCIBCX4UUAAEEEEAAAQScChAAORVjfAQQQAABBBAIvQABUOirkAIggAACCCCAgFMBngPkVIzxEUAAgQgKDBs2TPShq4MGDfKtdI2NjdLZmf7p9b5ligVHVoAAKLJVS8EQQAAB+wLDhw8Xffffhg0b7E+UpzH1vYRnnXWWtLa2EgDlyZTZ5BYgAMptxBgIIIBALAT0ye933nmn52WtqKiQP/zhD54vlwXGW4A2QPGuf0qPAAIIIIBALAUIgGJZ7RQaAQQQQACBeAsQAMW7/ik9AggggAACsRSgDVAsq51CI4AAAsET0EbY2iA630nnqynb/Lu6uvK9WOYXcAECoIBXENlDAAEEoi5QUlJibsGvqakR/euvNHjw4Iyzbmlpkfr6+ozDGRA9AQKg6NUpJUIAAQRCJVBcXCwFBQWydOlS2bhxo+d5nzFjhhx//PGeL5cF+itAAOSvP0tHAAEEEPifwNtvvy3r1q3z3OOCCy7wfJks0H8BGkH7XwfkAAEEEEAAAQQ8FiAA8hicxSGAAAIIIICA/wIEQP7XATlAAAEEEEAAAY8FAt0GqK2tzbyXRhvHTZo0SfROAb9SZWWlaaRnd/mlpaVm1KqqKruTZBzPuoUz4wgMQAABBBBAAAFHAoENgPSWxOuuu07GjRsnO3fulCeeeELmz5/vKAhxJJFlZL1DQW/NbG5uFrvPimhvbxf9y0ciAMqHIvNAAAEE0gvoPlZ/aOu+3o+ky9f3sOUr6fz0eUo9PT05Z6nltjNezhm5HEHLnc+yO8mGP7VtI4dLliyRyZMny5w5c8zYs2fPlrVr18qUKVNsTJ3fUXQF0XTrrbfKli1b8jtzG3N76qmnbIzFKAgggAACbgROOOEEKSsrk6OOOsrN5IGcRq9a2En6o74/Hj5pZ9k6jl7pqaurszt6XscrOBz55Q4R87pIezO77bbbZPr06XLOOeeYCR5++GFzCUzPClnpjjvukJUrV5pOjdyfffZZa1DeP3X+WkkdHR15n3euGQ4ZMsQst6GhIdeofYa/11ov9+14oU//odWjZGjNqD79U3vopTwtu5798jpp4DlgwABzJq2zs9PrxUt5ebn5ZaQbqNdJL/fqX2trqy+/jrTsumP0Y33XZWvSsnuddF3Xdd5Pd/01nOvs8ZYda/rQnFwxTGYf7f4HYm1trVnn9+zZ02fe/d1Dz1ho8HHw4EFf6n3o0KG+BgH97Ztt/n4HQEe6b9fjhK4/6a7O6L47WyAY2DNAu3fv7vVEUL0EtX379l71qJfHrMhVP/vzQKU7perq6l7Lz9ahFaJ/R1q51jL0YJjtKabWeKmf9U2HT6vuSO0r8smao+SKkR/tOyBNH3OKdGD2OFn9062AaWbnqJca+nV61Drzl/obwVrn+qO8yTh+lj152f1Vt8llTf6eyT15nP76rgGQ1nd/122m/Nst+5d2rJWew/+SU0lJqaN9RLpl6f7KzX4mOR9uv6t5RUWF+XM7j0zTaVm1brV8qduzTqP9Mg3LNM989k/e3vIxX52flildWVPnr+P68UPHyofu34/kOKnHRi1DuuN/ru04sAFQ6k5XgfRsQHL6zGc+k9wpu3bt6tXtZ4dGnRow7du3z89sSH1b+ke7Nzc15zVvgwYNkgMHDvhaVq8WrmXVFKfyxqWsI0aMkEOHDklTU5NXq5PL5fQOfnQmHYfbHDrZ3+iBQ892Bb+sLomSJtOy6lkefdWFnwf7pCz161e9+UYDgjiUVY+1Gtymu0KilzWzpcDeBq8ra/J1Qf0+cuTIbGVhGAIIIIAAAgggYEsgsAHQ1KlTZdmyZeZ6sP6qWb16tYwfP95WoRgJAQQQQAABBBDIJhDYS2DTpk2TVatWycyZM831PX1Z3ejRo7OVhWEIIIAAAggggIAtgcAGQHpNb968edLY2Gja/mg3CQEEEEAAAQQQyIdA4KMKJ3de5QOEeSCAAAIIIIBA9AUC2wYo+vSUEAEEEEAAAQT8EiAA8kue5SKAAAIIIICAbwIEQL7Rs2AEEEAAAQQQ8EuAAMgveZaLAAIIIIAAAr4JEAD5Rs+CEUAAAQQQQMAvgcDfBeYEJki3yuurA3bu3Cn6IlM/U3V3uZxSOaJPFkaU15jHh/cZ4LKHvnMlSP4ui2Frsh07/vtyNX0/XRxSnOr2jTfeMI/d0HdSBTmdWnm0dKe8C+zY8kGOtkHrfVFx2G715bKbN2+WgQMHmpcMB7lu85E3fQeY1m8c6jbbsdZ6b2Mm08C+DT5ThsPS/+c//7k88MAD8s9//jMsWSafNgW++tWvmjEfeeQRm1MwWlgETj31VJkzZ458+ctfDkuWyacNgVdffVU+//nPy9KlS+WUU06xMQWjhEXg4YcfFv17+eWXHWeZS2COyZgAAQQQQAABBMIuQAAU9hok/wgggAACCCDgWKDo/x1OjqdigpwCev31Qx/6kEycODHnuIwQLgG9rvzRj35UPvKRj4Qr4+Q2p4DW7aRJk2TEiL7t5nJOzAiBFSgoKBBts/exj31MKisrA5tPMuZcQI+1o0aNkgkTJjiemDZAjsmYAAEEEEAAAQTCLsAlsLDXIPlHAAEEEEAAAccCkboN3nHpXU7Q1tYmGzZsED2tqqfLS0pK0s6poaFB1q1bJ+PGjZOjjz46Mc57771nbpG3euit8mPHjrU6+fRZ4NChQ7Jx40Y5++yzM+Zk37598tJLL8lxxx0nJ554YmI8u+tGYgK+eCqwZcsWKS8vN/WWacF6K/y7775rTqkPHTrUjKbrxGuvvdZrkilTpvTqpsNfATt1qzlcvny5nHfeeb1uEc+0PftbIpZuCaSrM2uY9Zluv53rWEsbIEvP5mdLS4vMmjVLmpqaZNWqVbJixQq54IILTDCUPItnnnlGvv/974u+zf6Xv/yl6HNFxowZY0a577775MUXX5R33nlHdKPt7OyU0047LXlyvvsk0NraKrfffru8+eabcv7556fNxSuvvGJuldZniujtl3pAPemkk8TuupF2pvTsd4F///vfpt6OP/74jD847r//fnn66adFnxvz4IMPmiC4trbWbOsLFiyQvXv3mm1Wt9tM60e/F4QF9BGwU7c6kd4Gf/fdd8sXv/jFRACUaXvusxB6+CKQrs5SM5Jpv53rWMsZoFTJHN1LliyRyZMnmx2pjjp79mxZu3atpP4a/N3vfifavlwPjPr8Cf3TXx1lZWXy1ltvmY3wwx/+cI6lMdhLgbfffltuvfVW0QOe/mVKeiC888475fTTT5errrpKvvKVr8gll1widteNTPOlf/8J/PGPfzQ/RLLVq/4gWblypTz55JPmIXK6Df/2t7+V73znO2abveyyy+Saa67pv0wyZ1cCdupWf2TqD5v9+/f3WUam7bm0tLTPuPTwTiBbnSXnItt+O9exljZAyZI2vm/durVXa3Ntea5PGE1O+mRK/aVo3SWkl7j0TJA+Qbi5uVnq6urk/fffl8cff1y2b9+ePCnffRTQuvnud78rM2fOzJgL3Si1zqwzdsOHDzdn97Ru7awbGWfMgH4VGDBggPziF78wZ3700nW6pDtSrVe9q0RT8ratO9KqqipZvHixrF+/XvRJu6RgCNipW32auf5I1bN6ySnb9pw8Ht+9F8hUZ6k5ybTftnOsJQBK1czRvXv3bnM7pTWa3lqZ+qti0KBBcsIJJ8jf//53M5qeYtVrzLt27ZJt27aJthPRnahWsD519rnnnrNmx6ePAvqEWH0ScLakga3eRpt8ENWzChrU2lk3ss2bYf0nMH369MRraTIFL7p9Jp8hSt62NQBas2aNOYP72GOPybe//e3+yyxzdiRgp271zLuewUt9NUS27dlRJhg57wKZ6ix1QZn223aOtVwCS9XM0a3PCdHAxUr6C0J/gaQmvTS2cOFCWbRokWkAffLJJ5udqz4/5qmnnhINkjRpu6BHH33UXEJJnQfdwRNIrX/Noa4D2g4odVimdSN4pSJHKpCt/nQb1TZfenbo0ksvlcsvv9ycCdTnj5DCK5Ba51oSa3sOb6nIuQpo85Ncx1rOADlcV/SuEP21byX9PnLkSKsz8Tl+/HgT2Oj7ovQac319vXkwon7qJTIraTugPXv2SHd3t9WLzwAL6OVMbQCvZ/GsZK0DdtcNazo+gyVw1FFH9dm29e5NbRCtd4VZl8a0bYhe+tQzfqRwC2TbnsNdMnJv51hLAORwPZk6daosW7ZMtNW5XtZavXq1aLCjSbv1T9PXvvY12bRpk7lcpneL6cFRz/ocPHhQbrzxRnPHkJ6K/9Of/iSf/OQnEztXMzH/BU5AG8hqnespdG0Er3f5aXrhhRdMvWrdZls3AlcgMmQE9M49DW406SMt9Fb3//znP+YswLPPPmueHKyPuZg/f765BKbjvf766+ay9xlnnKGdpIAKJO+PM2Ux2/acaRr6+y9gp27tHGu5BOawLqdNm2ZuidWGsvqLcMaMGTJ69GgzF73dXXeW2q5HL4Hdc889iUsjegeCJm0bdOWVV5oASU+1ajuDefPmmWH8F1yB66+/Xu666y5z59cNN9wgN998szm9quuAVbfZ1o3glizeOdMfKXfccYcJaHVb1B8uelff4MGD5dhjj5Wrr77atPfSbVovZ+uf3sAwd+7cPu1J4i0ZvNIn74+z5S7T9pxtGob5K2Cnbu0ca3kVhst6bGxsNG1/UhvVpc5OH86kd4+kJr3kpcN0p0sKp4CeYtV2IanJ7rqROh3dwRDo6OgwlzjTbbf6q1K32eRG8MHINbk4UoFM2/ORzpfp/RXIdqwlAPK3blg6AggggAACCPggQBsgH9BZJAIIIIAAAgj4K0AA5K8/S0cAAQQQQAABHwQIgHxAZ5EIIIAAAggg4K8AAZC//iwdAQQQQAABBHwQIADyAZ1FIoAAAggggIC/AgRA/vqzdAQQyJNAQ0ODozk5Hd/RzBkZAQQCL0AAFPgqIoMIIJBL4Pnnn5cLL7ww12iJ4U7HT0zIFwQQiIwAAVBkqpKCIBBfAX2Fhb6jzW5yOr7d+TIeAgiER4AAKDx1RU4RiLXAK6+8IrNmzZJPf/rT8tWvflXWrVtnPP7xj3/I0qVLzdvZtb/1smF9l9d1111nxv/Sl75k3uGnE2QaP9a4FB6BGAoQAMWw0ikyAmET2LNnj5x77rnm3Xoa5OirKM4++2zZsmWLDBs2TEaNGiUVFRXm5aX6tvYHH3zQvMfr+OOPl2uuuUaam5vl4osvlvXr16cdP2we5BcBBI5cgJehHrkhc0AAgX4W0EtWGsR873vfMwHM5z//eTnppJOkp6fHfE6ePFm2bt1qzgxpVvbu3Sv33XefebmpdutLTY866ijzRvevf/3rkjq+jkNCAIF4CRAAxau+KS0CoRTQgEXf7jx27FiZPn26XHTRRaKXtYYMGZK2PBoo7dq1S/7whz+Ys0QbN26UlpYWaW1tTTs+PRFAIH4CXAKLX51TYgRCJ6BvZtc2Pz/84Q/NmaAbbrhB9PLWihUr0pZlwYIFMnr0aDO+ng367Gc/a84cpR2ZngggEEsBAqBYVjuFRiBcAm+++aZoo2YNfJ577jlziUsvgS1cuNAURNsEWUnP9Nxyyy1yzz33mDY/GgxdeeWVZpru7u4+41vT8YkAAvESIACKV31TWgRCK3DttdfKk08+KV1dXaIPMdS7vfSymKbBgwebS14aKGnSS2O7d+8WDXi07dA3vvENaWtrS1wCSx6/s7PTTMN/CCAQLwECoHjVN6VFIJQCH/nIR+Tee++V2267TWpra83lrVNPPdV0a4HOOeccKSoqkhNPPFG0vc+PfvQj+d3vfmcue40YMUL0EtqMGTNEb6VPHX/Dhg2mH/8hgEC8BAoO30XRE68iU1oEEAizgJ7ZGThwoLklPrUc9fX1ZpjVf/v27aIBUHFx+vs9Use3puMTAQSiL0AAFP06poQIIIAAAgggkCLAJbAUEDoRQAABBBBAIPoCBEDRr2NKiAACCCCAAAIpAgRAKSB0IoAAAggggED0BQiAol/HlBABBBBAAAEEUgQIgFJA6EQAAQQQQACB6AsQAEW/jikhAggggAACCKQIEAClgNCJAAIIIIAAAtEXIACKfh1TQgQQQAABBBBIEfj/vy1wWcPLS/0AAAAASUVORK5CYII=" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---correlation-1" class="section level3">
 <h3>Two numerical vars - correlation</h3>
@@ -1343,9 +1348,6 @@ <h3>Two numerical vars - correlation</h3>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
 ( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1362,13 +1364,12 @@ <h3>Two numerical vars - correlation</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> percentile_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, 
                             <span class="dt">point_estimate =</span> correlation_hat) )</code></pre></div>
-<pre><code>## Warning: 'get_ci' is deprecated.
-## Use 'get_confidence_interval' instead.
-## See help(&quot;Deprecated&quot;)</code></pre>
 <div class="kable-table">
 <table>
 <thead>
@@ -1385,8 +1386,10 @@ <h3>Two numerical vars - correlation</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">endpoints =</span> standard_error_ci, <span class="dt">direction =</span> <span class="st">&quot;between&quot;</span>)  </code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---t" class="section level3">
 <h3>Two numerical vars - t</h3>
diff --git a/inst/doc/two_sample_t.R b/inst/doc/two_sample_t.R
index 80ce97a0..69ce6fd4 100644
--- a/inst/doc/two_sample_t.R
+++ b/inst/doc/two_sample_t.R
@@ -43,7 +43,9 @@ t_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
+t_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 t_null_distn %>% 
@@ -56,7 +58,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2")) %>%
-  visualize(method = "theoretical", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ----eval=FALSE----------------------------------------------------------
 #  fli_small %>%
@@ -65,12 +68,14 @@ fli_small %>%
 #    hypothesize(null = "independence") %>%
 #    generate(reps = 1000, type = "permute") %>%
 #    calculate(stat = "t", order = c("h1", "h2")) %>%
-#    visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+#    visualize(method = "both") +
+#      shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ----echo=FALSE----------------------------------------------------------
 # To use same distribution calculated above
 t_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 fli_small %>% 
diff --git a/inst/doc/two_sample_t.Rmd b/inst/doc/two_sample_t.Rmd
index 9fbe54ab..c77980bb 100755
--- a/inst/doc/two_sample_t.Rmd
+++ b/inst/doc/two_sample_t.Rmd
@@ -93,7 +93,9 @@ t_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
+t_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -113,7 +115,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2")) %>%
-  visualize(method = "theoretical", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
@@ -125,13 +128,15 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2")) %>% 
-  visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ```{r echo=FALSE}
 # To use same distribution calculated above
 t_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
index 7a8ecc8c..94ebc223 100644
--- a/inst/doc/two_sample_t.html
+++ b/inst/doc/two_sample_t.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-09-03" />
+<meta name="date" content="2018-09-14" />
 
 <title>Two sample t test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Two sample <span class="math inline">\(t\)</span> test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-03</em></h4>
+<h4 class="date"><em>2018-09-14</em></h4>
 
 
 
@@ -167,8 +167,10 @@ <h2>Randomization approach to t-statistic</h2>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">visualize</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
@@ -197,11 +199,12 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>, <span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" 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" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-t-distribution-on-top-of-permuted-t-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on top of permuted t-statistics</h2>
@@ -211,10 +214,11 @@ <h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on t
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>, <span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
+<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. `infer` currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
+## theoretical method. {infer} currently does not check these for you.</code></pre>
+<p><img 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" /><!-- --></p>
 </div>
 <div id="compute-theoretical-p-value" class="section level2">
 <h2>Compute theoretical p-value</h2>
diff --git a/vignettes/chisq_test.Rmd b/vignettes/chisq_test.Rmd
index d56b5201..0f7ea2f0 100644
--- a/vignettes/chisq_test.Rmd
+++ b/vignettes/chisq_test.Rmd
@@ -92,7 +92,9 @@ chisq_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>% visualize(obs_stat = obs_chisq, direction = "greater")
+chisq_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -111,7 +113,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq") %>%
-  visualize(method = "theoretical", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
@@ -122,13 +125,15 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq") %>% 
-  visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ```{r echo=FALSE}
 # To use same distribution calculated above
 chisq_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_chisq, direction = "right")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 
diff --git a/vignettes/observed_stat_examples.Rmd b/vignettes/observed_stat_examples.Rmd
index 3a2f3dc7..30404cda 100644
--- a/vignettes/observed_stat_examples.Rmd
+++ b/vignettes/observed_stat_examples.Rmd
@@ -70,7 +70,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
 null_distn %>% 
-  visualize(obs_stat = x_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
@@ -91,7 +92,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
 null_distn %>% 
-  visualize(obs_stat = t_bar, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
@@ -114,7 +116,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "median")
 null_distn %>% 
-  visualize(obs_stat = x_tilde, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
@@ -136,7 +139,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
 null_distn %>% 
-  visualize(obs_stat = p_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
@@ -174,7 +178,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -196,7 +201,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
 null_distn %>% 
-  visualize(obs_stat = z_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
@@ -225,7 +231,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -247,7 +254,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
 null_distn %>% 
-  visualize(obs_stat = Chisq_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -269,7 +277,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -291,7 +300,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = t_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -316,7 +326,8 @@ null_distn <- fli_small %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
 null_distn %>% 
-  visualize(obs_stat = d_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -338,7 +349,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
 null_distn %>% 
-  visualize(obs_stat = F_hat, direction = "greater")
+  visualize() +
+    shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 ```
@@ -360,7 +372,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
 null_distn %>% 
-  visualize(obs_stat = slope_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
@@ -382,7 +395,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
 null_distn %>% 
-  visualize(obs_stat = correlation_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
@@ -407,7 +421,8 @@ null_distn <- fli_small %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "t")
 null_distn %>% 
-  visualize(obs_stat = t_hat, direction = "two_sided")
+  visualize() +
+    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -431,9 +446,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical (one mean - standardized)
@@ -452,9 +471,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -474,9 +497,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One categorical variable (standardized proportion $z$)
@@ -499,9 +526,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -520,9 +551,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -542,9 +577,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two categorical variables (z)
@@ -563,9 +602,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -585,9 +628,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between") 
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two numerical vars - correlation
@@ -606,10 +653,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")  
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -631,7 +682,11 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>% visualize(endpoints = percentile_ci, direction = "between")
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>% visualize(endpoints = standard_error_ci, direction = "between")  
+boot %>%
+  visualize() +
+    shade_confidence_interval(endpoints = standard_error_ci)
 ```
diff --git a/vignettes/two_sample_t.Rmd b/vignettes/two_sample_t.Rmd
index 9fbe54ab..c77980bb 100755
--- a/vignettes/two_sample_t.Rmd
+++ b/vignettes/two_sample_t.Rmd
@@ -93,7 +93,9 @@ t_null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>% visualize(obs_stat = obs_t, direction = "two_sided")
+t_null_distn %>%
+  visualize() +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -113,7 +115,8 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2")) %>%
-  visualize(method = "theoretical", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "theoretical") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
@@ -125,13 +128,15 @@ fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2")) %>% 
-  visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ```{r echo=FALSE}
 # To use same distribution calculated above
 t_null_distn %>% 
-  visualize(method = "both", obs_stat = obs_t, direction = "two_sided")
+  visualize(method = "both") +
+    shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 

From 861f85f39e271e9d02ccce1889d4a94f441a6346 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 16 Sep 2018 10:00:57 +0300
Subject: [PATCH 57/78] Move warning about right-tail tests into
 `shade_p_value()`.

---
 R/visualize.R                   | 31 ++++++++++++++++++-------------
 tests/testthat/test-visualize.R | 28 ++++++++++++++++++++++++++--
 2 files changed, 44 insertions(+), 15 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index b556aaff..4034c49c 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -180,11 +180,6 @@ check_visualize_args <- function(data, bins, method, dens_color,
     )
   }
   
-  theory_type <- short_theory_type(data)
-  if (theory_type %in% c("F", "Chi-Square")) {
-    warn_right_tail_test(direction, theory_type)
-  }
-  
   TRUE
 }
 
@@ -433,7 +428,7 @@ shade_p_value <- function(obs_stat, direction,
       
       res <- c(res, list(geom_tail(tail_data, fill, ...)))
     } else if (direction %in% c("two_sided", "both")) {
-      tail_data <- two_tail_data(obs_stat)
+      tail_data <- two_tail_data(obs_stat, direction)
       
       res <- c(res, list(geom_tail(tail_data, fill, ...)))
     } else {
@@ -568,7 +563,8 @@ short_theory_type <- function(x) {
 }
 
 warn_right_tail_test <- function(direction, stat_name) {
-  if (!is.null(direction) && !(direction %in% c("greater", "right"))) {
+  if (!is.null(direction) && !(direction %in% c("greater", "right")) &&
+      (stat_name %in% c("F", "Chi-Square"))) {
     warning_glue(
       "{stat_name} usually corresponds to right-tailed tests. ",
       "Proceed with caution."
@@ -591,19 +587,28 @@ geom_tail <- function(tail_data, fill, ...) {
 }
 
 one_tail_data <- function(obs_stat, direction) {
-  if (direction %in% c("less", "left")) {
-    data.frame(x_min = -Inf, x_max = obs_stat)
-  } else if (direction %in% c("greater", "right")) {
-    data.frame(x_min = obs_stat, x_max = Inf)
+  # Take advantage of {ggplot2} functionality to accept function as `data`.
+  # Needed to warn about incorrect usage of right tail tests.
+  function(data) {
+    warn_right_tail_test(direction, short_theory_type(data))
+    
+    if (direction %in% c("less", "left")) {
+      data.frame(x_min = -Inf, x_max = obs_stat)
+    } else if (direction %in% c("greater", "right")) {
+      data.frame(x_min = obs_stat, x_max = Inf)
+    }
   }
 }
 
-two_tail_data <- function(obs_stat) {
-  # Take advantage of {ggplot2} functionality to accept function as `data`
+two_tail_data <- function(obs_stat, direction) {
+  # Take advantage of {ggplot2} functionality to accept function as `data`.
   # This is needed to make possible existence of `shade_p_value()` in case of
   # `direction = "both"`, as it depends on actual `data` but adding it as
   # argument to `shade_p_value()` is very bad.
+  # Also needed to warn about incorrect usage of right tail tests.
   function(data) {
+    warn_right_tail_test(direction, short_theory_type(data))
+    
     if (get_viz_method(data) == "theoretical") {
       second_border <- -obs_stat
     } else {
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index f3a94e48..53222d9f 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -474,10 +474,34 @@ test_that("shade_confidence_interval throws errors and warnings", {
   )
 })
 
+test_that("warn_right_tail_test works", {
+  expect_warn_right_tail <- function(stat_name) {
+    warn_regex <- paste0(stat_name, ".*right-tailed")
+    
+    expect_silent(warn_right_tail_test(NULL, stat_name))
+    expect_silent(warn_right_tail_test("right", stat_name))
+    expect_warning(warn_right_tail_test("left", stat_name), warn_regex)
+    expect_warning(warn_right_tail_test("two_sided", stat_name), warn_regex)
+  }
+  
+  expect_warn_right_tail("F")
+  expect_warn_right_tail("Chi-Square")
+})
+
+test_that("one_tail_data works", {
+  fun_output_left <- one_tail_data(1, "left")
+  expect_equal(colnames(fun_output_left(iris_permute)), c("x_min", "x_max"))
+  
+  fun_output_right <- one_tail_data(1, "right")
+  expect_equal(colnames(fun_output_right(iris_permute)), c("x_min", "x_max"))
+})
+
 test_that("two_tail_data works", {
+  fun_output <- two_tail_data(1, "two_sided")
+  
   attr(iris_permute, "viz_method") <- "both"
-  expect_equal(colnames(two_tail_data(1)(iris_permute)), c("x_min", "x_max"))
+  expect_equal(colnames(fun_output(iris_permute)), c("x_min", "x_max"))
   
   attr(iris_permute, "viz_method") <- "theoretical"
-  expect_equal(colnames(two_tail_data(1)(iris_permute)), c("x_min", "x_max"))
+  expect_equal(colnames(fun_output(iris_permute)), c("x_min", "x_max"))
 })

From c65c52bb8d5b13b94f05e2e6847d403ebf1ceba6 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 16 Sep 2018 10:15:12 +0300
Subject: [PATCH 58/78] Update 'NEWS.md'.

---
 NEWS.md | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index d3915e1f..c101e33e 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,18 @@
 # infer 0.3.1.9000
 
+## Deprecation changes
+
+- Deprecated `p_value()` (use `get_p_value()` instead) (#180).
+- Deprecated `conf_int()` (use `get_confidence_interval()` instead) (#180).
+- Deprecated (via warnings) plotting p-value and confidence interval in `visualize()` (use new functions `shade_p_value()` and `shade_confidence_interval()` instead) (#178).
+
+## New functions
+
+- `shade_p_value()` - {ggplot2}-like layer function to add information about p-value region to `visualize()` output. Has alias `shade_pvalue()`.
+- `shade_confidence_interval()` - {ggplot2}-like layer function to add information about confidence interval region to `visualize()` output. Has alias `shade_ci()`.
+
+## Other
+
 - Account for `NULL` value in left hand side of formula in `specify()` (#156) and `type` in `generate()` (#157).
 - Update documentation code to follow tidyverse style guide (#159).
 - Remove help page for internal `set_params()` (#165).
@@ -9,8 +22,6 @@
 - Make transparancy in `visualize()` to not depend on method and data volume.
 - Make `visualize()` work for "One sample t" theoretical type with `method = "both"`.
 - Add `stat = "sum"` and `stat = "count"` options to `calculate()` (#50).
-- Deprecated `p_value()` (use `get_p_value()` instead) (#180).
-- Deprecated `cont_int()` (use `get_confidence_interval()` instead) (#180).
 
 # infer 0.3.1
 

From d90eb1b9c30f245bc7dea32c076801d045664ab3 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Sun, 16 Sep 2018 10:34:01 +0300
Subject: [PATCH 59/78] Use `shade_confidence_interval()` in `visualize()`
 instead of `shade_ci()`.

---
 R/visualize.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/visualize.R b/R/visualize.R
index 4034c49c..119cce3b 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -116,7 +116,7 @@ visualize <- function(data, bins = 15, method = "simulation",
   
   if (!is.null(direction) && (direction == "between")) {
     infer_plot <- infer_plot +
-      shade_ci(endpoints, endpoints_color, ci_fill, ...)
+      shade_confidence_interval(endpoints, endpoints_color, ci_fill, ...)
   }
   
   infer_plot

From 005af2bdf1528638a9eb775e6f0c5a39c371bf97 Mon Sep 17 00:00:00 2001
From: "Albert Y. Kim" <albert.ys.kim@gmail.com>
Date: Sun, 16 Sep 2018 10:36:43 -0400
Subject: [PATCH 60/78] Added middle initial to "Albert Y. Kim"

thanks
---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index c3301693..c0a8128b 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -11,7 +11,7 @@ Authors@R: c(
     person("Ted", "Laderas", email = "tedladeras@gmail.com", role = "ctb"),
     person("Nick", "Solomon", email = "nick.solomon@datacamp.com", role = "ctb"),
     person("Johanna", "Hardin", email = "Jo.Hardin@pomona.edu", role = "ctb"),
-    person("Albert", "Kim", email = "albert.ys.kim@gmail.com", role = "ctb"),
+    person("Albert Y.", "Kim", email = "albert.ys.kim@gmail.com", role = "ctb"),
     person("Neal", "Fultz", email = "nfultz@gmail.com", role = "ctb"),
     person("Doug", "Friedman", email = "doug.nhp@gmail.com", role = "ctb"),
     person("Richie", "Cotton", email = "richie@datacamp.com", role = "ctb"),

From bf3e403451a0931ce238682d34b99a786381a88e Mon Sep 17 00:00:00 2001
From: alex hayes <alexpghayes@gmail.com>
Date: Mon, 17 Sep 2018 16:19:26 -0500
Subject: [PATCH 61/78] Use pull() only (without select()) in vignettes

---
 vignettes/chisq_test.Rmd       | 3 +--
 vignettes/flights_examples.Rmd | 6 ++----
 vignettes/two_sample_t.Rmd     | 6 ++----
 3 files changed, 5 insertions(+), 10 deletions(-)

diff --git a/vignettes/chisq_test.Rmd b/vignettes/chisq_test.Rmd
index f2dd959a..636b553a 100644
--- a/vignettes/chisq_test.Rmd
+++ b/vignettes/chisq_test.Rmd
@@ -137,7 +137,6 @@ chisq_null_distn %>%
 ```{r}
 fli_small %>% 
   chisq_test(formula = origin ~ season) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 ```
 
diff --git a/vignettes/flights_examples.Rmd b/vignettes/flights_examples.Rmd
index 07f3f82c..059d2da2 100644
--- a/vignettes/flights_examples.Rmd
+++ b/vignettes/flights_examples.Rmd
@@ -259,8 +259,7 @@ null_distn %>%
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 null_distn <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    hypothesize(null = "independence") %>%
@@ -348,8 +347,7 @@ c(lower = d_hat - 2 * sd(boot),
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 boot <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    generate(reps = 1000, type = "bootstrap") %>%
diff --git a/vignettes/two_sample_t.Rmd b/vignettes/two_sample_t.Rmd
index 9a2295f4..67d2a1a4 100755
--- a/vignettes/two_sample_t.Rmd
+++ b/vignettes/two_sample_t.Rmd
@@ -69,8 +69,7 @@ Or using `t_test` in `infer`
 obs_t <- fli_small %>% 
   t_test(formula = arr_delay ~ half_year, alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(statistic) %>% 
-  dplyr::pull()
+  dplyr::pull(statistic)
 ```
 
 The observed $t$ statistic is `r obs_t`.
@@ -142,7 +141,6 @@ fli_small %>%
   t_test(formula = arr_delay ~ half_year,
          alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 ```
 

From d0780dcad63a066c8839c59117bdc2c8062497fa Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Mon, 24 Sep 2018 21:07:24 +0300
Subject: [PATCH 62/78] Separate long pipelines ending with plot into two:
 build data and visualize.

---
 vignettes/chisq_test.Rmd   | 27 ++++++++-------------------
 vignettes/two_sample_t.Rmd | 28 ++++++++--------------------
 2 files changed, 16 insertions(+), 39 deletions(-)

diff --git a/vignettes/chisq_test.Rmd b/vignettes/chisq_test.Rmd
index 0454873b..1cb2f535 100644
--- a/vignettes/chisq_test.Rmd
+++ b/vignettes/chisq_test.Rmd
@@ -87,12 +87,12 @@ Lastly, the observed $\chi^2$ statistic is `r obs_chisq`.
 ## Randomization approach to $\chi^2$-statistic
 
 ```{r}
-chisq_null_distn <- fli_small %>%
+chisq_null_perm <- fli_small %>%
   specify(origin ~ season) %>% # alt: response = origin, explanatory = season
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>%
+chisq_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
@@ -100,7 +100,7 @@ chisq_null_distn %>%
 ## Calculate the randomization-based $p$-value
 
 ```{r}
-chisq_null_distn %>% 
+chisq_null_perm %>% 
   get_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
@@ -108,35 +108,24 @@ chisq_null_distn %>%
 ## Theoretical distribution
 
 ```{r }
-fli_small %>%
+chisq_null_theor <- fli_small %>%
   specify(origin ~ season) %>% 
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "Chisq") %>%
+  calculate(stat = "Chisq")
+chisq_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
 
-```{r eval=FALSE}
-fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "Chisq") %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
-```
-
-```{r echo=FALSE}
-# To use same distribution calculated above
-chisq_null_distn %>% 
+```{r}
+chisq_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
-
 ## Compute theoretical p-value
 
 ```{r}
diff --git a/vignettes/two_sample_t.Rmd b/vignettes/two_sample_t.Rmd
index 3916a88c..a4ef6534 100755
--- a/vignettes/two_sample_t.Rmd
+++ b/vignettes/two_sample_t.Rmd
@@ -86,13 +86,13 @@ The observed $t$ statistic is `r obs_t`.
 ## Randomization approach to t-statistic
 
 ```{r}
-t_null_distn <- fli_small %>%
+t_null_perm <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>%
+t_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
@@ -100,7 +100,7 @@ t_null_distn %>%
 ## Calculate the randomization-based $p$-value
 
 ```{r}
-t_null_distn %>% 
+t_null_perm %>% 
   get_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
@@ -108,37 +108,25 @@ t_null_distn %>%
 ## Theoretical distribution
 
 ```{r }
-fli_small %>%
+t_null_theor <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "t", order = c("h1", "h2")) %>%
+  calculate(stat = "t", order = c("h1", "h2"))
+t_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
 
-```{r eval=FALSE}
-fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("h1", "h2")) %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-```{r echo=FALSE}
-# To use same distribution calculated above
-t_null_distn %>% 
+```{r}
+t_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
-
 ## Compute theoretical p-value
 
 ```{r}

From cf00041adc7167da800e5c4f03ec344d10753fa8 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Mon, 24 Sep 2018 21:07:48 +0300
Subject: [PATCH 63/78] Rebuild vignettes.

---
 inst/doc/chisq_test.R          | 28 +++++++++-------------------
 inst/doc/chisq_test.Rmd        | 30 +++++++++---------------------
 inst/doc/chisq_test.html       | 24 ++++++++++--------------
 inst/doc/flights_examples.R    |  6 ++----
 inst/doc/flights_examples.Rmd  |  6 ++----
 inst/doc/flights_examples.html |  6 ++----
 inst/doc/two_sample_t.R        | 32 ++++++++++----------------------
 inst/doc/two_sample_t.Rmd      | 34 ++++++++++------------------------
 inst/doc/two_sample_t.html     | 30 ++++++++++++------------------
 9 files changed, 66 insertions(+), 130 deletions(-)

diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
index e93599f9..8f338c9f 100644
--- a/inst/doc/chisq_test.R
+++ b/inst/doc/chisq_test.R
@@ -37,46 +37,36 @@ obs_chisq <- fli_small %>%
   chisq_stat(formula = origin ~ season)
 
 ## ------------------------------------------------------------------------
-chisq_null_distn <- fli_small %>%
+chisq_null_perm <- fli_small %>%
   specify(origin ~ season) %>% # alt: response = origin, explanatory = season
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>%
+chisq_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
-chisq_null_distn %>% 
+chisq_null_perm %>% 
   get_p_value(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
-fli_small %>%
+chisq_null_theor <- fli_small %>%
   specify(origin ~ season) %>% 
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "Chisq") %>%
+  calculate(stat = "Chisq")
+chisq_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 
-## ----eval=FALSE----------------------------------------------------------
-#  fli_small %>%
-#    specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-#    hypothesize(null = "independence") %>%
-#    generate(reps = 1000, type = "permute") %>%
-#    calculate(stat = "Chisq") %>%
-#    visualize(method = "both") +
-#      shade_p_value(obs_stat = obs_chisq, direction = "right")
-
-## ----echo=FALSE----------------------------------------------------------
-# To use same distribution calculated above
-chisq_null_distn %>% 
+## ------------------------------------------------------------------------
+chisq_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ------------------------------------------------------------------------
 fli_small %>% 
   chisq_test(formula = origin ~ season) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
index 0f7ea2f0..1cb2f535 100644
--- a/inst/doc/chisq_test.Rmd
+++ b/inst/doc/chisq_test.Rmd
@@ -87,12 +87,12 @@ Lastly, the observed $\chi^2$ statistic is `r obs_chisq`.
 ## Randomization approach to $\chi^2$-statistic
 
 ```{r}
-chisq_null_distn <- fli_small %>%
+chisq_null_perm <- fli_small %>%
   specify(origin ~ season) %>% # alt: response = origin, explanatory = season
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_distn %>%
+chisq_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
@@ -100,7 +100,7 @@ chisq_null_distn %>%
 ## Calculate the randomization-based $p$-value
 
 ```{r}
-chisq_null_distn %>% 
+chisq_null_perm %>% 
   get_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
@@ -108,41 +108,29 @@ chisq_null_distn %>%
 ## Theoretical distribution
 
 ```{r }
-fli_small %>%
+chisq_null_theor <- fli_small %>%
   specify(origin ~ season) %>% 
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "Chisq") %>%
+  calculate(stat = "Chisq")
+chisq_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
 
-```{r eval=FALSE}
-fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "Chisq") %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
-```
-
-```{r echo=FALSE}
-# To use same distribution calculated above
-chisq_null_distn %>% 
+```{r}
+chisq_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
-
 ## Compute theoretical p-value
 
 ```{r}
 fli_small %>% 
   chisq_test(formula = origin ~ season) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 ```
 
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
index 0f324cff..4f3e1658 100644
--- a/inst/doc/chisq_test.html
+++ b/inst/doc/chisq_test.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-09-14" />
+<meta name="date" content="2018-09-24" />
 
 <title>Chi-squared test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Chi-squared test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-14</em></h4>
+<h4 class="date"><em>2018-09-24</em></h4>
 
 
 
@@ -174,19 +174,19 @@ <h2>Calculate observed statistic</h2>
 </div>
 <div id="randomization-approach-to-chi2-statistic" class="section level2">
 <h2>Randomization approach to <span class="math inline">\(\chi^2\)</span>-statistic</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-chisq_null_distn <span class="op">%&gt;%</span>
+chisq_null_perm <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <p><img 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AIAAKZDAAIAAKbj5TtBa5q2c+fOffv2ORyOfv36Pf744/q3SBYWFu7YsaOmpiY1NTUzM9NqtTbXCAAA4GtePgJ05MiRgoKCrVu3vvjii+Hh4a+//roQorq6etOmTUuWLNm+fXtFRcWePXuaawQAAPADLwegHj16LFq0KDAwUNM0m82mfwl5UVGR3W632+2KokyePPnAgQPNNQIAAPiBl0+BxcbGCiH27du3efPmqKgo/RuaysrKoqOj9RViYmLKy8uba9SVlZXNmzdPfzxjxoxG3515VWp4+23XuXPuRclms5rgVQMAYBSffBv86NGjhw4d+uabb27evPnJJ59s9HVjLpdLCNFko85ms40fP15/nJCQUFNT44sivcJisciyXFdX18Ht1K9b5/r4Y/ei1K2bPGREB7fZdSmK4nQ6ja7iqiLLsizLDofD6EKuKuyoXseO6guyLOvf/Wl0IYbRNM2SknBlu5cDUF5enizLY8aMCQoKGjVq1KpVq4QQkZGRx44d01coLy+PjIxsrlEXGhr66KOPuhc9Dw51NiEhIaqqVlVVdXA7stMpeSxqmjDzRwCfgF6nqqokSYyqdzGkXqeqKr/+XmexWBwOh5kDkBDC0lSjl68Bstlsu3btqq2t1TTt4MGDAwYMEEKkpaWVlJScPXtW07Tc3Nz09PTmGgEAAPzAy0eAbrnlljNnzixdurS+vr5fv37z588XQgQFBS1evDg7O9vhcCQnJ0+dOrW5RgAAAD+QOv9hsc5/CqyioqKD25GnTJE++eRfyxHdAvcd6uA2uy6r1VpfX290FVcVVVUVRen4xWrwZLFYGhoajK7iqqKqqqqqtbW1RhdyVeEUmBDCNnjAlY3cCRoAAJgOAQgAAJgOAQgAAJiOT+4DhLYLDAyUZbleUVwejZIkVNV/bw2TTgEAZsMRICMpihIcHCxJhl2KLkmSfocYQ3oHAMAoHAEykp48NmzYMOnUqf4e7Q0NDfv37/dDAVFRUYMGDfJDRwAAdCocAQIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKajGl1A60JDQ40uoVmqqsqy3O4KJUnybj3tY7FYNE0zuop/kSTJYrEYXcVVRZZlRtXrZFlmSL1L/0hkVL1L31E71Ye8nzX32rtAALp8+bLRJTQrJCREVdV2V6iqqtVq9W5J7dDQ0NCpfjesVmtDQ4PRVVxVVFVVFIVR9S6LxcKQepf+LyWj6l0Wi8XhcHSqD3n/azLrcAoMAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYThf4Nnif0r8lu90/riiKJEkBAQHt+3FZJoACAGAAswegiIiIjm8kNDS04xsBAAB+Y+oAJEmSEOK1117729/+ZkgBgwcPfuCBBwzpGgAAMzN1ANJdvnz5u+++M6TrixcvGtIvAAAmxzUoAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdJgGDyHLsqZpRvWuaZqBvQMAzIkAZGr6PawtFouxZdTW1hpbAADAbAhApqbfC/vo0aP19fWGFBAbG9u7d29DugYAmBkBCKKysrKurs6Qrrt162ZIvwAAk+MiaAAAYDrePwL04Ycf7tq1q7KyMi4ubsGCBT179hRCFBYW7tixo6amJjU1NTMz02q1NtcIAADga14+AvTll19u3759+fLlO3bsSEpKysnJEUJUV1dv2rRpyZIl27dvr6io2LNnT3ONAAAAfuDlAFReXn7bbbfFxsaqqjpmzJhvvvlGCFFUVGS32+12u6IokydPPnDgQHONAAAAfuDlU2CpqampqalCiPr6+v/5n/8ZOXKkEKKsrCw6OlpfISYmpry8vLlGXVlZ2bx58/THM2bMmDVrlneLRGcTEBDguShJUqMWdJA+3Y9R9S5JkmSZyyi9jx3Vu9hRXS5Xk+0+mQV26NChl156adiwYXPmzBFCNLrNnV5Kk406m802fvx4/XFCQoJPZ2gHBgb6buNoI6fT6bmoKEqjFnSQLMuyLDOq3sWO6nWyLEuSxKh6l36rWzPfb1bTNKWpdi8HIE3TXnjhhdLS0uXLl7vv7xIZGXns2DH9cXl5eWRkZHONutDQ0EcffdS96HlwyLskSSIAdQYOh8NzUZblRi3oIFVVJUliVL2LIfU6VVX59fc6i8XicDjMHICEEE3e7dfLh8WKioqKi4vXrl3reXe7tLS0kpKSs2fPapqWm5ubnp7eXCMAAIAfePkI0PHjx8+cOZORkaEvBgUF7dy5MygoaPHixdnZ2Q6HIzk5eerUqfpTVzYCAAD4gZcD0OzZs2fPnn1lu/vi6FYbAQAAfM3UV4YDAABzIgABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTaSUAjR8/3ul0eracO3du+vTpviwJAADAt9QmWw8ePHjw4EEhxN69e7OzsxVFcT916tSpAwcO+Kk6AAAAH2g6AJ06derPf/6z/jg3N1eSJPdTsixv3LjRH6UBAAD4RtMBaM6cOXPmzBFCDBkyZO/evara9GoAAABdUSvJ5vDhwzU1NV988UWj9oSEBF9VBAAA4GOtBKBdu3bdd999dXV1jdo1TfNZSQAAAL7VyiywrKysH/zgB3//+9/L/p1/igMAAPCFVo4AXbhwYfny5QMGDPBPNQAAAH7QyhGgsWPHFhcX+6cUAAAA/2jlCNBPf/rT+fPnHz9+PC0tLTAw0N0+fvx4HxcGAADgK60EoLFjxwohsrOzG7XX1tb6qiIAAAAfayUAEXQAAMDVp5UAVF5e3mR7ZGSkD4ppWnh4uN/6giGsVqvnoizLjVrQQZIkSZLEqHoXQ+p1+rcOMKreJUmSxWIxugojuVyuJttbCUBRUVFNtvvzPkAXL1700ZYlSbrmmmt8tHG0XX19veei1Wpt1IIOUlVVURRG1bssFktDQ4PRVVxVVFVVVZUd1bssFovD4TD53fuazDqtBKCSkhL345qamoKCgl/84hevvPKKVwsDAADwq1YCUKOvvBg0aFBUVNRjjz22d+9eX1YFAADgQ63cB+hKPXr0yM/P90UpAAAA/tHKEaDjx497Ll64cGHNmjVxcXG+LAkAAMC3WglAgwYNatQSGRnJNUAAAKBLayUAXb58uVFLcHCwPlMRAACgi2olAIWEhLhcrgMHDhQXFzscjpSUlNGjRxOAAABAl9ZKAPruu+8mT55cVFQUHx8vy3JpaWlqaup7773X3P2BAAAAOr9WZoEtXLhQVdXTp0+fPn361KlTp0+fliRp0aJF/ikOAADAF1o5ArR3797XX389Pj5eX4yPj1+3bt0999zj+8IAAAB85XvfBwgAAKCrayUAjRs3Lisr68yZM/ril19+mZWVNW7cON8XBgAA4CutBKDNmzcLIfr375+QkJCQkGC324UQOTk5/igNAADAN1r/Nvi//e1v+jR4IURSUtKoUaOYBg8AALq0VgJQfX39M888c/78+a1btwohbrzxxjFjxjzzzDNBQUF+KQ9XOavVKoSwWCyejZIkNWrxKZfL5XQ6/dYdAKAzaCUALVu2bOfOnevWrdMXMzMz16xZ43K59FNjQAfpSbq6utqoAgICAlRVJQABgNm0EoB27dr13HPPzZo1S1+cP3++3W6///77CUDwFpfLVVBQYFTvAwYM6NGjh1G9AwCM0spF0FVVVf369fNsCQkJqaur82VJAAAAvtVKABo7duzKlSvPnz+vL1ZUVKxevXrMmDE+rwsAAMBnWjkFtnXr1gkTJsTFxQ0cOFBV1RMnTvTq1SsvL88/xQEAAPhCKwEoJiamsLBwz549x48fb2hoyMrKmjp1qj9n6AAAAHhdKwFICKEoypQpU6ZMmeKHagAAAPyA7wIDAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACm45MA5HQ6MzMzL1265G4pLCxcsGDBAw88sHXr1vr6+hYaAQAAfM37AWjPnj1PPPHEuXPn3C3V1dWbNm1asmTJ9u3bKyoq9uzZ01wjAACAH3g/APXp0ycjI8PzbtFFRUV2u91utyuKMnny5AMHDjTXCAAA4Aet3wn6+0pJSRFCKIribikrK4uOjtYfx8TElJeXN9foXn/evHn64xkzZsyaNcvrRQJusiwHBAQYXYVvSZIkhLjqX6afSZIky1xG6X3sqN7FjupyuZps934AupKmaVeW0mSjLiAgYNiwYfrjuLi4hoYG39XmGdRgWs39elw19E/Aq/5l+hlD6nWSJEmSxKh6Fzuqy+Vq8i+9PwJQZGTksWPH9Mfl5eWRkZHNNerCwsKWLVvmXvQ8OORdkiTZbDYfbRxdhcvl8mnI7gxUVZUk6ap/mX5msVgYUu9SVVWWZUbVuywWi8PhaHTQwWya/Ap3fxwWS0tLKykpOXv2rKZpubm56enpzTUCAAD4gT+OAAUFBS1evDg7O9vhcCQnJ0+dOrW5RgAAAD/wVQD6/e9/77mYmpqampraaJ0mGwEAAHzN1FeGAwAAcyIAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA01GNLgAwkqIokiQpimJgDS6XS9M0AwsAABPqAgEoPDzc6BJw1QoNDZUkyWKxGFiDpmm+DkCSJEmSZLVafdqL2TCkXidJkhCCUfUuwz/iDOdyuZps7wIB6OLFiz7asiRJ11xzjY82jq6iqqrq008/Nar3wYMHh4WF1dfX+7QXVVUVRfF1L2ZjsVgaGhqMruKqoqqqqqrsqN5lsVgcDofJDzM3mXW6QAACfErTNIfDYWDvRnUNAGbGRdAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0uAgaMFJQUJAsyzabzQ99NddLQ0OD0+n0QwEA0HkQgAAjKYpSWVl57tw5owro27evfvMVADAVAhBgsJqami+//NKo3uPi4lSVzwEApsM1QAAAwHQIQAAAwHQIQAAAwHQIQAAAwHS4+BEwNUVRZFk29sui+aZGAP5HAAJMTVXVhoaGuro6owoIDQ11uVzciAiAnxGAALM7e/bs559/bkjXkiSNHTvWkK4BmBzXAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANPhRogADCZJkiwb9s+Ypml8EQdgQgQgAIaRJEkIoaoGfxA5nc52ZCBZlr1VucPh8Mp2ALQdAQiAYfQA9MUXX5SVlRlSQERERGJiotPpbMeXkTU0NHS8AEVRrFary+Xq+KbajWNgMCcCEACD1dbWXr582ZCuAwIChBDHjx+vqKgwpIC+ffva7Xar1WpI7261tbXGFgD4HwEIAAyjX/x09OjR+vp6QwqIjY3t3bu3IV0DxjIyABUWFu7YsaOmpiY1NTUzM9Pw/4EAwBCVlZV1dXWGdN2tWzdD+gUMZ1gAqq6u3rRp06pVq+Lj49euXbtnz55p06YZVQwAmFNgYKD456lAo7hcLv0aLP2aMP9PCTT2GiwYxbAAVFRUZLfb7Xa7EGLy5Mm/+93vCEAA4GcBAQGapp09e9aoAqKioiwWi6Io7hb/nw1wOp1euaS9i1IURY+eBjJkIqRhAaisrCw6Olp/HBMTUxHK0moAAAdASURBVF5e7n7q/PnzCxcu1B9PmTJlxowZPq1k+vTp48eP92kXzQkODhZCzJs3T337beExC0ZVLUOHDvVDAfq/fddff71Rc0ACAwNlWfbPi22uACGEgQUoitKtWzcDC5AkqUePHt27dzeqACFE3759e/XqZUjX+jz2pKSkdswC84rO8DsoSVJoaKghvQshPKOPgTV0hjJ8p/O/OkVRfPcr0NwRPsMCUKOX6lmfxWJJTk7WH0dFRfk6GHbr1q3dZ8FlWZYkqSMfnZqmRUdHVwUEeG5CUpWgG5Lbvc3vxSVJgYbOgHVJUtC/FyDLsv+OSEuSECLIuBHQJEn2fQH6nQab3FFdkqQKoRg3Ai5JsmiaxajuhdBkOaBd+5u3dtRO+DvoV5Lk+udfhBZ2VF/2L4kr/iRdTQW0uqNKkmT4fRB8egTO4XCENNVuWACKjIw8duyY/ri8vDwyMtL9VFhY2LJly9yLngeHOpuQkBBVVTs+gVZ2ODyPP7pcrgsXLnRwm11XWFjYpUuXjK7iqhIcHGy1Ws28U/lCaGhoZWWl4X85riZBQUGBgYHsqN4VEhJSXV1t8uucQkKaiECG3X4+LS2tpKTk7Nmzmqbl5uamp6cbVQkAADAbw44ABQUFLV68ODs72+FwJCcnT5061ahKAACA2Rh5H6DU1NTU1FQDCwAAAOZk2CkwAAAAoxCAAACA6RCAAACA6RCAAACA6Rh/+6MuLS8v79tvv73nnns6uJ36V191ffWVe1EKDAx47LEObhNwO3jw4OnTp2fPnm10IUBL8vPzjx49+pOf/MToQmAKRs4CuwocOnToxIkTHQ9A1lmzvFIP0KTDhw/n5eURgNDJHTly5PXXXycAwT84BQYAAEyHAAQAAEyHa4A65MKFCw0NDe6vtQc6p4sXL9bU1MTGxhpdCNCSS5cuVVVV9ejRw+hCYAoEIAAAYDqcAgMAAKbDLLD2Kyws3LFjR01NTWpqamZmptVqNboioAlLliwpLS2VJEkIMXHiRKbYoFNxOp3z589/9tlnw8LC9BY+WuEfBKB2qq6u3rRp06pVq+Lj49euXbtnz55p06YZXRTQmKZpZ8+efe211/grgk5oz549eXl5586dc7fw0Qq/4RRYOxUVFdntdrvdrijK5MmTDxw4YHRFQBPKysqEEBs3bszMzMzJybl8+bLRFQH/0qdPn4yMDIvF4m7hoxV+QwBqp7KyMvfkr5iYmPLycmPrAZp08eLFpKSkuXPnbt26VQixfft2oysC/iUlJWXo0KGKorhb+GiF3xCA2qnR7DmXy2VUJUALEhMTV6xYER0dbbVa77333vz8fCZ+ojPjoxV+QwBqp8jISPe/JuXl5ZGRkcbWAzTp9OnTn3/+uf5YVVXPf7WBToiPVvgNAaid0tLSSkpKzp49q2labm5uenq60RUBTbh8+fLatWsvXLigadof//jH4cOH69PBgM6Jj1b4DTdCbL/CwsKXX37Z4XAkJyc/+OCDntfxAZ2EpmnvvPPOu+++q2nawIEDMzMzg4KCjC4K+DcZGRm/+c1vPKfB89EKPyAAAQAA0+EUGAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEIAu6be//e3Bgwc7vg4AcyIAAeiSCEAAOoIABAAATIcABKCz27179/XXXx8YGBgfH//ss88KIYYMGbJv374nn3xy/PjxQojPPvtsypQpUVFRoaGhI0eOLCwsvHIdAPDEd4EB6NRKS0sTExMXLVp011137d+//6c//emBAwduvvnmcePGTZgw4YknnlAUJSkpqUePHk8//bQQYtWqVVVVVZ9++qnD4fBcx+jXAaBzUY0uAABacvr0aafTmZmZmZiYOHTo0OTk5Li4OFVVJUlSFEVRFJfL9dBDD91xxx12u10I8c033yxatEgI4bmO0S8CQKdDAALQqY0YMWLq1KnXX3/9pEmTxo0b96Mf/SgsLMxzBVmW58+fv3///t27d3/66ae5ubmSJBlVLYCugmuAAHRqNpvt7bffPnbsWGpq6quvvtq7d++3337bc4XKysoRI0YsXbq0oaFhwYIF27ZtM6pUAF0IAQhAp5aXl7ds2bL+/fs/9dRTH3300YQJE1566SXPFT788MPi4uKCgoInn3wyPT3d6XQaVSqALoRTYAA6NUVR1q5dGx4e/h//8R/FxcWHDh3KzMwUQsiyfOrUqXPnzoWFhVVXV7/yyisTJ04sKCh4+umnq6qqvv766169ernXiY2NNfp1AOhcmAUGoLPbsmVLTk7O119/HRMTc/fdd69Zs8Zqtb788stLlixJT0//wx/+8POf/3zbtm36tK81a9bceeedQUFB+fn57nXeeusto18EgM6FAAQAAEyHa4AAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDp/D9odEhDAl6mLgAAAABJRU5ErkJggg==" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
@@ -205,11 +205,12 @@ <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-val
 </div>
 <div id="theoretical-distribution" class="section level2">
 <h2>Theoretical distribution</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_theor &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
+chisq_null_theor <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
@@ -218,11 +219,7 @@ <h2>Theoretical distribution</h2>
 </div>
 <div id="overlay-appropriate-chi2-distribution-on-top-of-permuted-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution on top of permuted statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
@@ -233,8 +230,7 @@ <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution
 <h2>Compute theoretical p-value</h2>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">chisq_test</span>(<span class="dt">formula =</span> origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(p_value) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>()</code></pre></div>
+<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(p_value)</code></pre></div>
 <pre><code>## [1] 0.7513</code></pre>
 </div>
 </div>
diff --git a/inst/doc/flights_examples.R b/inst/doc/flights_examples.R
index f72fc849..0d8022e1 100644
--- a/inst/doc/flights_examples.R
+++ b/inst/doc/flights_examples.R
@@ -190,8 +190,7 @@ null_distn %>%
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 null_distn <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    hypothesize(null = "independence") %>%
@@ -262,8 +261,7 @@ c(lower = d_hat - 2 * sd(boot),
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 boot <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    generate(reps = 1000, type = "bootstrap") %>%
diff --git a/inst/doc/flights_examples.Rmd b/inst/doc/flights_examples.Rmd
index 07f3f82c..059d2da2 100644
--- a/inst/doc/flights_examples.Rmd
+++ b/inst/doc/flights_examples.Rmd
@@ -259,8 +259,7 @@ null_distn %>%
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 null_distn <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    hypothesize(null = "independence") %>%
@@ -348,8 +347,7 @@ c(lower = d_hat - 2 * sd(boot),
 slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
   broom::tidy() %>% 
   filter(term == "dep_delay") %>% 
-  select(estimate) %>% 
-  pull()
+  pull(estimate)
 boot <- fli_small %>%
    specify(arr_delay ~ dep_delay) %>% 
    generate(reps = 1000, type = "bootstrap") %>%
diff --git a/inst/doc/flights_examples.html b/inst/doc/flights_examples.html
index 70b57a27..084b8af7 100644
--- a/inst/doc/flights_examples.html
+++ b/inst/doc/flights_examples.html
@@ -343,8 +343,7 @@ <h3>Two numerical vars - SLR</h3>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">slope_hat &lt;-<span class="st"> </span><span class="kw">lm</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay, <span class="dt">data =</span> fli_small) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span>broom<span class="op">::</span><span class="kw">tidy</span>() <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">filter</span>(term <span class="op">==</span><span class="st"> &quot;dep_delay&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(estimate) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>()
+<span class="st">  </span><span class="kw">pull</span>(estimate)
 null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
@@ -432,8 +431,7 @@ <h3>Two numerical vars - SLR</h3>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">slope_hat &lt;-<span class="st"> </span><span class="kw">lm</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay, <span class="dt">data =</span> fli_small) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span>broom<span class="op">::</span><span class="kw">tidy</span>() <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">filter</span>(term <span class="op">==</span><span class="st"> &quot;dep_delay&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(estimate) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>()
+<span class="st">  </span><span class="kw">pull</span>(estimate)
 boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
diff --git a/inst/doc/two_sample_t.R b/inst/doc/two_sample_t.R
index 69ce6fd4..ff907dfa 100644
--- a/inst/doc/two_sample_t.R
+++ b/inst/doc/two_sample_t.R
@@ -29,51 +29,40 @@ obs_t <- fli_small %>%
 obs_t <- fli_small %>% 
   t_test(formula = arr_delay ~ half_year, alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(statistic) %>% 
-  dplyr::pull()
+  dplyr::pull(statistic)
 
 ## ------------------------------------------------------------------------
 obs_t <- fli_small %>% 
   t_stat(formula = arr_delay ~ half_year, order = c("h1", "h2"))
 
 ## ------------------------------------------------------------------------
-t_null_distn <- fli_small %>%
+t_null_perm <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>%
+t_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
-t_null_distn %>% 
+t_null_perm %>% 
   get_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
-fli_small %>%
+t_null_theor <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "t", order = c("h1", "h2")) %>%
+  calculate(stat = "t", order = c("h1", "h2"))
+t_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
-## ----eval=FALSE----------------------------------------------------------
-#  fli_small %>%
-#    # alt: response = arr_delay, explanatory = half_year
-#    specify(arr_delay ~ half_year) %>%
-#    hypothesize(null = "independence") %>%
-#    generate(reps = 1000, type = "permute") %>%
-#    calculate(stat = "t", order = c("h1", "h2")) %>%
-#    visualize(method = "both") +
-#      shade_p_value(obs_stat = obs_t, direction = "two_sided")
-
-## ----echo=FALSE----------------------------------------------------------
-# To use same distribution calculated above
-t_null_distn %>% 
+## ------------------------------------------------------------------------
+t_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
@@ -82,6 +71,5 @@ fli_small %>%
   t_test(formula = arr_delay ~ half_year,
          alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 
diff --git a/inst/doc/two_sample_t.Rmd b/inst/doc/two_sample_t.Rmd
index c77980bb..a4ef6534 100755
--- a/inst/doc/two_sample_t.Rmd
+++ b/inst/doc/two_sample_t.Rmd
@@ -69,8 +69,7 @@ Or using `t_test` in `infer`
 obs_t <- fli_small %>% 
   t_test(formula = arr_delay ~ half_year, alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(statistic) %>% 
-  dplyr::pull()
+  dplyr::pull(statistic)
 ```
 
 The observed $t$ statistic is `r obs_t`.
@@ -87,13 +86,13 @@ The observed $t$ statistic is `r obs_t`.
 ## Randomization approach to t-statistic
 
 ```{r}
-t_null_distn <- fli_small %>%
+t_null_perm <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_distn %>%
+t_null_perm %>%
   visualize() +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
@@ -101,7 +100,7 @@ t_null_distn %>%
 ## Calculate the randomization-based $p$-value
 
 ```{r}
-t_null_distn %>% 
+t_null_perm %>% 
   get_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
@@ -109,37 +108,25 @@ t_null_distn %>%
 ## Theoretical distribution
 
 ```{r }
-fli_small %>%
+t_null_theor <- fli_small %>%
   # alt: response = arr_delay, explanatory = half_year
   specify(arr_delay ~ half_year) %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
-  calculate(stat = "t", order = c("h1", "h2")) %>%
+  calculate(stat = "t", order = c("h1", "h2"))
+t_null_theor %>%
   visualize(method = "theoretical") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
 
-```{r eval=FALSE}
-fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("h1", "h2")) %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-```{r echo=FALSE}
-# To use same distribution calculated above
-t_null_distn %>% 
+```{r}
+t_null_perm %>% 
   visualize(method = "both") +
     shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
-
 ## Compute theoretical p-value
 
 ```{r}
@@ -147,7 +134,6 @@ fli_small %>%
   t_test(formula = arr_delay ~ half_year,
          alternative = "two_sided",
          order = c("h1", "h2")) %>% 
-  dplyr::select(p_value) %>% 
-  dplyr::pull()
+  dplyr::pull(p_value)
 ```
 
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
index 94ebc223..c115c4a5 100644
--- a/inst/doc/two_sample_t.html
+++ b/inst/doc/two_sample_t.html
@@ -12,7 +12,7 @@
 
 <meta name="author" content="Chester Ismay" />
 
-<meta name="date" content="2018-09-14" />
+<meta name="date" content="2018-09-24" />
 
 <title>Two sample t test example using nycflights13 flights data</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Two sample <span class="math inline">\(t\)</span> test example using <code>nycflights13</code> <code>flights</code> data</h1>
 <h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-14</em></h4>
+<h4 class="date"><em>2018-09-24</em></h4>
 
 
 
@@ -135,8 +135,7 @@ <h2>Calculate observed statistic</h2>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">t_test</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year, <span class="dt">alternative =</span> <span class="st">&quot;two_sided&quot;</span>,
          <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(statistic) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>()</code></pre></div>
+<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(statistic)</code></pre></div>
 <p>The observed <span class="math inline">\(t\)</span> statistic is 0.8685.</p>
 <p>Or using another shortcut function in <code>infer</code>:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
@@ -160,21 +159,21 @@ <h2>Calculate observed statistic</h2>
 </div>
 <div id="randomization-approach-to-t-statistic" class="section level2">
 <h2>Randomization approach to t-statistic</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># alt: response = arr_delay, explanatory = half_year</span>
 <span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
 <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_distn <span class="op">%&gt;%</span><span class="st"> </span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <div class="kable-table">
 <table>
@@ -193,27 +192,23 @@ <h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-val
 </div>
 <div id="theoretical-distribution" class="section level2">
 <h2>Theoretical distribution</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_theor &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># alt: response = arr_delay, explanatory = half_year</span>
 <span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
+<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_theor <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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JDLW+mu+yMlSZKiKHT02lccDoeiKGzf+kpN2pteZwNVXACZm5vn5uYyjysrK83NzduPzc7Ozs/Pf+WVV+RyeW1t7csvv7xt27YhQ4YQBKGnp+fr69s2ZUsNmz1BEwRBkiQKoL5iyl+st36gKKqX602Lt79NTU3nz59PTExMSEgoLCwkCILH4zk7O48YMSIqKsrV1XXo0KGiPxkbG7f1BN3U1NTQ0FBfX9/Q0FBTU1NQUHDz5s3r16+fPn2aKZtMTU1DQkLCwsLCwsKGDh3K9gtVMUqh6HB0Qamkya6bE0VRNE3jc9pX+GHcP8zxL/VcbyougLy8vP71r3+VlpaKxeK4uLjAwEBmeHFx8dChQ1evXt32dOPGjd9++61qlw4AGufWrVvx8fGJiYkXL16UyWTW1tZhYWGTJk0aNWqUk5NTbw5mGRgYGBgYiMVi5unkyZOZBzRNFxYW3rhxIz09PTEx8b///S9JkqNHjw4LC4uIiPD29tbm3UIA0BMVF0BCoXDNmjVbtmyRy+UjR46cPn06M3zFihUff/zxqFGjVLs4ANBQTU1Nv/zyy4EDBzIzM/X09Pz8/NavXx8WFjZy5EhVLYIkSXt7e3t7+6ioqI0bN5aUlDC7l/7zn//s2rXL3t7+hRdemDdvXlvlBAA6hVTPy9FxM1RNhJuh9o+u3Qw1MzPzwIEDx48fb2pqmjhx4gsvvBAdHW1gYNDX+fT7ZqhyuTw1NfXgwYOnTp1SKpXh4eELFiwIDw/X0POCqUOHcDPUQYCbofaPOt8MVSM/8ACgcVpbW48dO/bNN9/k5+dbWFi89NJL8+fPZ7rMGGRcLjckJCQkJKS6uvrIkSMHDhx44YUXxGLx4sWLX3nlFSMjo8GPBACDD4fAAWBgtba2/vjjj/7+/q+//rpYLP7hhx9ycnLee+89Vqqf9kxNTV977bXU1NRTp06FhITs2LHD09Nz69atdbpxqR2AjkMBBAADpbW19aeffgoMDFy1apWDg8OZM2eOHDkSExOjbv30+Pj4fPHFF5cvX547d+7nn38+bty4Dz/8sKamhu1cADCAUAABgOoplcoDBw5MmDBh5cqVLi4u8fHxx44d8/T0ZDtXd4YNG/bxxx9funRp7ty5u3fv9vLy2rx5c2NjI9u5AGBAoAACABXLyMiIiIhYvXq1u7v72bNnDxw4MG7cOLZD9dawYcM++eSTjIyMZ5999ssvv/T39z927Jh6XiwCAE8CBRAAqEx1dfX69eunTZvW3Nx85MiR/fv3jxkzhu1Q/WFtbb158+YLFy6MGTNm2bJlM2fOvH79OtuhAECVUAABgArI5fJ9+/b5+/sfPHhw7dq1KSkpoaGhbId6Ug4ODgcPHjxw4EBJSUloaOjy5curq6vZDgUAqoECCACeVGZmZlhY2Lp16yIjIzMyMt566y09vU7vvaORoqKiLly48Oabb/76668BAQHHjh1jOxEAqAAKIADov9bW1o8//njatGkURf3+++9ffPGFhYUF26FUj8/nr1u3Li0tzcfHZ9myZUuWLMGuIABNhwIIAPrp5s2b0dHRn3/++d/+9re4uDgfHx+2Ew0sW1vbffv27d2798KFCwEBAbGxsWwnAoD+QwEEAH1G0/S+ffsiIiJqamp++eWXDRs2aNMxr+7NmDHj/Pnz48ePX7hw4UsvvdSPe3EAgDpAAQQAfVNYWDhjxox169Y999xzqamp/v7+bCcabJaWlgcPHty8eXN8fHxYWFhaWhrbiQCgz1AAAUAf/P7776GhoYWFhT/99NMnn3yir6/PdiJ2kCT58ssvJyUlWVhYPP3009u3b1cqlWyHAoA+QAEEAL0il8s3bdq0ZMkSPz+/5ORkLbjK/ck5OTmdPHly+fLln3zyyfz583FmNIAGQQEEAD2rqqp69tlnv/nmm3Xr1u3fv3/IkCFsJ1IXXC53w4YNBw4cYPoCuHLlCtuJAKBXUAABQA8uXrwYHBycl5d36NCht956i6Kw3egoMjKSORw2c+bMf//732zHAYCeYUMGAF2iafrLL7986qmn7OzscNirezY2Nr/++uucOXPefffd5cuXSyQSthMBQHdQAAFA56RS6WuvvbZp06YXX3zxxIkTVlZWbCdSdwKB4LPPPvvss89OnDgxderU0tJSthMBQJe4bAfoHLt9ipAkSRAEh8NhMYMmIkmSJEnd6Q9GhUiS7OVxJWNj44EOwygvL58zZ052dvZ33333/PPPD85C+4TD4ZAkOWgrpPeWLVvm4+Mze/bsqKio48ePjx8/fkAXJ9fXb/nrEB6Xy+32Y9j79gZtsH3rN9bbW1dXaKppASSTyVhcOkVRJEkqFAoWM2giHo9HkiS7750moiiKoii5XN6biRvr6gY6D0EQN27cmD9/fmNj45EjRyZOnFg3KAvtK0NDQy6Xq57ZXFxc4uLiXnjhhdDQ0K+//nrq1KkDtyxKIiH/OqRVLld2/THsU3uDNlwul8PhYPvWVyRJcjgc1ttbp7UOfgQAwF8kJydPnTqVy+WeOnVq4sSJbMfRVGKx+MSJE8HBwUuWLNm6dSvbcQCgIxRAAPD/9u/fP3/+/HHjxsXHxzs7O7MdR7MZGBh89913K1eu3LZt26pVq7DzAECtoAACAIIgCKVS+d57761Zs2bevHmHDx9Ww3NrNBGHw9mwYcO2bduOHDkyb968+vp6thMBwP+gAAIAQiaT/e1vf/v3v/+9cePGHTt28Hg8thNplcWLFx86dCg7O3vWrFkVFRVsxwEAgkABBADNzc0LFy48ceLErl27li9fznYc7RQSEnL8+PHS0tIpU6bcvXuX7TgAgAIIQLfV1tbOmTMnLS2NOfuH7TjabOzYsadPn+ZwONOnT8/Ly2M7DoCuQwEEoLvKyspmzJhRUFBw7NixyZMnsx1H+9nb2588edLCwmLWrFnp6elsxwHQaSiAAHRUQUFBdHR0dXX1iRMnfH192Y6jKywsLI4fPz5y5Mg5c+b89ttvbMcB0F0ogAB0UU5OzrRp0wQCQWxs7KhRo9iOo1uMjY2PHj0aGhq6dOnSw4cPsx0HQEehAALQORkZGbNnz7axsfn9999tbW3ZjqOLBALB999//9RTT61atWrfvn1sxwHQRWp6KwwAGCCXLl2aN2+ek5PTkSNHTE1N2Y6ju7hc7ldffSUSidatWyeRSF599VW2EwHoFhRAADokLS1t/vz57u7uhw8fFolEbMfRdSRJbtmyhcPhvPfee83NzatXr2Y7EYAOQQEEoCsSExMXLVo0bty4Q4cOGRoash0HCIIgSJL86KOPOBzOxx9/LJfL33zzTbYTAegKFEAAOiE+Pn7JkiXe3t4HDx40MDBgOw78P5IkP/zwQwMDg61bt7a0tGzYsIHtRAA6AQUQgPY7efLk0qVLQ0JCvvvuOz6fz3Yc6MTbb79NkuSnn35KEARqIIBBgAIIQMudOnVq6dKlERERe/bs0dPTYzsOdOnvf/87n8//6KOPKIp699132Y4DoOVQAAFos8TExFdeeSUoKAjVj0Z44403eDzexo0beTzeW2+9xXYcAG2GAghAayUlJS1cuDAgIOCHH35A9aMpli9f3tjYuG3bNi6Xu2bNGrbjAGgtFEAA2iklJWXhwoX+/v779+/HeT+a5e9//7tCodi8eTNFUW+88QbbcQC0k+oLoKysrL1790okEk9Pz6VLl7b/3UnT9P79+1NSUuRy+fDhw1etWjVkyBCVBwCAS5cuMVe8//DDD6h+NNH69euVSiVzPtDrr7/OdhwALaTiW2E0Nzfv3Llz7dq1e/bsqa2tjY2NbT82JycnIyPjyy+//M9//mNsbPzTTz+pdukAQBBERkbGs88+y/R2KBQK2Y4D/fTee++tXLnyn//857fffst2FgAtpOI9QNnZ2Y6Ojo6OjgRBxMTE/PjjjzNnzmwba2VltXr1an19fblcLhAI2ndEq1QqGxsbmces/2AlSZIkSXYzAHSqx5aZmZk5d+7c0aNH//TTT1rf34/Wf1Tff//9pqam9evXCwSCBQsWsB0HoM/U+UOq4gKooqLCwsKCeWxpaVlZWdl+rFgsJggiJSXls88+Gzp06M6dO9tGlZeXT5s2jXm8aNGilVEzVBusH7hcnCDVHwKBgO0IGqmX7U1gZtbN2Nzc3Hnz5o0aNSouLk5H7nRh1u0K0QLffvstSZJr1661tLR87rnnHp9AZmjY9Nchenp6vJ4+hti+9Q+2b/3D4XBYXLpSqex0uIo/AzRN97jU4OBgHx+fn3/++bPPPnvnnXeYgUZGRuvXr2ceu7q6tra2qjZYn1AURXS9yqArHA6HJEm5XM52EA3D/ELqZXuT/rmj9HF3796NioqysrI6evQoSZKNXU+pHfh8PofDaW5uZjvIgNuxY0ddXd2CBQuUSmX7feoMWirtMEQhlxNdb0JJkqQoSqFQqD6oVuNwOBRFsfvdpInUob3RNN3pRbAqLoDMzc1zc3OZx5WVlebm5u3HJiYmUhQVEhIiFAqDgoI2bdrUNkooFD799NNtT1tyb6o2WJ/QNE2SJDYQfcUUjlhvfUVRVO83EC0tLZ0OLykpmTZtmr6+/pEjR4RCYVeTaRMul0uSpC68UoIgPv/88/r6+hdffPHAgQOhoaHtR1GtrR0OMCiUSqLr5oTPaf8wP1Sw3vqKOf6lnutNxSdBe3l5FRQUlJaW0jQdFxcXGBjIDC8uLpZKpQKB4OjRoy0tLTRNp6amjhgxQrVLB9BNlZWVc+bMUSgUP//8s6WlJdtxQPV4PN7evXt9fX0XLVp06dIltuMAaAMVF0BCoXDNmjVbtmxZsWKFvr7+9OnTmeErVqy4c+eOv79/YGDgunXrXn311cLCwldffVW1SwfQQXV1dXPnzq2urj569KitrS3bcWCgCASCH3/8ccyYMfPnz2/b0Q4A/UZ2OGtHTbB7CIyiKOzq7Acej0eSpEwmYzuIhmEOgfXy3KlG67+c89vc3PzMM8/cuHHjl19+GTNmzMAEVFOGhoZcLre2tpbtIIOqvr5+1qxZJSUlv/76q6urK0EQ1KFD5F87CtLb9RUnNLyrOfSpvUEbLpfL4XCkj51xBd0jSZLD4bDe3gRjOjnipOI9QAAwaKRS6fPPP3/t2rWffvpJ16ofnWVkZHT48GFjY+O5c+c+fPiQ7TgAGgwFEIBGUigUr7766uXLl/ft2+ft7c12HBg8FhYWx44do2n6mWeeqa6uZjsOgKZCAQSgeWiaXrduXWxs7DfffBMUFMR2HBhstra2v/zyS01Nzbx583TkOjgAlUMBBKB5Pvzww4MHD27btm3GDPa7DAVWODo6Hjx48NatW3v27GE7C4BGQgEEoGG+/fbbL774Yv369QsXLmQ7C7DJ09Nz//799+7dYzsIgEZCAQSgSY4ePbp+/fqXX375jTfeYDsLsG/SpEmLFi1iOwWARkIBBKAxiouLX3/99dmzZ3/00UdsZwF1MXbsWLYjAGgkFEAAmqG8vDwlJSUsLOyLL75g7mYAAAD9hs0ogAaora1NTEw0MzPbu3cv7uMNAPDkUAABqLvm5uaEhAShUBgaGioQCNiOA+quBp0DAfQCCiAAtSaVSuPj42maDg8P5/P5bMcBDZB37VpVVRXbKQDUHQogAPUll8sTExMlEkl4eLiBgQHbcUAz6OvrJyQk1NfXsx0EQK2hAAJQU0qlMiUlpbq6OiwsbMiQIWzHAY0xduxYPT29s2fPSiQStrMAqC8UQABq6uLFiyUlJZMmTbKwsGA7C2gSHo8XHh4ul8vj4+NlMhnbcQDUFAogAHWUlZV1+/Ztf39/Ozs7trOA5hGJROHh4Y2NjSkpKUqlku04AOoIBRCA2snPz8/Lyxs/fryzszPbWUBTmZmZhYSEPHr0KDU1laZptuMAqB0UQADq5f79+1euXHF1dR09ejTbWUCzWVtbBwQEPHjw4PLly2xnAVA76FENQI2UlZWlpqba2NhMmDCB7SygDYYPH97U1JSVlWVoaOju7s52HAA1oqYFEI/HY3HpJKYBhg4AACAASURBVEkSBIG7DfQVs8bYfe80EdPeeDxeTU1NcnKyhYVFeHg4h8PpdGKRSDS46dQXl8ulKAorRCEQSP86hMvlctt9DMePHy+TyTIzMw0MDFxcXNra2+DG1HgURZEkifXWV+SfWMzQ1SFgNS2AWltbWVw6892jUChYzKCJeDweSZLsvneaiKIoiqLq6+vPnDkjFApDQkKUSmVXJ642NTYOcjy1ZWBgwOPxGnV+hZAtLR2+W+RyOf3Xj6Gnp2dzc3Nqaqqenp6NjQ1FUXK5fDBDagEul8vhcLB96ytm+8Z6e+u01umhAKJpmt3CjRW6+aqBRS0tLW3dPevp6XUzJc5mbY+maayQXm6qJk6c2NLSkpKSMmXKlKFDhw5sJoA/qfMntIejPB4eHlu2bCkqKhqcNAA6SKFQMH3WobtnGDgURQUHB4tEovj4eHQSDUD0WAA9++yz33//vb29fVhY2HfffYePDYBq0TSdkpJSVVWF7p5hoPF4vLCwMC6XGxcX19LSwnYcAJb1UAC9//77+fn5V65c8fT03LBhg6Wl5XPPPRcbG8v68TwA7ZCRkVFUVBQcHIzunmEQCIXCyMhImUyWkJCAzTjouJ4vdCJJ0tPT89NPP83Ozp47d+7hw4djYmKGDRv2j3/8QyqV9vjvANCVnJycGzdu+Pj4ODg4sJ0FdIWRkVFERERdXV1SUhI6iQZd1nMBVFZWtnv37smTJ1tZWZ07d27dunUXLlzYsmXLoUOHnnvuuUGICKCVCgoKcnNzx4wZM2rUKLazgG4ZOnRoUFDQo0eP0tLS2M4CwJoergILCgpKTU21t7efM2fO5s2bvb29mcujJk6caGVlhQIIoH+Ki4svXbrk6Og4btw4trOALmI620xPTzcwMBg/fjzbcQBY0EMB5Ofnt3379ra6p8Oo8+fPD1gwAK1VWVl57tw5KyuriRMnsp0FdJeLi0tzc/PVq1cFAsHIkSPZjgMw2Ho4BJaVleXp6dm++ikrK3vqqacIgjAxMfHw8BjYdABap66uLiEhwcTEJDg4GL2NA7vGjh3r6up65cqVwsJCtrMADLbO9wClpqampqYSBJGQkLBly5b2vfLfvn0bO34A+qe5ufns2bN8Pj88PJzLVdN+2EGnTJgwQSKRnD9/fvLkyZaWlmzHARg8nW+Cb9++ffr0aeZxXFxc+z1AFEVt3759MKIBaBfm2mOapidPnszn89mOA0AQBEGSZFBQUHx8fGJiYlRUlKmpKduJAAZJ5wXQ4sWLFy9eTBCEt7d3QkICfqoCPCGFQpGYmNjY2BgVFWVoaMh2HID/x+FwwsLCzpw5k5iYOGXKFLRP0BGdVzZXrlyxsLCws7PbvXt3Tk7O4xN4e3sPcDAA7UHT9Pnz5ysrK8PDw/ELG9SQnp5eeHj46dOn4+Pjo6OjBQIB24kABlznBZCPj8+qVat27drl4+PT6QTqfHszAHXDdPccFBRkZWXFdhaAzgmFwsmTJ58+fToxMTEyMhI7/kHrdd7EJRIJc+Jza2vr4OYB0DZXr169ceOGr6+vvb0921kAumNkZBQWFhYXF5eSkhIaGoqrFEG7dd6+BQIBj8cjCILL5V68eLG4uJjL5Z46deqZZ575+OOPsfsHoJdu3bp19erVMWPGuLm5sZ0FoGfm5uZBQUGlpaXp6elsZwEYWD0U+Fu3bp00adK1a9fq6+sXLFjA5XL//e9/b9iwYXDCAWi0wsLCS5cuubi4oLtn0CA2NjZ+fn537tzJyspiOwvAAOqhAPriiy8+/fTTmJiY/fv3jx49+ujRo9u2bTt06NDghAPQXGVlZefPn2duOMB2FoC+cXZ2Hj9+fF5e3rVr19jOAjBQejjNraKiIigoiCTJxMTEmJgYgiDc3NwePXo0KNkANFVNTU1ycjJzNAEnUoAmGj16tFQqzczM5PP5zs7ObMcBUL0eCiAXF5ejR4+amJjExsZu3bqVpum4uDgbG5tu/iUrK2vv3r0SicTT03Pp0qV6enrtxyYnJx89erSxsdHGxmb58uXW1tYqeBEA6qShoeHs2bNCoTA0NLR9L+oAmsXb21smk6Wnp+vp6dnZ2bEdB0DFevhtunHjxh07dri6ukZERDg5Ob377rtvv/32qlWrupq+ubl5586da9eu3bNnT21tbWxsbPuxhYWFe/bs2bBhw969e93c3Hbt2qWaFwGgNpqbm+Pj47lcbkRERIfqH0Dj+Pn5DRs27Pz58+Xl5WxnAVCxHgqg2bNnFxQUnD179qeffiIIYvLkySkpKStXruxq+uzsbEdHR0dHRw6HExMT0+GuYZWVlREREWKxmMvlhoSElJSUqOQ1AKgJmUyWmJgol8vDw8P19fXZjgPwpCiKCg4ONjMzS0xMrK6uZjsOgCr13NWVWCxWKBTFxcUEQTB7QW/fvt3VIeGKigoLCwvmsaWlZWVlZfuxnp6enp6eBEHIZLJDhw5NmjSpbVRZWdm0adOYx4sWLVoZNaM/r0almI4AoK90tg9ZuVweFxfX2Ng4bdo0c3Pzvv57L/udE/R9ztqtH6tay8gMDZv+OkRPT4/X08ewT/0cxsTE/PbbbwkJCTNmzDA2Nu57Ru2hs9u3J8Ruv5pKpbLT4T1kOnr06IIFC6RSaYfhXXUF1GF4p0tNT0//7rvvJkyYwNxujCESidp2LHl4eMjl8u6DDSiSJEmS7GqVQVcoiiJJUqFQsB2EBUqlMiEhobKyMjIy0sTEpE8NuE/tTdrU1PNEuoHP51MUJZFI2A7CMuVj22eFQkF22wIpiurT9o2iqMmTJ//++++xsbExMTFCobA/QTUcRVEURbH73aSh+treVI6m6U6PdvVQAP3973+fNWvWpk2bzMzMerMYc3Pz3Nxc5nFlZWWHH2c0Te/evfvevXsbNmywtbVtP8rAwGDRokVtT1tyb/ZmcQNEl7/InwSzz0wHNxDMrb4ePnwYEhJibm7e1zXQpw0rvu/bcDgcLpeLFUK1tpJ/HaJQKIiumxNzWWJfWymfz4+IiDh9+vTp06ejoqL4fH5/smoyLpdLkqQObt+eEEmShBp8L3R6QKeHc4Bqamo2bNgwYsQI87/qanovL6+CgoLS0lLmerHAwEBmeHFxsVQqzc7Ozs/P37x5c4fqB0CjXbp06f79+35+ft1fIAmg0UQiUUREhEQiSUhIYP37DODJ9VAAhYaG5ufn9352QqFwzZo1W7ZsWbFihb6+/vTp05nhK1asuHPnTl5e3oMHD+bOnTt79uzZs2cvWLCg/8EB1ENWVtatW7d8fHzQVwpoPRMTk/Dw8Nra2sTEROwjB01Hdn9jr/T09GXLls2aNcvLy6v9VS2TJ08e0Fg4BKaJeDweSZIymYztIIMnLy8vKytr/Pjxo0eP7vdM+nQIrNG6VwejdYGhoSGXy62trWU7CMuoQ4fI119vP0Rv11ec0PAup3/ic1lKS0sTEhKGDRsWHBysO/18crlcDofz+Bmx0D2SJDkcDuu7DAVjRjw+sIdzgEJDQwmC2LJlS4fhLS0tqooFoKGYmyW5ubk9SfUDoHGsrKyCgoLOnTuXlpYWEBDAnOQBoHF6KIBQ6AB06v79+2lpaS4uLr6+vmxnARhsdnZ2fn5+aWlpfD7fx8eH7TgA/dHzpfnV1dWpqam3b99esmTJw4cPR4wYgQ5yQMcVFRWlpqba29v7+fmxnQWAHc7OzjKZ7MqVKzweb9y4cWzHAeizHgqg69evM0d5Kysrn3766bVr1xYWFsbGxjo4OAxKPAC1U1paeu7cObFYHBgYiJ3/oMtGjRrV2tp69epVDoeDA8GgcXo4f23NmjVBQUFFRUUikYggiH379onF4jVr1gxKNgC1U15enpSUZGFhERoaqjunfwJ0ZezYsaNHj87Ozr527RrbWQD6poc9QKmpqWfPnm27p6OlpeX69evnzp078MEA1E5lZWVCQsKQIUNwm3eANuPHj1cqlZmZmTwez9XVle04AL3VQwFkamra4TxoiURiYGAwkJEA1FFNTU1CQoJIJAoPD2f3vjYA6sbLy6u1tfXixYsURaFDLNAUPezDnzlz5oYNG9ruaZqfn7969eqpU6cOfDAANVJfXx8fHy8UCiMiItp2iAJAmwkTJjg6Oqanp9+/f5/tLAC90kMB9Mknn1haWorF4rq6Oh8fn1GjRrm4uGzbtm1wwgGog/r6+jNnzggEgoiICB28BRJAb5AkOXHiRFtb29TU1KKiIrbjAPSshz35QqHw2LFjN27cyMvLk8vlo0aNGjNmzOAkA1AH9fX1cXFxPB4vIiJCIBCwHQdAfVEUFRQUlJycnJKSEhISglvjgZrrvAA6e/ZshyEmJiYEQZSXlzOjBvpWGADqoKGhIS4ujsPhREZGtr8VDAB0iqKokJCQ5OTk5ORk1ECg5jovgGbNmtX2uLm5mblfGEmSNE0bGBg4ODjk5eUNUkAAljQ2NsbHx1MUFRERIRQK2Y4DoBlQA4Gm6PwcoMY/HTx40MHBITY2ViKRSCSSM2fOWFpafvzxx4OcEmCQNTY2xsXFEQQRGRlpaGjIdhwATcLUQNbW1snJycXFxWzHAehcDydBv/feezt27JgyZYpAIODz+ZGRkdu3b9+wYcPghANgRVNTU1xcHE3TqH4A+gc1EKi/HgqgBw8eDBkypP0QExOTBw8eDGQkADY1NTWdOXOGpumoqChUPwD9hhoI1FwPBZCvr++HH35YX1/PPK2rq/vggw9wA0jQVg0NDadPnyYIAtUPwJOjKCo4ONjS0jIlJeXhw4dsxwH4ix4ug//qq6+Cg4Pt7e19fX0Jgrh06ZJQKDx37tygZAMYVHV1dcxZzzjyBaAqHA4nLCwsKSkpKSkpKCjIzs6O7UQA/9PDHqARI0YUFBRs2bLFzc3Nw8Nj+/btt2/fRk/noH3q6uri4uK4XO6UKVNQ/QCoEFMD2djYpKSk3L17l+04AP/T8y2NRCLRq6++OghR2uPxeIO8xPZIkiQIAvf67itmjbH73vVPVVUV09dzdHT04F/xzrS3Xq43FGdteDweRVFYIQo+X/bXIVwul9t1c+pTe1Oh8PDwpKSkCxcuUBTl4uIyyEt/ciRJkiSpids31rG+3pRKZafD1fSejnK5nMWlUxRFkqRCoWAxgyZimji7710/VFVVxcfHGxgYMPf5Gvz8fWpvEolkoPNoCpIkuVwuVggh61D/EAqFgui6GbO4fQsKCkpLSzt37pxUKnVzcxv8AE+Cy+XSNK1x2zfWkSTJ4XDUc72paQHEdL3I7tLZzaCJaJpmestkO0gflJeXJyQkGBkZTZ48mc/nsxK+T+sNdXkbmqZpmsYKoWia/OsQZs10NT27n1N/f3+CIC5duqRUKkeOHMlKhv7B90K/dd8gWaSmBRDAICgpKUlOTjY1NQ0PD8eebYBBwNwzlcPhZGRkKBQKDw8PthOB7kIBBDrq/v37qamplpaWoaGhXC4+CACDZ8KECVwuNysrSyqVenl5sR0HdBS2+6CLbt26denSJRsbm6CgIA6Hw3YcAJ3j5eXF5/OzsrIkEsnEiRNx0QkMPhRAoHPy8vKysrKcnJz8/f2x2QVgi4eHh76+fnp6emtrK36KwODD1h90S2ZmZlZWlpubW0BAAKofAHY5OTkFBQWVlJQkJCS0trayHQd0C74AQFfQNJ2Wlnbt2jVPT0+mZ3MAYJ2dnV14eHhVVVVcXFxLSwvbcUCHoAACnSCXy5OSku7cuTNx4kRceAKgVsRicURERGNj45kzZxobG9mOA7oCBRBov5aWlri4uNLS0uDgYNzIBUANmZubR0VFyeXy2NjYqqoqtuOATkABBFqOucF7Q0NDREQEbsQIoLZMTExiYmL09fXPnDlTXFzMdhzQfiiAQJtVVlbGxsYqlcro6GgLCwu24wBAd/T19adMmWJpaZmUlHTz5k2244CWQwEEWquwsPDMmTOGhoYxMTFGRkZsxwGAnnG53NDQUBcXl0uXLl2+fFk9b6EA2gH9AIF2ys/Pv3Llio2NzaRJk9DRM4AGoSjKz8/P0NCQ6SYxMDAQXQTBQMAeINA2SqXy0qVLGRkZI0aMCAkJQfUDoIk8PDwCAgKKiorOnj0rlUrZjgNaCAUQaBWpVJqQkFBQUODr6+vr60uSZM//AwBqycnJafLkybW1tb///nttbS3bcUDboAAC7VFfX3/69OnKysqQkBA3Nze24wDAkxKLxTExMVwu99SpU0VFRWzHAa2CAgi0RElJyalTp5RKZUxMjI2NDdtxAEA1RCJRdHS0WCxOSkq6evUq23FAe+D0CNAGt27dunz5srm5eUhIiEAgYDsOAKgSj8cLDQ3Nzs6+evVqfX39xIkTcVo0PDkUQKDZFArFpUuXbt++7ebm5u3tjfubAmglkiQ9PT2NjIwuXrzY2NgYHBwsFArZDgWaTfXfFllZWcuXL3/xxRe//PJLmUz2+AQKhWLp0qX19fUqXzTomsbGxtOnT9+9e9fPz8/X1xfVD4B2c3Z2joyMbGhoOHny5KNHj9iOA5pNxV8Yzc3NO3fuXLt27Z49e2pra2NjYztMEBsb+/bbb5eVlal2uaCDysrKTp06JZFIoqKiXF1d2Y4DAIPBwsJi2rRpIpEoLi4uLy+P7TigwVRcAGVnZzs6Ojo6OnI4nJiYmPPnz3eYwM7Obu7cuTweT7XLBV2Tl5cXHx9vbGw8derUoUOHsh0HAAaPUCiMiopyd3fPyspKSkrq9FADQI9UfA5QRUVF2x2XLC0tKysrO0zg7u5OEMTj56+Vl5fPmzePeTxv3rylIVGqDdYPqNL6Z6DPQZbJZMnJyffv3/fw8PDz89Oaw1697LCRb2Y20Ek0BdPJk5nOrxCZgUHzX4fo6enxevoYakEHoRMnTrSwsDh37lxsbGxERISpqekgLBTXWPQPu+1NoVB0OlzFmTrct0WpVPbyH/X19Z966inmsYeHh1wuV22wPiFJkiTJ3ocHBlPXdtXUVKKysjIpKUkqlYaHh9vb2yuVSi14m/rU3lpbWgY6j6bg8XgURaGPYEVra8chCgXZ9SZUm7ZvDg4OxsbGCQkJJ06cCAgIcHR0HLhlURRFURS7302aSB3aG03TnV40qOICyNzcPDc3l3lcWVlpbm7ey38UiUQrV65se9qSy+Z9gCmKIklyQL/ItRLT0AduA3H9+vWsrCxjY+Pw8HAjIyOt2RL1acPa1NQ00Hk0haGhIZfLxQqhZLIO/Z0rFAqi6+akZV/kIpEoJiYmLS0tOTm5uLjY19d3gHY2cLncAd2+aSuSJDkcDuvrrdMDOio+fODl5VVQUFBaWkrTdFxcXGBgIDO8uLgYP9Sg31paWhISEq5cueLg4BAdHY1buwNAGz09vZCQkICAgPv37588ebK6uprtRKAZVFwACYXCNWvWbNmyZcWKFfr6+tOnT2eGr1ix4s6dO6pdFuiIsrKykydPVlRUBAcHBwYGasG5CwCgck5OTlOnTuVwOKdOncrLy+twPgbA41T/XeLp6enp6dlh4PHjx9s/PXLkiMqXC9pHqVT+8ccfubm5FhYWkyZNQr9nANANY2PjmJiYzMzMrKyssrKygIAAfX19tkOB+sKPaVBTtbW1Fy5cqKmpGTt27OjRo3FfdwDoEYfD8fX1FYvFaWlpJ0+e9Pf3x50BoSsogEDt0DR97dq1nJwcAwODqKgodPMDAH1iZ2dnamqalpaWmJjo7Ozs4+ODbk3gcSiAQL00NjZeuHDh0aNHrq6u3t7eOOMHAPrB0NAwMjLy1q1bV65cefjwIXYFwePw7QJqhNlaCQSCiIgIKysrtuMAgGZzdXW1tra+cOFCYmIiflNBB2gKoBYaGhrS09PLysqwvxoAVIjZFXT9+vWcnJzS0lJ/f3+xWMx2KFALKICAZUql8tq1a7m5uXw+Pzw8fNiwYWwnAgCtQpKku7v7sGHD0tLS4uLinJycvL29+Xw+27mAZSiAgE3l5eUXL16sq6tzcXHx8vLCjh8AGCAmJibR0dEFBQWZmZlFRUWenp6urq5shwI2oQACdshkspycnJs3bzJbpd7fNQUAoH9IknR1dbW1tc3MzLx48eK9e/f8/PyMjY3ZzgXsQAEELLh7925mZmZra6uXl5ebm5vW3NEdANSfvr5+YGCgvb395cuXT548OXr0aHd3d+ZezqBTUADBoKqsrMzIyKioqLCxsfH19TU0NGQ7EQDoIltbW7FYnJOTk5ube/v2bS8vL3t7e7ZDwaBCAQSDRCKRXL16taCgQCQShYWFoU8OAGAXj8fz8fEZMWJERkZGSkqKubm5r68vDsfrDhRAMOCUSuXNmzdzcnJIkhw/fvyoUaNwzAsA1ISRkVF4eHhpaenly5djY2OHDx/u7e0tEAjYzgUDDgUQDCCapu/fv5+dnd3U1OTi4jJu3DhsVgBADVlZWU2fPv369et//PFHcXGxh4eHm5sbek3Ubnh3YaAUFxdnZ2fX1NRYW1uHhISYmpqynQgAoEsURXl4eDg5OeXk5OTk5OTn548ZM8bFxQV7rLUVCiBQvYqKiuzs7LKyMnNzc9zUAgA0iL6+vr+/v7u7e25u7uXLl69du+bh4eHi4sJ2LlA9FECgSpWVlVeuXCkuLjYxMQkJCbGzs2M7EQBAnxkZGQUGBo4cOTI7O/vixYs3b9708vIaPnw427lAlVAAgWpUVFTk5eUVFRUZGhoGBAQ4OjqSJMl2KACA/jMzM5s8eXJZWVl2dvbZs2fNzMzc3d3t7e2xcdMOKIDgSZWXl+fl5RUXF4tEIj8/P2dnZxwyBwCtIRaLo6Ojq6qq/vjjj3PnzhkaGnp4eGBDpwXUtABi99x7plmjxu9RUVHR1atXy8vLhwwZEhIS4uzsTJKkQqFgO5eGIUmSJMletnkDA4OBzqMpuFwuRVFYIQo9vda/DuFwON00pz61N2hjZWU1bNiw4uLiq1evXrx48dq1a2PGjHF2dkYX0t1Th/ZG03Snw9X0M6BUKtmOoBYZ1JNcLr99+/b169fr6+vNzMxCQ0Pt7OyYepGmaay3vmI2EL1cb1KpdKDzaAoOh0OSJFYIIZd3GEDTym6aE0mSFEXhc9pXJEnSNG1hYREREVFRUZGbm3vhwoXMzExXV1c3NzehUMh2QDWlDu0NBVDf9P4LSadIJJJbt27duHFDJpOJxWJvb2+mQ2f6T1hv/UBRVO/Xm/yxbzudpVQqKYrCCqGUyg47q5VKmuy6OVEUhR8q/cAcGWDWG/PDr76+/saNG9evX8/Ly7O1tR01atTQoUPZjql2+vQDb5CpaQEE6ubRo0c3b94sLCykKMrZ2dnNzc3IyIjtUAAArDEyMvL19R07duytW7du3rwZGxtrYWExcuRIW1tbnB6kEVAAQXekUumdO3cKCgrq6uoMDQ3Hjx/v4uKip6fHdi4AALXA5/OZ+8k/ePAgPz8/JSVFIBA4OTm5uLjgV6KaQwEEnauqqiooKLhz545SqRSLxePGjcPPGgCATlEUNXz48OHDh9fW1t69e7egoODatWtmZmYuLi6Ojo445Vw94V2Bv6irq7t3797du3cbGxtFIhFzmYO+vj7buQAANICJiYmnp+eYMWPu379fUFBw8eLF7Oxse3t7R0dHCwsLttPBX6AAAoIgiObm5vv379+9e7e6uprL5drZ2fn7++MWFgAA/cDlcp2dnZ2dnWtrawsKCu7fv3/r1i1DQ0NmL5GJiQnbAYEgUADpuJaWlqKiovv375eVlZEkaWVlFRgYaGdnhx22AABPzsTExMfHx9vbu7S09N69ezdu3Pjjjz+GDBkyfPhwOzs7nCTELnzP6aLGxsaioqLCwsLy8nKCIMzNzX18fBwcHAQCAdvRAAC0DUmS1tbW1tbWfn5+RUVFd+/evXr1alZWlomJiZ2dnZ2dnampKdsZdREKIF1B03RVVVVJSUlRUVFVVRVFUWKx2NfX187ODqf4AAAMAg6H4+Dg4ODg0Nra+vDhw8LCwvz8/NzcXENDQ1tb22HDhllaWqJr6UGDAkjLSaXSkpKShw8flpSUtLS0cLlca2trNzc3W1tbXM0OAMAKHo/HVEJKpbKkpKSwsPDevXv5+flcLlcsFltbWw8bNkwkErEdU8uhANJCcrm8vLy8rKysrKysqqqKpmljY2NHR0dra2uxWIxL2QEA1ARFUTY2NjY2NjRNV1dXP3z48OHDhxkZGZcvXzYyMrKyshKLxZaWljg/YSCgANIScrm8oqKirKzs0aNHlZWVSqVST09PLBY7OztbW1sbGhqyHRAAALpEkqSZmZmZmdmYMWOkUmlpaWlJSUlxcfHNmzcJgjAxMRGLxUwxxOfz2Q6rJVAAabDGxsaKP9XU1DBFj4WFhaenp1gsHjJkCG5oDwCgcfh8PnOAjCCIhoaGR48elZWVFRYW3rhxgyAIIyOjoX8yMTHBdr7fUABpEolEUl1dXVVVVV1dXVFRIZFICIIwMDAYOnQo08uWqakpPgwAAFpDJBKJRCJnZ2eCIOrr68vLy8vLyysrK+/evUvTNI/HMzc3Z3YdmZqa4rShPkEBpL5omm5oaKitra3+U3NzM0EQXC7X1NR0+PDhzC8AoVDIdlIAABhwRkZGRkZGTDEkk8kqKyuZIwAFBQV5eXkEQejp6Zn+ycTExMTEBCd9dgMFkLqgabqxsbG+vr6mpqauro75q1AoiD/btIODA9OsjY2NsZsHAECX6enpMX0LMU+bmpqYgwPV1dX37t27fv06QRAURYlEIpM/GRsbGxkZoSRqgwKIBTRNNzU1NTY2NjQ0NDQ01NfX19XVNTQ0KJVKgiA4HI6xsbGJiYmDgwPTanEKMwAAdMPAwMDAwMDOzo55KpVKa2pqav9UWloqk8kIgiBJ0sDAwKgdQ0NDQ0ND3ayKUAANIKVS2dTU1Nzc3NTUxFQ8jKamJqbWIQhCKBQaTuHxGwAACRtJREFUGRlZWlq6uLgwzVEkEmEHDwAA9Bufz2euGmsb0tTUVP+nurq6hw8f3rx5k6ZpZqxQKDQ0NBSJRIaGhgYGBkKhkBmi3bdF0ubXNggUCkVLS0tzc3P7vxKJpLm5mXnaNiWXy2UK7WHDhjEPmKbG4/FYzA8AALqA2UXU/hbXSqWyoaGh7Zd5Q0NDTU1NUVERs6+IoaenxxRD+vr6zF+GQCDQ19fX9O8v1RdAWVlZe/fulUgknp6eS5cu7dDdcPdj1YdMJpNIJDKZTCqVSqXStgdtJBJJS0uLXC5v/18CgUAgEAiFwiFDhtjY2DDthqmm0XMDAACoD4qijI2NjY2NOwyXy+XMUQuJRNL2oKamhrmdQNtOI4IgOBwOn88X/InP5/P5fD09vQ4P1LYXRxUXQM3NzTt37ty0aZO9vf3mzZtjY2NnzpzZy7GDRiaT3bp1Sy6Xy+Xy1tZWmUzW2toql8uZB8zfx/+Ly+W2vZcikWjo0KF8Pp8phJmiRyAQ6OZhVAAA0BpcLrfTwoggCJqmW1paJBIJswtAKpUyT6VSaUNDQ0VFBbO/oNN58ng8PT09PT09Lperp6fH4/GYgTwez8bGxsTEZOBf2WOpVDu77OxsR0dHR0dHgiBiYmJ+/PHH9iVO92MHjUwmu3r1atv7wTwQCoUmJibMu8KUrsyb1FbDorgBAABdRpIkcwism2lomm5/8KS1tVWhUDBHVNp2MUil0sbGRplMxuyGYL5/B+1VtFFxAVRRUWFhYcE8trS0rKys7OXYqqqqN954g3k8bdq0Z8b6qjZYe6ampkuWLOlmAuYc5PY7+qA3mBpRbQ9rqi2mvfWywmZlM6GeKIoiSRIrRC4USv46hMflcrv+GPapvUEbkiRJksT2rTf4fH5bl4zq8H3adtVRByougDq8yA5L7WYsj8cbOXIk83jo0KEyByuCPcyGlemDB3qPx+ORJNnp/k/oBkVRFEV1OJ+sS72cTAcw7a236017Pb6larUYoux6E9q39gZ/4nK5HA5HJpWyHUTDkCTJ4XDYbW9yubzTvmRUXACZm5vn5uYyjysrK83NzXs51sjIaP369W1PO+w6GmR6enoURbW/hgt6QyQSURTV2NjIdhANwxwLZ25sAr3HXKOL9kZJpR26zWhpaaG7Xi3MwX2mW3noPQMDA2zf+oHD4QgEgqamJnZjdNqdnor3gnp5eRUUFJSWltI0HRcXFxgYyAwvLi6WSqVdjQUAAAAYTCreAyQUCtesWbNlyxa5XD5y5Mjp06czw1esWPHxxx+PGjWq07EAAAAAg0n1/QB5enp6enp2GHj8+PFuxgIAAAAMJlwIAAAAADoHBRAAAADoHBRAAAAAoHNQAAEAAIDOIdHfMahKXFxcfX39nDlz2A4COiEpKam0tHT+/PlsB2GZ4o8/Wk+daj9E7+mnKRcXtvJoqwsXLhQUFCxevJjtIKAyqr8KDHTWuXPnSkpKUADB4EhPT8/NzUUBxBk9mjN6NNsptF9mZmZ8fDwKIG2CQ2AAAACgc1AAAQAAgM7BOUCgMtXV1QqFYujQoWwHAZ1QW1srk8ksLCzYDgI6ob6+vrm5WSwWsx0EVAYFEAAAAOgcHAIDAAAAnYOrwECVkpOTjx492tjYaGNjs3z5cmtra7YTgXbKysrau3evRCLx9PRcunSpnp4e24lAm2HLppWwBwhUprCwcM+ePRs2bNi7d6+bm9uuXbvYTgTaqbm5eefOnWvXrt2zZ09tbW1sbCzbiUCbYcumrVAAgcpUVlZGRESIxWIulxsSElJSUsJ2ItBO2dnZjo6Ojo6OHA4nJibm/PnzbCcCbYYtm7bCITBQGU9PT09PT4IgZDLZoUOHJk2axHYi0E4VFRVtF39ZWlpWVlaymwe0G7Zs2goFEDyRpKSk3377jSCIRYsWjR07liCI9PT07777bsKECegyFQZIh2tXlUolW0lAd2DLpn1QAMETCQ0NDQ0NZR7TNL179+579+5t2LDB1taW3WCgxczNzXNzc5nHlZWV5ubm7OYB7YYtm7bCOUCgMtnZ2fn5+Zs3b8Y2AgaUl5dXQUFBaWkpTdNxcXGBgYFsJwJthi2btkJHiKAy+/bt+/nnn7nc/+1WFAqF+/fvZzcSaKusrKwffvhBLpePHDny1Vdf5fF4bCcCrYUtm7ZCAQQAAAA6B4fAAAAAQOegAAIAAACdgwIIAAAAdA4KIAAAANA5KIAAAABA56AAAgAAAJ2DAggAAAB0DgogAFAv33//fWpqqgon7tMMAUBHoAACAPWCAggABgEKIAAAANA5KIAAQI14e3unpKS88847kydP7jDqv//979ixY/X19e3t7T/99NPHJ7558+a0adOGDh0qEokmTZqUlZXV/QwBQJfhXmAAoEbkcnl4eHhkZOTbb7/N4XDaht+7d8/FxWX16tXPPPPMuXPn3nzzzfPnz/v5+bWf2M3NzcrK6r333iMIYtOmTU1NTZmZmV3NEAB0HJftAAAA/4/L5ZIkyeFwOhQrd+/eVSgUS5cudXFx8fHxGTlypI2NTfuJlUrla6+9NmPGDEdHR4IgSkpKVq9e3c0MAUDHoQACAA0QEBAwffr0sWPHRkdHh4eHv/DCC0ZGRu0noChq2bJl586d++9//5uZmRkXF0eSJFtpAUD94RwgANAAAoHg119/zc3N9fT0PHjwoK2t7a+//tp+gsbGxoCAgHXr1rW2ti5fvvzrr79mKyoAaAQUQACgARITE9evX+/k5PTuu+9euHAhMjLyu+++az9BcnJyfn5+RkbGO++8ExgYqFAo2IoKABoBBRAAqBeKom7fvl1WVtZ+IIfD2bx589atW3Nycg4dOpSenj5+/Pj2ExsZGTU3Nx84cKCkpOTEiRPvvfdeU1PTw4cPu5ohAOg6GgBAnXz//fdmZmYzZ87sMPyzzz4bPny4np6era3tm2++KZVK20+sVCo3btxoYWFhamr6zDPP3Lx5093d3cfHp5sZAoAuw2XwAAAAoHNwCAwAAAB0DgogAAAA0DkogAAAAEDnoAACAAAAnYMCCAAAAHQOCiAAAADQOSiAAAAAQOegAAIAAACdgwIIAAAAdA4KIAAAANA5/wfECSAk+zNL7wAAAABJRU5ErkJggg==" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-t-distribution-on-top-of-permuted-t-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on top of permuted t-statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># alt: response = arr_delay, explanatory = half_year</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
 <span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
@@ -226,8 +221,7 @@ <h2>Compute theoretical p-value</h2>
 <span class="st">  </span><span class="kw">t_test</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year,
          <span class="dt">alternative =</span> <span class="st">&quot;two_sided&quot;</span>,
          <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(p_value) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>()</code></pre></div>
+<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(p_value)</code></pre></div>
 <pre><code>## [1] 0.3855</code></pre>
 </div>
 </div>

From 35f3d52632ec168ec66de36c80efd7c6ce3160a7 Mon Sep 17 00:00:00 2001
From: "evgeni.chasnovski" <evgeni.chasnovski@gmail.com>
Date: Mon, 24 Sep 2018 22:26:22 +0300
Subject: [PATCH 64/78] Stop using `%>%` and `+` in one pipeline in vignettes.

---
 inst/doc/chisq_test.R                |  17 +--
 inst/doc/chisq_test.Rmd              |  17 +--
 inst/doc/chisq_test.html             |  17 +--
 inst/doc/observed_stat_examples.R    | 210 +++++++++++++--------------
 inst/doc/observed_stat_examples.Rmd  | 210 +++++++++++++--------------
 inst/doc/observed_stat_examples.html | 168 +++++++++------------
 inst/doc/two_sample_t.R              |  17 +--
 inst/doc/two_sample_t.Rmd            |  17 +--
 inst/doc/two_sample_t.html           |  15 +-
 vignettes/chisq_test.Rmd             |  17 +--
 vignettes/observed_stat_examples.Rmd | 210 +++++++++++++--------------
 vignettes/two_sample_t.Rmd           |  17 +--
 12 files changed, 449 insertions(+), 483 deletions(-)

diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
index 8f338c9f..fc1d4f24 100644
--- a/inst/doc/chisq_test.R
+++ b/inst/doc/chisq_test.R
@@ -42,9 +42,9 @@ chisq_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_chisq, direction = "greater")
+
+visualize(chisq_null_perm) +
+  shade_p_value(obs_stat = obs_chisq, direction = "greater")
 
 ## ------------------------------------------------------------------------
 chisq_null_perm %>% 
@@ -56,14 +56,13 @@ chisq_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq")
-chisq_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+
+visualize(chisq_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ------------------------------------------------------------------------
-chisq_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+visualize(chisq_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 
 ## ------------------------------------------------------------------------
 fli_small %>% 
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
index 1cb2f535..2693b8c3 100644
--- a/inst/doc/chisq_test.Rmd
+++ b/inst/doc/chisq_test.Rmd
@@ -92,9 +92,9 @@ chisq_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_chisq, direction = "greater")
+
+visualize(chisq_null_perm) +
+  shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -113,17 +113,16 @@ chisq_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq")
-chisq_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+
+visualize(chisq_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
 
 ```{r}
-chisq_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+visualize(chisq_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Compute theoretical p-value
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
index 4f3e1658..b6f3c74b 100644
--- a/inst/doc/chisq_test.html
+++ b/inst/doc/chisq_test.html
@@ -179,9 +179,9 @@ <h2>Randomization approach to <span class="math inline">\(\chi^2\)</span>-statis
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-chisq_null_perm <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(chisq_null_perm) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
@@ -210,18 +210,17 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-chisq_null_theor <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(chisq_null_theor, <span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-chi2-distribution-on-top-of-permuted-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution on top of permuted statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(chisq_null_perm, <span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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" /><!-- --></p>
diff --git a/inst/doc/observed_stat_examples.R b/inst/doc/observed_stat_examples.R
index 4beac9aa..e4556a1b 100644
--- a/inst/doc/observed_stat_examples.R
+++ b/inst/doc/observed_stat_examples.R
@@ -34,9 +34,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 10) %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 
@@ -51,9 +51,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 8) %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 
@@ -68,9 +68,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", med = -1) %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "median")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 
@@ -85,9 +85,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", p = .5) %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = p_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 
@@ -110,9 +110,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -127,9 +127,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = z_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 
@@ -147,9 +147,9 @@ null_distn <- fli_small %>%
               p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
@@ -164,9 +164,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 
@@ -181,9 +181,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -198,9 +198,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 
@@ -216,9 +216,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 
@@ -233,9 +233,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = F_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 
@@ -250,9 +250,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 
@@ -267,9 +267,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 
@@ -285,9 +285,9 @@ null_distn %>%
 #     hypothesize(null = "independence") %>%
 #     generate(reps = 1000, type = "permute") %>%
 #     calculate(stat = "t")
-#  null_distn %>%
-#    visualize() +
-#      shade_p_value(obs_stat = t_hat, direction = "two_sided")
+#  
+#  visualize(null_distn) +
+#    shade_p_value(obs_stat = t_hat, direction = "two_sided")
 #  null_distn %>%
 #    get_p_value(obs_stat = t_hat, direction = "two_sided")
 
@@ -302,13 +302,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( t_hat <- fli_small %>% 
@@ -321,13 +321,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( p_hat <- fli_small %>% 
@@ -340,13 +340,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>%
@@ -359,13 +359,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( t_hat <- fli_small %>%
@@ -378,13 +378,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( d_hat <- fli_small %>% 
@@ -397,13 +397,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( z_hat <- fli_small %>% 
@@ -416,13 +416,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( slope_hat <- fli_small %>% 
@@ -435,13 +435,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ------------------------------------------------------------------------
 ( correlation_hat <- fli_small %>% 
@@ -454,14 +454,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 
 ## ----eval=FALSE, echo=FALSE----------------------------------------------
 #  # **Point estimate**
@@ -475,11 +475,11 @@ boot %>%
 #     generate(reps = 1000, type = "bootstrap") %>%
 #     calculate(stat = "t")
 #  ( percentile_ci <- get_ci(boot) )
-#  boot %>%
-#    visualize() +
-#      shade_confidence_interval(endpoints = percentile_ci)
+#  
+#  visualize(boot) +
+#    shade_confidence_interval(endpoints = percentile_ci)
 #  ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-#  boot %>%
-#    visualize() +
-#      shade_confidence_interval(endpoints = standard_error_ci)
+#  
+#  visualize(boot) +
+#    shade_confidence_interval(endpoints = standard_error_ci)
 
diff --git a/inst/doc/observed_stat_examples.Rmd b/inst/doc/observed_stat_examples.Rmd
index 30404cda..780512b3 100644
--- a/inst/doc/observed_stat_examples.Rmd
+++ b/inst/doc/observed_stat_examples.Rmd
@@ -69,9 +69,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 10) %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
@@ -91,9 +91,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 8) %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
@@ -115,9 +115,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", med = -1) %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "median")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
@@ -138,9 +138,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", p = .5) %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = p_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
@@ -177,9 +177,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -200,9 +200,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = z_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
@@ -230,9 +230,9 @@ null_distn <- fli_small %>%
               p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -253,9 +253,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -276,9 +276,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -299,9 +299,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -325,9 +325,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -348,9 +348,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = F_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 ```
@@ -371,9 +371,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
@@ -394,9 +394,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
@@ -420,9 +420,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "t")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -446,13 +446,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical (one mean - standardized)
@@ -471,13 +471,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -497,13 +497,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One categorical variable (standardized proportion $z$)
@@ -526,13 +526,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -551,13 +551,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -577,13 +577,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two categorical variables (z)
@@ -602,13 +602,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -628,13 +628,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two numerical vars - correlation
@@ -653,14 +653,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -682,11 +682,11 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
diff --git a/inst/doc/observed_stat_examples.html b/inst/doc/observed_stat_examples.html
index bb6704a4..0b5a1667 100644
--- a/inst/doc/observed_stat_examples.html
+++ b/inst/doc/observed_stat_examples.html
@@ -134,9 +134,8 @@ <h3>One numerical variable (mean)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)</code></pre></div>
 <pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -181,9 +180,8 @@ <h3>One numerical variable (standardized mean <span class="math inline">\(t\)</s
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)</code></pre></div>
 <pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -228,9 +226,8 @@ <h3>One numerical variable (median)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)</code></pre></div>
 <pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -275,9 +272,8 @@ <h3>One categorical (one proportion)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
 <pre><code>## Setting `type = &quot;simulate&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -334,9 +330,8 @@ <h3>Two categorical (2 level) variables</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
 <pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -381,9 +376,8 @@ <h3>Two categorical (2 level) variables (z)</h3>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
 <pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -432,9 +426,9 @@ <h3>One categorical (&gt;2 level) - GoF</h3>
               <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;EWR&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;JFK&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;LGA&quot;</span> =<span class="st"> </span>.<span class="dv">34</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
@@ -478,9 +472,9 @@ <h3>Two categorical (&gt;2 level) variables</h3>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
@@ -524,9 +518,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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QADAEiJAAMASIkAAwBISY6T+QJoV5wnTzbs3OneYh0/Xu3Xz1/1oHOSIMBqC5u7VO4Vsdlsuq67rgbSzlmtVllKVVXVZrPV19df0VVI/Kg1y/ZSz26+KaZ5oaGhFoulsrLS34X8iLJrl/rgg+4ttufWapNus1gsTqdTltUgMDDQbrdf3UXgfM/4iPm7ilbRNM1qtdbW1nqxz8DB113eyC5EAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlCQ4Ewfge6qqBgQEmNS5ruuynFIBaM8IMMCTcSIcW1iYebOorKyU5QxGQLtFgAGeFEURQjzwwANmdH7ttdcuWbLEjJ6BzoYAAxpXVVVlRrc1NTVmdAt0QgziAABIiQADAEhJgl2INpvNW12pqqrruhc7NJWiKLKUarBYJFidDM0vW+MYmKlCQ0N1XW/NlJqmKYoSERFhdklXxB4c7HGtJ4vFYrXZFEVRVZm+FmuaJkvBxlX3/F1FqxifIC9W29QV5iTY4nhxwDEXtDSJ8dGy2+2yXMmw+WVrsVjMDuNLly61chSicUHLCxcumFrPlVKqqz22+na7Xa+vl+6Clg6HQ5bhoNJd0NK71Tb6gZTjqwcAAB4IMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQs/i4A6Fw0TRNCBAQEWK3W1kxvsVhUVQ0KCmr9LOrq6pxO51XWB8iDAAN8KiYmRgihKEorp6+rq7ui/kNCQoQQNTU1V1oYIB0CDPApI7p+85vfnDhxwoz+X3/9dTO6BdohUwLM4XDMmTNnzZo14eHhRsuCBQtKSkqMj+6kSZMeeOABIcShQ4c2b95cU1OTkpKSnZ1ts9nMKAYA0CF5P8Dy8vJ27dpVWlrqatF1/ezZs9u2bXOPqOrq6nXr1i1fvjwuLm7lypV5eXkZGRleLwYA0FF5P8D69OmTmZmZm5vraikrKxNCrF27tqSkZODAgVlZWWFhYQUFBfHx8fHx8UKIKVOmbN261RVgDQ0Nrr0r3bp1i1S9NlTS+Amoeq9DUymKIlGpQraCZSn1KqiqarGYfHRA0/QfNyiKaixS6ZatRNXKUqrXt7S6rjfa7v21PDk5WfxzqJXhwoULiYmJWVlZkZGRGzdu3LRpU05OTllZWXR0tDFBTExMeXm5a/rz58/ffffdxu1777330Z+me7E8RVEk2lcpUalCiFYOq2sn5Fq2VyQoKOiKRi1ehfrg4Koft1gsFhkXqelJ71VyLWEvVtvUqFpfvHkJCQlLly41bs+aNWvevHm6rnskqnt93bp1cx2I7tatW/25C96qxGq16rput9u91aGpLBaLLKUqimK1WhsaGpr6otTeNL9sffELxjSqql7++fKiurq6uro6UV3t0W6320V9vdlz9y6bzWa322X5y4FEGwTjE1RfX++tDnVdD2is3Ref0lOnTum63q9fPyGExWIxfpxFRUUVFhYaE5SXl0dFRbmmt1qtSUlJrru1pRXeqsT4aMmyvkpUqrGvQKKCmy9Vlh01jVIU5fDhwyUlJWZ0PmrUqJCQELvdrjgcHstI151Op9MIMFlWA4NE1cpSqrEL0QfV+iLALl68+NJLL61evToyMvL9998fOXKkoiipqam/+93vzp49Gxsbm5+fP3r0aB9UAnQGBQUFeXl5ZvSckJDQt29fM3oGroIvAmzw4MHp6emLFi3SdX3gwIHZ2dlCiODg4JycnNzcXLvdnpSUNG3aNB9UAgDoMMwKsLffftt1W1GU9PT09HTPsRgpKSkpKSkmFQAA6Ngk3tcPAOjMCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICULP4uAIA0rFarqqpWq1XXNP3HDymKqqqqoiiqevVfi3Vd13W95ekAIQQBBqD1evToYbVaIyIi6oODq378kMVisdlsbexf1/W6uro2doLOgwAD0FqKohQUFGzZsuX648fv+PFDx48f/6HLl23p/Nprr+3Ro0dbekBnQ4ABuAK1tbXnzp27cOGCR3tDQ31tbW1berbb7W15OjohBnEAAKQkwS+wtu9Yd1FVVdd1L3ZoKkVRZCnVYLFIsDoZml+2iqL4shi4+H6d1zStLaNOfElVVVk2CMYnyIvVOp3ORtsl2OLU19d7qyubzabrekNDg7c6NJXVapWlVOOjZbfbm1rP2pvml63FYpEojDsSXde9+HlvUWBgoMPhkGXXpc1m8+XCaQtN06xWq3erbfQDKcdXDwAAPBBgAAApEWAAACkRYAAAKXGkGlLSNE3TtKt+unHSo6YeZRQiIAUCDFJSVdXhcFRUVJjReUREREBAgBk9A/AiAgyyqq6uPnz4sBk9Dxo0qHv37mb0DMCLOAYGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkJIpAeZwOLKzs3/44QdXy6FDh+bOnfuLX/xiw4YN9fX1zTQCANAaLQTYhAkTHA6He0tpaen06dObeUpeXt6iRYtKS0tdLdXV1evWrVuwYMGmTZsqKyvz8vKaagQAoJUsjbbu3bt37969QoidO3fm5uZqmuZ66MSJE3v27Gmmxz59+mRmZubm5rpaCgoK4uPj4+PjhRBTpkzZunVrRkZGo41eeUkAgM6g8QA7ceLEn/70J+N2fn6+oiiuh1RVXbt2bTM9JicnCyHcM6+srCw6Otq4HRMTU15e3lSjoaqq6p133jFuX3/99UOCIq/wRTXJeCEWS+Ovur1RFEWWUg2qqqqqj46quq+T6DB8v86rqirLp0yiDYLXt7S6rjfa3vgMZs+ePXv2bCHE0KFDd+7c2cY6PObtdDqbajRUVVVt2bLFuH3nnXemjvtpW+buQVEUn21k206iUoU83wzQnvk+wCT6lMn1EfNitR5Hsv5/Fs0/7eDBgzU1Nd9++61He//+/Vs/76ioqMLCQuN2eXl5VFRUU42G6OjoXbt2ue7WFh5r/byaZ7PZdF1vaGjwVoemslqtspSqqqrNZquvr3f/ImIqq9XqmxnBl3Rdr6ur89nsAgMD7Xa73W732RzbwviI+buKVtE0zWq11tbWerPPxhpb+Oqxffv2Ll26JFzmimacmppaXFx89uxZXdfz8/NHjx7dVCMAAK3Uwi+whQsX/uxnP1u+fHm3bt2ueh7BwcE5OTm5ubl2uz0pKWnatGlNNQIA0EotBFhFRcXTTz993XXXXWm/b7/9tvvdlJSUlJQUj2kabQQAoDVa2IWYlpZWVFTkm1IAAGi9Fn6BPfHEE3PmzDl8+HBqampQUJCrfcKECSYXBgBAc1oIsLS0NCGE+7+SDd4dXgIAwJVqIcAIKgBA+9RCgLmfIMOd+9+2AADwvRYCrHv37o22N3ViDwAAfKOFACsuLnbdrqmpOXDgwEsvvfTGG2+YXBUAAC1oIcA8Thk1aNCg7t27z5s3b+fOnWZWBQBAC674LJY9evTYv3+/GaUAANB6LfwCO3z4sPvdioqKZ555pnfv3maWBABAy1oIsEGDBnm0REVFcQwMAOB3LQTYxYsXPVpCQkK4liAAwO9aCLDQ0FCn07lnz56ioiK73Z6cnDx27FgCDADgdy0E2Llz56ZMmVJQUBAXF6eqaklJSUpKyscff9zU/8MAAPCNFkYhPvbYYxaL5dSpU6dOnTpx4sSpU6cURZk/f75vigMAoCkt/ALbuXPnW2+9FRcXZ9yNi4t77rnn7rrrLvMLAwCgOVf8PzAAANqDFgJs/PjxCxcu/O6774y7f/vb3xYuXDh+/HjzCwMAoDktBNgLL7wghOjXr1///v379+8fHx8vhFi/fr0vSgMAoGktn43+L3/5izGMXgiRmJg4ZswYhtEDAPyuhQCrr69/9tlnz58/v2HDBiHEjTfeOG7cuGeffTY4ONgn5QEA0LgWdiEuXrz4lVdeGTp0qHE3Ozv77bfffvLJJ80vDACA5rQQYNu3b3/++ednz55t3J0zZ86rr766fft20+sCAKBZLQRYVVVV37593VtCQ0Pr6urMLAkAgJa1EGBpaWnLli07f/68cbeysnLFihXjxo0zvS4AAJrVwiCODRs2TJw4sXfv3gMHDrRYLEeOHOnVq9euXbt8UxwAAE1pIcBiYmIOHTqUl5d3+PDhhoaGhQsXTps2zWq1+qY4AACa0kKACSE0TZs6derUqVN9UE2jVNVr57sy/sHmxQ5NpSiKRKUK3xbMnxE7Kt+v87J8yoQ8pfpsS9tygPmd15eCLCuBkKpUIYSiKOQK2sL3X9ok+ohJ943Wi9Xqut5ouwQBZrfbvdWVqqq6rnuxQ1NZrVZZSjXWVKfT6XQ6fTNH9mN3SD7+eFosFqfTKdGnTJZSNU3TNM271Tb6gZcjzwEA8ECAAQCkRIABAKREgAEApCTBIA4AnUFYWJiiKIGBgebNora21rzO4XsEGIB2QdO0hoaGkydPmtF5WFhYr169FEVpakA2ZESAAWgvHA7HmTNnzOi5e/fuvXr1MqNn+BHHwAAAUiLAAABSIsAAAFIiwAAAUmIQB0xk3rl9OWswAAIMZrHZbLKcPBuAjAgwmEVRlHPnzp0+fdqMzpOTk83oFoBECDCYqLa2tqKiwoyefXbdFgDtFnt4AABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUvLRyXwXLFhQUlJiXMNp0qRJDzzwgBDi0KFDmzdvrqmpSUlJyc7OttlsvikGQGdjbHw0TdN13aNd0zSvzMLhcHilH7SeLwJM1/WzZ89u27bNPaKqq6vXrVu3fPnyuLi4lStX5uXlZWRk+KAYAJ1QWFiYEMJi8dziaZrmrQDTdZ2LJPiYLwKsrKxMCLF27dqSkpKBAwdmZWWFhYUVFBTEx8fHx8cLIaZMmbJ161YCDICp/vKXv9TX13u9W5vNNmLECK4S7nu+CLALFy4kJiZmZWVFRkZu3Lhx06ZNOTk5ZWVl0dHRxgQxMTHl5eWu6f/xj3889dRTxu2JEyemJw7xViWqquq6Lsu+SkVRZCnV4PH1ls8z2hu73W63273erXHlcYvF4q0fc42SaINgfPa9WG1TP219EWAJCQlLly41bs+aNWvevHm6rnvsiXavT1XV8PBw43ZAQIDHlG3n9Q5NYqwEUlTryiopqgXMcPlmzbsURZHo8+Wban0RYKdOndJ1vV+/fsLtS0pUVFRhYaExQXl5eVRUlGv6yMjI3Nxc193awmPeqsRYpg0NDd7q0FRWq1WWUlVVtdlsdrvd44uIH0sCfMzhcJg6jsNms8myQdA0TVVV71bbaFb5YhNz8eLFlStXVlRU6Lr+/vvvjxw5UlGU1NTU4uLis2fP6rqen58/evRoH1QCAOgwfPELbPDgwenp6YsWLdJ1feDAgdnZ2UKI4ODgnJyc3Nxcu92elJQ0bdo0H1QCAOgwfBFgiqKkp6enp6d7tKekpKSkpPigAABAx8NRCgCAlAgwAICUCDAAgJR8dC5EtE+Xn1nn6rhONOc+dJ4/MgMwFQHWeSmKYrFY6uvrTfrzirfSEQAaxSamszt+/Pi5c+fM6DktLc2MbgHAwDEwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAAmOt4YAAAoZSURBVICUCDAAgJQIMACAlCz+LqBlFovXilQURVEUL3ZoKlVVrVarrusm9a8oikk9A52Qpmmmfqbk2nYJr266m9oMSrA4nE6nt7rSNE3XdS92aCpVVR0Oh6mz0DTN1P6BzsPpdJq6bVFVVZZtl/B2tQSYEELoui5RgJldKr/AAC/ywbZFlm2XsW3xQbUcAwMASIkAAwBIiQADAEiJAAMASIkAAwBIiQADAEhJgmH0ANCeGaPGNU0z/sBrBrP/EiopAgwA2sRqtQohqqur6+vrzeg/MjLSvGiUGgEGAF5QUlJSVlZmRs8jR46U5SRSPkaqAwCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApMS/u9s1VVWNs9QAADwQYO2aoiiKopSWljqdTq93brFYoqOjvd4tAPgGASaBkydP1tXVeb3b0NBQAgyAvDgGBgCQEgEGAJASuxABoF0zLpWpKIpJQ7qcTqekF8wkwLzApGvNGSM4zOgZgEQsFovD4WhoaDCjc5vNpmkaAdZJWSwW8641x2VYAQghzpw5c+LECTN6Tk5O7t69uxk9+wAB5gV1dXVfffWVGT3HxcX16tXLjJ4BQAgREhKiqmpgYKDXezb6dDqd9fX1Xu/c4M8AO3To0ObNm2tqalJSUrKzs202m0kzMvYgCyFUVdV13bv7kVVVtdvttbW1XuzTxW63m9EtABgsFkttbe33339vRueRkZFRUVFm9GzwW4BVV1evW7du+fLlcXFxK1euzMvLy8jIMGlemqY5nc6amhozOg8ODjajWwDwjdra2r/97W9m9Ox0OjtmgBUUFMTHx8fHxwshpkyZsnXrVvMCTAhx/vz5I0eOmNHz0KFDzfvtCABoiqLrul9m/N57750+fXru3LlCiNOnTy9ZsuS1114zHiotLZ06dapx+9577330p+l+qRCd3YA4f1fQftVv21Z1113uLSHPv2S7bZq/6kHH5nQ61cS+l7f77ReYR3C6n+svPDx88eLFxu0BAwZc6tmtjfNSVVXTNCGEzWZzOp3ePbBkDHY341yFVqu1oaFBVVUzOjd4q3NFUWw2W0NDg3tvplZu/MHgqr9+Gcu2mc4VRXGacPouwxUtGYvFoqpq6w+D+2ax2y9beg1REXpcrKZpuq5fXQHmfZQMly+ZgIAAu93ulRHkPvicNr/SXrU2fpQapWmaxWIxToDnlT+ZOZ3O8Mba/RZgUVFRhYWFxu3y8nL3/aTBwcG333676255ebm3ZhoZGWm32y9duuStDk0VFhZ26dIlf/1EviIWi8Vms1VVVUkx6kRRlNDQ0IsXL/q7kFYJDQ21WCztrVqlttbjHx61tbX6xYtBQUF2u92kfyx5XUBAQF1dnUlHx70uPDy8va0GTQkMDAwNDfXB5stvfzNKTU0tLi4+e/asruv5+fmjR4/2VyUAABn57RdYcHBwTk5Obm6u3W5PSkqaNo295wCAK+DP/4GlpKSkpKT4sQAAgLw4UxEAQEoEGABASgQYAEBKBBgAQEoEGABASgQYAEBKfjsXol+89dZbUVFR48eP93chHc25c+feeuutmTNnxsbG+ruWjmb37t1nz56dNWuWvwv5EUdRUcMHH7i3WNPTtaQkf9VzdTZs2DBmzJjBgwf7u5CO5ujRozt37pwzZ455F/s1dK4LWn744YcDBgwgwLyuvLx8y5Yt48aNI8C8bt++fYWFhe0twLSkJOni6nJ/+MMfoqKiCDCvKy4u3rJlS3Z2ttkBxi5EAICUCDAAgJQ61zGwc+fOWa3WLl26+LuQjqa+vr6srKx79+5c29PrKisr6+vro6Oj/V1IB3T69OmIiIjQ0FB/F9LRVFdXV1RU9OzZ07hWi3k6V4ABADoMdiECAKTUWUYhOhyOOXPmrFmzJjz8/y7seejQoc2bN9fU1KSkpGRnZ7Pvq40WLFhQUlJi7DGYNGnSAw884O+KOgLWUpOwunqdX7axnSLA8vLydu3aVVpa6mqprq5et27d8uXL4+LiVq5cmZeXl5GR4ccKZafr+tmzZ7dt28YW1otYS03C6up1/trGdopdiH369MnMzLRara6WgoKC+Pj4+Ph4TdOmTJmyZ88eP5bXAZSVlQkh1q5dm52dvX79elkufN7OsZaahNXV6/y1je0UAZacnDxs2DBN01wtZWVlrmFdMTEx5eXlfiqtg7hw4UJiYmJWVtaGDRuEEJs2bfJ3RR0Ba6lJWF29zl/b2I65C3H37t0ffvihEOLee+8dMmTI5RN4jL10Op0+qqwD8VjIS5cuNdpnzZo1b948XdfNHkHb4bGWmiQhIYHV1Wy+WXs7ZoClpaWlpaU1M0FUVFRhYaFxu7y8PCoqyid1dSjuC/nUqVO6rvfr108IYbFY3L+I4aqxlpqE1dUHfLP2dopdiJdLTU0tLi4+e/asruv5+fmjR4/2d0Vyu3jx4sqVKysqKnRdf//990eOHMn32bZjLTUJq6sP+Gbt7Zi/wFoUHByck5OTm5trt9uTkpKmTZvm74rkNnjw4PT09EWLFum6PnDgwOzsbH9X1BGwlpqE1dUHfLP2ciYOAICUOukuRACA7AgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwoJ167bXX9u7d2/ZpgI6KAAPaKQIMaB4BBgCQEgEG+N+OHTuGDBkSFBQUFxe3Zs0aIcTQoUM/++yzJ598csKECUKIY8eOTZ06tXv37mFhYbfccsuhQ4cunwbobDgXIuBnJSUlCQkJ8+fPnzlz5ueff/7EE0/s2bNnxIgR48ePnzhx4qJFizRNS0xM7NGjx1NPPSWEWL58eVVV1VdffWW3292n8ffrAHytk56NHmg/Tp065XA4srOzExIShg0blpSU1Lt3b4vFoiiKpmmapjmdzoceeig9PT0+Pl4IcebMmfnz5wsh3Kfx94sA/IAAA/xs1KhR06ZNGzJkyOTJk8ePH//zn/88PDzcfQJVVefMmfP555/v2LHjq6++ys/P5/pVgOAYGOB3gYGBH3zwQWFhYUpKyptvvnnNNdd88MEH7hNcunRp1KhRjz/+eENDw9y5czdu3OivUoF2hQAD/GzXrl2LFy/u16/fkiVLvvjii4kTJ/7+9793n+DTTz8tKio6cODAk08+OXr0aIfD4a9SgXaFXYiAn2matnLlyoiIiJ/+9KdFRUX79u0zrhGsquqJEydKS0vDw8Orq6vfeOONSZMmHThw4Kmnnqqqqjp9+nSvXr1c08TGxvr7dQA+pwPwtxdeeKFv3742m+2aa6554okn6urqdF1/7bXXunXrlpGR4XQ6ly1bFh0d3bVr15kzZx47diw5OXnYsGHu0/j7FQB+wDB6AICUOAYGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJDS/wLAkn1a9uoyXAAAAABJRU5ErkJggg==" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -570,9 +564,9 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -618,9 +612,9 @@ <h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
 <span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -664,9 +658,9 @@ <h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
 <span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
@@ -710,9 +704,9 @@ <h3>Two numerical vars - SLR</h3>
 <span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -756,9 +750,9 @@ <h3>Two numerical vars - correlation</h3>
 <span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
-null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+
+<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
@@ -824,9 +818,8 @@ <h3>One numerical (one mean)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> x_bar) )</code></pre></div>
 <div class="kable-table">
@@ -845,9 +838,8 @@ <h3>One numerical (one mean)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="one-numerical-one-mean---standardized" class="section level3">
@@ -891,9 +883,8 @@ <h3>One numerical (one mean - standardized)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
 <div class="kable-table">
@@ -912,9 +903,8 @@ <h3>One numerical (one mean - standardized)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="one-categorical-one-proportion-1" class="section level3">
@@ -958,9 +948,8 @@ <h3>One categorical (one proportion)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> p_hat) )</code></pre></div>
 <div class="kable-table">
@@ -979,9 +968,8 @@ <h3>One categorical (one proportion)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="one-categorical-variable-standardized-proportion-z-1" class="section level3">
@@ -1029,9 +1017,8 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
 <div class="kable-table">
@@ -1050,9 +1037,8 @@ <h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="one-numerical-variable-one-categorical-2-levels-t-1" class="section level3">
@@ -1096,9 +1082,8 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
 <div class="kable-table">
@@ -1117,9 +1102,8 @@ <h3>One numerical variable, one categorical (2 levels) (t)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-diff-in-proportions" class="section level3">
@@ -1163,9 +1147,8 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
 <div class="kable-table">
@@ -1184,9 +1167,8 @@ <h3>Two categorical variables (diff in proportions)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="two-categorical-variables-z" class="section level3">
@@ -1230,9 +1212,8 @@ <h3>Two categorical variables (z)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> z_hat) )</code></pre></div>
 <div class="kable-table">
@@ -1251,9 +1232,8 @@ <h3>Two categorical variables (z)</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---slr-1" class="section level3">
@@ -1297,9 +1277,8 @@ <h3>Two numerical vars - SLR</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> slope_hat) )</code></pre></div>
 <div class="kable-table">
@@ -1318,9 +1297,8 @@ <h3>Two numerical vars - SLR</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---correlation-1" class="section level3">
@@ -1364,9 +1342,8 @@ <h3>Two numerical vars - correlation</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
 <p><img 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UtwEfEWhVqtVupCJKPl9XuunjFt8VUG9wo0vxmK51AoFO3t7VJXIT0vLy+DwdC1u7VR4Wrddzz/77GL9Arsp1N0HEyhULAs29bW5sLS3EVISEjHRuxCBAAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACpR8E8cAOAmGIZhWSeu9QqCwPO88/qHHgYBBgD2ksvlzg4wT/4rNbhdCDAAuA11dXVVVVXO6FmlUvXr188ZPUNPhQADgNvA87yT/lQXOw/hdjk+wI4ePbpv376bN2/2799/4cKFffv2JYSUlZXt3LmztbVVrVZnZWUpFAprjQAAAPZw8O7sH3/8cceOHWvWrNm5c2dMTExhYSEhpKWlZfPmzcuWLduxY0djY2NRUZG1RgAAADs5OMA0Gs0DDzwQHh4uk8nGjx//yy+/EELKy8tVKpVKpeI4burUqcePH7fWCAAAYCcH70JUq9VqtZoQ0t7e/re//W3MmDGEkLq6utDQ/39T3bCwMPEGlRYbRc3NzR988IH4eOjQoTExMY4t0m3JZDJCiFPP8nJzLG9+cIVhGXGyeCaGca/RZxjG2f1bHF/x9H23mhSOZTZZWZb19vbuOJhMJmMYxtnfghuydnzUKTPEyZMn33777dGjR8+bN48QIghCx1IsNoqam5t37dolPn700UfFRPQcnnwskDXozFuYnrzYsoenjb618WVZtiev292aSRzH+fj4WBtWLpc7vyD3Yu2+5A7+bQiCsH379pqamjVr1gwYMEBsDAkJqaioEB9rNBrxztAWG0WhoaElJSXGp6YbZz2bl5cXIcSTr4PRdtgCMxgMnnkDdZFCobD205WEs9eurF0H5uXlZTAYnHT2ozswW6HX6XT19fUdB1MoFCzLeuYvQlw8mnHwGk15eXlVVVV+fr4xvQghCQkJ1dXVtbW1giAUFxcnJSVZawQAALCTg7fAKisrr1y5kpGRIT718fHZvXu3j49PdnZ2QUGBXq+PjY1NTU0VX+rYCAAAYCcHB9icOXPmzJnTsd14cofNRgAAAHt41vFhAHBb3t7eDMNYPMzGMAzHcd0/icOtDihC9yHAAMAtiGdqWDx5wSGdBwYGMgxjdroEUA0BBgDuQqfTVVZWOqPn3r17Dxs2zBk9g4R67nUVAADQoyHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKgkk7oA23x9faUuwUU4jiOEyGQUfClOwhl0Zi0My3jyBGEY9xp9hmGkLqFbZDKZIAhSV2GB2WTlOM7ico/jOIZhxAWFR9Hr9Rbb3ei3YU1bW5vUJbiIQqEghLS3t0tdiGS0vPlsKvCCwWCQpBh3wLKsW40+y9K9z8ZgMLhngJnVxPO8xeWeXC5nWVar1bqmKvdh7VujIMDc6gfsVDzPE08a344MvIVxd88ljst4+Og7liAIVExPQbC83iZue3nyIsIM3etTAADgsRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUouCGlgBgP/Gu807q3Hk9A3QBtsAAehS5XI4AAw+BLTCAnuby5cs//fSTM3oeO3asM7oF6BpsgQEAAJUQYAAAQCUEGAAAUMkpx8AMBsOCBQs2btwYEBAgtixbtqympkY8Ajx58uSnnnqKEFJWVrZz587W1la1Wp2VlaVQKJxRDAAA9EiOD7CioqKSkpJr164ZWwRBqK2t3bt3r2lEtbS0bN68OS8vLzIyMj8/v6ioKD093eHFAABAT+X4XYgREREZGRlyudzYUldXRwjZtGlTVlZWYWFhU1MTIaS8vFylUqlUKo7jpk6devz4cYdXAgAAPZjjt8Di4+MJIRzHGVtu3LgRExOTmZkZFBS0bdu2HTt2ZGdn19XVhYaGigOEhYVpNBrj8G1tbSdOnBAf33nnnWFhYQ4v0j3JZB5/VQPPmTUwDGM6L3kgDx99x2JZNz3qb3Z5HcuyXl5eHQeTyWQMw1h8qWfjed5iuyuWmNHR0WvXrhUfz5o1a8mSJYIgCIJgOoxpfY2NjStWrBAfz507d/HixS4oEtyB3KAza2FZ1nRr3gO57TKXRu47L916hTjHcf7+/taGVSqVzi/IvbS3t1tsd0WAXb58WRCEQYMGEUJkMpm4RhkSElJRUSEOoNFoQkJCjMOHhoaWlJSIj728vOrr611QpDsQV6y0Wq3UhUhGy+vNWgwGQ1tbmyTFuAOFQmHtp2uNBy7d7KfVas1Wnd2EWVV6vd7ick+hULAs65m/iF69enVsdEWANTU1vf766xs2bAgKCjp48GBiYiLDMAkJCW+++WZtbW14eHhxcXFSUpJxeJZljacvim93QZHuQJyJ3fMH5hqePO4ARh33UZm242di5IoAGz58eFpa2ooVKwRBiIuLy8rKIoT4+PhkZ2cXFBTo9frY2NjU1FQXVAIAAD2GswLs/fffNz5mGCYtLS0tLc1sGLVarVarnVQAAAD0bDg+DAAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJVfc0BIAQFpeXl6EELlc7ryP0Ol0uFeyiyHAAKDnEwPs+vXrPM87vHOWZcPDw1mWNRgMDu8cOoEAAwBPcfHixfb2dod3q1AowsPDHd4t2IRjYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQrOQvT29pa6BBeRyWSEEJb13LUKltebtTAsI04Wz8QwHj36dGFZlmGYrr3X7G0sy1pc7nEc151PoZe1ix8o+G3o9eYLtZ6KZVlBEDxnfDvSdwgwQRCcceEOLViW9eTRp4sD51VrywGGYTxzEWHtCnEKAkyn00ldgouI216eM74d6ToEGBGsrnx5CA8ffYp0J8DMFs+CIFhcDjAMw7KsJy8izHju3ioAAKAaAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqGQjwCZNmmR2h5tr165Nnz7dmSUBAADYZvk6sBMnTpw4cYIQcuTIkYKCAo7jjC9dvHjx+PHjLqoOAADACssBdvHixX/84x/i4+LiYtN/LmFZdtOmTa4oDQAAwDrLATZv3rx58+YRQkaMGHHkyBH8GxsAALgbG8l0+vTp1tbWH374waw9KirKWRUBAADYwUaA7du3b/bs2Vqt1qzd2l8rAgAAuIaNsxBzcnIeeuih8+fP193KNcUBAABYY2MLrKGhYc2aNUOGDHFNNQAAAHaysQWWnJxcVVXlmlIAAADsZ2ML7Pnnn1+wYEFlZWVCQoLpHUInTZrk5MIAAAA6YyPAkpOTCSEFBQVm7W1tbc6qCAAAwA42AgxBBQAA7slGgGk0GovtISEhTigGAADAXjYCrHfv3hbbcR0YAABIy0aAVVdXGx+3traWlpa+/vrre/bscXJVAAAANtgIMLO/jBo2bFjv3r2XLFly5MgRZ1YFAABgw23f0LJPnz6nTp3qfBiDwZCVlfX7778bW8rKyhYuXPjHP/5x69at7e3tnTQCAADYw8YWWGVlpenThoaGF198sX///p28paioqKSk5Nq1a8aWlpaWzZs35+XlRUZG5ufnFxUVpaenW2zszpgAAIBHsbEFNuxWY8eOLSsrKyws7OQtERERGRkZcrnc2FJeXq5SqVQqFcdxU6dOFe+HabERAADATja2wJqamsxafH19Te9v2VF8fDwhxPQmznV1daGhoeLjsLAw8dR8i42i9vb2b775Rnzcp0+fgIAAe8akBxAnmmn2exqe7zBrMYRlb3tHt5tjGKbzH5HpkLgbHy0YhunyvGo2NzAMY3E5IJPJrL3Us1k7793Gb8PPz4/n+ePHj1dVVen1+vj4+HHjxtn527P22TzPW2sU/fbbb88884z4eO7cuYsXL76tjwN6tRl0Zi0cyykUCkmKcRMIMFp065u6daEqk8kCAwOtDWv6r34ewtpJEjam+PXr16dOnVpeXh4ZGcmybE1NjVqt/vTTT61dH2ZRSEhIRUWF+Fij0YgXQVtsFPXq1Wv37t3Gx42NjfZ/FtXEJbUnn8+i5fVmLQbe0MMmiLgGfe7cuebmZmf0P3LkSGd0Czbp9XrTFfHbc+sKvV6vt7jck8vlDMP0sF+EPQRBsLgiayPAnnvuOZlMdvny5cjISELIlStXZs6cuXTp0tu6FCwhIeHNN9+sra0NDw8vLi5OSkqy1iiSy+WxsbHGp9b+DaTnEXch6vXmC3HPoe8QYEQgXV8ouCVxL1NLS0vH/fNANUEQujyvmu0gEwTB4nKAZVmWZT15EWHGRoAdOXLkf//3f8X0IoRERka+8sorjz322G19ho+PT3Z2dkFBgV6vj42NTU1NtdYIAABgJ2ftXn///fdNn6rVarVabTaMxUYAAAB72DhnZuLEiTk5OVeuXBGf/vjjjzk5ORMnTnR+YQAAAJ2xEWBbtmwhhAwaNCgqKioqKkqlUhFCOr8ODAAAwAVs/xv9P//5T/E0ekJITEzM2LFjb/c0egAAAIezEWDt7e0vvfRSfX391q1bCSH33HPP+PHjX3rpJR8fH5eUBwAAYJmNXYirVq3avn37iBEjxKdZWVnvv//+ypUrnV8YAABAZ2wE2L59+1599dV58+aJTxcsWPDWW2/t27fP6XUBAAB0ykaANTc3Dxw40LTFz89Pq9U6syQAAADbbARYcnJybm5ufX29+LSxsXHdunXjx493el0AAACdsnESx9atW1NSUvr37x8XFyeTyc6ePduvX7+SkhLXFAcAAGCNjQALCwsrKysrKiqqrKzU6XQ5OTmpqake+Gf+AADgbmz/lRTHcdOmTZs2bZoLqgEAALBTT7tVIAAAeAgEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJVs/5mv5Ly8vKQuwUXkcrkgCFJXISmeM2tgGIbjzBupxjCM1CWAU3RnXjWbJ1iWtbjck8lkDMN4ziLRiOd5i+0UBJhH/eAZhvGo8TXjyeMOtGMYxoEroBZ/C8y/OOpTaGFtlCkIsLa2NqlLcBFxxUqr1UpdiGS0vN6sRRAEg8EgSTFOwrLYb98z8Tzf5XnVLPd4nre43FMoFCzLes4i0Sb8lgAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACAShSchQgA4M7Ek7w5juvyKabMrVeCcRzn7+9v2tLW1qbT6bpcYU+FAAMA6Ba5XE4IaWlpaW9v71oPgnDLhbptbW3V1dXGp0OGDFEoFAiwjhBgAAAOUFNTU1dX17X39r3TmzX5E4+ffvrp5b+9a3y6ZcsWPz+/bpbXI+EYGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlVx0P7Bly5bV1NSI9y2dPHnyU089RQgpKyvbuXNna2urWq3OyspSKBSuKQYAAHoAVwSYIAi1tbV79+41jaiWlpbNmzfn5eVFRkbm5+cXFRWlp6e7oBgAAOgZXBFg4l1KN23aVFNTExcXl5mZ6e/vX15erlKpVCoVIWTq1KnvvfeeMcAMBsO1a9fEx/7+/hzHWeu5h2FZlhDiOePbEccIHRvFDXcXc96HSjI6QDuGYTiOY1mWZVkPXEQIgoUlA3FNgN24cSMmJiYzMzMoKGjbtm07duzIzs6uq6sLDQ0VBwgLC9NoNMbh6+rqjGE2d+7cxYsXu6BI9+Hr6yt1CZJpM+jIlRzzIskAAAyhSURBVFtaOI7z8vKSqBwAd6FUKpVKpfjYx8dH2mJcr7293WK7KwIsOjp67dq14uNZs2YtWbJEEASzROV53vg4ODh427Zt4uM+ffrcuHHDBUW6A7lcTgjR6XRSFyIZLa83azHwBmvzrvOwLCuTycrLy53Rea9evSIiIpzRM/RgWq22ra1NLpczDOP6X4TkBEGweJKEKwLs8uXLgiAMGjSIECKTycTt35CQkIqKCnEAjUYTEhJiHF6hUIwaNcr41HTjrGcTdyF6coDpOgQYEW5ZuXEN8YtoaGhwRufYoIQu4Hlep9MxDMOyrCcvIsy44jT6pqam/Pz8hoYGQRAOHjyYmJjIMExCQkJ1dXVtba0gCMXFxUlJSS6oBAAAegxXbIENHz48LS1txYoVgiDExcVlZWURQnx8fLKzswsKCvR6fWxsbGpqqgsqAQCAHsMVAcYwTFpaWlpamlm7Wq1Wq9UuKAAAAHoe/BMHAABQCQEGAABUQoABAACVEGAAAEAlF/2ZL4BjGS8oBACPhQADKjEMo9PpnHSR+x133OHt7e2MngHAgRBgQKvW1tbz5887o+dhw4YhwADcH46BAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQiYL7gclkFBTpEBzHCYLgOePbkYHv0MQQlrWwmsUwjAvqAXATLMuKdyEXH0hdjqsJgmCxnYIJIZfLpS7BRTiOk7oEifG8eSwxDGMxwAA8CsuycrlcDDDPWSQa8XzHdVtCqAiw1tZWqUtwES8vL0KIVquVuhDJaHm9WYvAC3q9eSPxpNUaAEKIXq9vbW1VKBQsy7a1tUldjgT8/f07NmLdFgAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqUXAaPVBKvGzltt7CdDiNnmEYi53gQmYAQICBs/j5+d1ugMkMOrMWz7xsEwDsgQADJ/rkk0/ee+89+4dnFDK/JammLbXXas9V/NhxyMTExO4WBwCUwzEwAACgErbAAADcWlBQkFwu9/b2Fp/6+fk5tn+tVtvU1OTYPl0DAQYA4NZYlv3uu+++/vprZ3SenJwcERHhjJ5dAAEGAODurly5UlJS4oye4+Li6A0wHAMDAAAqIcAAAIBKCDAAAKASjoF5NJZlnfeXFvizDABwKgSY52IYJjg4WOoqAAC6CAHmucQtpP37958/f94Z/a9YscIZ3QIAiBBgnu6nn36qrKx0Rs+CIDijWwAAEU7iAAAAKiHAAACAStiF2F0cxykUCod0JZPJCCEsy5o16vXmd8lyCJwlCAA+Pj4syxr/aNHhDAZDe3u7kzqXMsDKysp27tzZ2tqqVquzsrIcFQMdsSx7uzemsp9SqfTy8nJghx17c2z/AABG/fv35zjO19fXeR+h0Wic1LNkAdbS0rJ58+a8vLzIyMj8/PyioqL09HQnfZa/v79Tb4qo0WiWLFnijJ4fe+yx1NTURYsW/fbbbw7vPCIioqCgwOHdAgBdLly4kJeX54yeJ0+ePGfOHGf0LJIswMrLy1UqlUqlIoRMnTr1vffec16AiR+3a9cuZ/S8dOlSp668AACARYxU5zofOHDg559/XrhwISHk559/Xr169TvvvCO+dO3atWnTpomP586du3jxYkkqBNdrM+ie/faAaUuAb+/wkCip6gFwjUtXTxsMOuPTKL+Q5wcnS1iPu2lvb7d4jEmyLTCz4OR53vg4ICBg1apV4uPBgwffvHmzm5/l1GNgDMMwDGNaf5eJp2+YdcWyrEM6t/aJbtW51uQ3LBoi85rt17vjkOIZKE5a/XLgd2qR/VNGLpfrdObTxFGdd4FUk12hUBgMBoPB0J3+3W1uN7WSMM0mT3me12q1HTsX//utm9PBjFO/U0IIz/PdL5jnefcKsJCQkIqKCvGxRqMJCQkxvuTj4/Pwww8bnzrvAKC7EU/WMJ1xPY2WNz/fUq/XU3qvWIcICAjw5NE3Cg4Obm9vb2lpkboQZzHLD4PBYPF7VygULMu2tbW5qi53J9l1YAkJCdXV1bW1tYIgFBcXJyUlSVUJAADQSLItMB8fn+zs7IKCAr1eHxsbm5qaKlUlAABAIymvA1Or1Wq1WsICAACAXvgrKQAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACoJNl/IQJ0pBf4I9e/r6k8f7G88oHZMwghfZWBwwL7SF0XSOwvf/nL8OHDR40aJXUhzlJyvVon/Pv/loLkPqODIySshxa4oSW4ERnD/iEs5q+HT538qGjj8hekLgfcxd69e3me78EBNiE0WuoSqIRdiAAAQCUEGAAAUAnHwMDtNDU1NTU19e3bV+pCwF3U1tb6+voGBARIXQi4FwQYAABQCbsQAQCASjgLESRQVla2c+fO1tZWtVqdlZVldq/VQ4cO7d+/X6/XR0dHL168ODAwkBCybNmympoa8e6xkydPfuqpp6QpHZyj81ni4MGDH3/8McuyQ4cOffrpp8Vbv3b+FvAIAoBrNTc3P/7445cuXdLr9evWrTtw4IDpq1evXp05c+avv/6q1+u3bt26fft2QRB4nn/ssce0Wq1EJYNzdT5LnDlzZtGiRb/++qvBYNi+ffuePXtsvgU8BHYhgquVl5erVCqVSsVx3NSpU48fP276qpeXF8dxer3eYDAYDAa9Xk8IqaurI4Rs2rQpKyursLDQ4t3WgV6dzxIXLlwYOnRoaGgoy7ITJkz46quvbL4FPAQCDFytrq4uNDRUfBwWFqbRaExfDQkJmTFjxvz58x9//PGKiorZs2cTQm7cuBETE5OZmbl161ZCyI4dO1xfNjhP57PEoEGDvv76659++unGjRtFRUX19fU23wIeAsfAwNWEW0985Xne9GlVVdXRo0e3bt0aGhr64Ycf7tu3LzMzMzo6eu3ateIAs2bNWrJkiSAI4vEw6AE6nyVGjhx59erVF198US6X33fffUql0uZbwENgCwxcLSQkxLi+rNFoQkJCTF8tLy8fMWJERESEUqmcNm3aZ599JgjC5cuXL126JA4gk8k4jnN10eBMnc8SOp0uJSXlzTff3Lp16+DBg8PCwmy+BTwEAgxcLSEhobq6ura2VhCE4uLipKQksf3q1atarTYqKurMmTPXr18XBOHYsWMDBgxgGKapqSk/P7+hoUEQhIMHDyYmJmLzqyfpfJa4evXq/PnzGxoa2tvbP/roo5SUlE7eAh4FFzKDBMrKynbt2qXX62NjY59++mm5XE4Ieeihh15++eXY2NgDBw588sknDMP07dt3wYIFffr0EQTh448/PnTokCAIcXFxWVlZPj4+Uo8EOFIns0RcXNyBAwc++ugjX1/f5OTkGTNmiKsvFt8CHgUBBgAAVMIuRAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAA3NQ777xz4sSJ7g8D0FMhwADcFAIMoHMIMAAAoBICDEB6+/fvv+uuu7y9vSMjIzdu3EgIGTFixLFjx1auXDlp0iRCyIULF6ZNm9a7d29/f/8xY8aUlZV1HAbA0+C/EAEkVlNTEx0dvXTp0pkzZ37xxRfPP//88ePH77333okTJ6akpKxYsYLjuJiYmD59+rzwwguEkLy8vObm5jNnzuj1etNhpB4PAFfDDS0BJHb58mWDwZCVlRUdHT1y5MjY2Nj+/fvLZDKGYTiO4ziO5/n58+enpaWpVCpCyC+//LJ06VJCiOkwUo8EgAQQYAASu//++1NTU++6664pU6ZMnDjx8ccfDwgIMB2AZdkFCxZ88cUX+/fvP3PmTHFxMW6HBkBwDAxAckql8u9//3tFRYVarX733XcHDBjw97//3XSAmzdv3n///cuXL9fpdAsXLty2bZtUpQK4FQQYgMRKSkpWrVo1aNCg1atXf/nllykpKW+//bbpAEePHq2qqiotLV25cmVSUpLBYJCqVAC3gl2IABLjOC4/Pz8wMPAPf/hDVVXVyZMns7KyCCEsy168ePHatWsBAQEtLS179uyZPHlyaWnpCy+80Nzc/PPPP/fr1884THh4uNTjAeByAgBIbcuWLQMHDlQoFAMGDHj++ee1Wq0gCO+8806vXr3S09N5ns/NzQ0NDQ0ODp45c+aFCxfi4+NHjhxpOozUYwAgAZxGDwAAVMIxMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKj0/wAIZbF4iyPIhAAAAABJRU5ErkJggg==" /><!-- --></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, 
                             <span class="dt">point_estimate =</span> correlation_hat) )</code></pre></div>
@@ -1386,9 +1363,8 @@ <h3>Two numerical vars - correlation</h3>
 </tbody>
 </table>
 </div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="two-numerical-vars---t" class="section level3">
diff --git a/inst/doc/two_sample_t.R b/inst/doc/two_sample_t.R
index ff907dfa..ea06c5c3 100644
--- a/inst/doc/two_sample_t.R
+++ b/inst/doc/two_sample_t.R
@@ -42,9 +42,9 @@ t_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_perm) +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 t_null_perm %>% 
@@ -57,14 +57,13 @@ t_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
-t_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+visualize(t_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 
 ## ------------------------------------------------------------------------
 fli_small %>% 
diff --git a/inst/doc/two_sample_t.Rmd b/inst/doc/two_sample_t.Rmd
index a4ef6534..e4a2b100 100755
--- a/inst/doc/two_sample_t.Rmd
+++ b/inst/doc/two_sample_t.Rmd
@@ -92,9 +92,9 @@ t_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_perm) +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -114,17 +114,16 @@ t_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
 
 ```{r}
-t_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+visualize(t_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Compute theoretical p-value
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
index c115c4a5..f14c8554 100644
--- a/inst/doc/two_sample_t.html
+++ b/inst/doc/two_sample_t.html
@@ -166,9 +166,8 @@ <h2>Randomization approach to t-statistic</h2>
 <span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_perm) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="calculate-the-randomization-based-p-value" class="section level2">
@@ -199,18 +198,16 @@ <h2>Theoretical distribution</h2>
 <span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
 <span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
 <pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_theor <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_theor, <span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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" /><!-- --></p>
 </div>
 <div id="overlay-appropriate-t-distribution-on-top-of-permuted-t-statistics" class="section level2">
 <h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on top of permuted t-statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">visualize</span>(<span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
-<span class="st">    </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_perm, <span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
+<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
 <pre><code>## Warning: Check to make sure the conditions have been met for the
 ## theoretical method. {infer} currently does not check these for you.</code></pre>
 <p><img 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" /><!-- --></p>
diff --git a/vignettes/chisq_test.Rmd b/vignettes/chisq_test.Rmd
index 1cb2f535..2693b8c3 100644
--- a/vignettes/chisq_test.Rmd
+++ b/vignettes/chisq_test.Rmd
@@ -92,9 +92,9 @@ chisq_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "Chisq")
-chisq_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_chisq, direction = "greater")
+
+visualize(chisq_null_perm) +
+  shade_p_value(obs_stat = obs_chisq, direction = "greater")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -113,17 +113,16 @@ chisq_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "Chisq")
-chisq_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+
+visualize(chisq_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
 
 ```{r}
-chisq_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_chisq, direction = "right")
+visualize(chisq_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_chisq, direction = "right")
 ```
 
 ## Compute theoretical p-value
diff --git a/vignettes/observed_stat_examples.Rmd b/vignettes/observed_stat_examples.Rmd
index 30404cda..780512b3 100644
--- a/vignettes/observed_stat_examples.Rmd
+++ b/vignettes/observed_stat_examples.Rmd
@@ -69,9 +69,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 10) %>%
   generate(reps = 1000) %>%
   calculate(stat = "mean")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_bar, direction = "two_sided")
 ```
@@ -91,9 +91,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", mu = 8) %>%
   generate(reps = 1000) %>%
   calculate(stat = "t")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_bar, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_bar, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_bar, direction = "two_sided")
 ```
@@ -115,9 +115,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", med = -1) %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "median")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = x_tilde, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = x_tilde, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = x_tilde, direction = "two_sided")
 ```
@@ -138,9 +138,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "point", p = .5) %>%
   generate(reps = 1000) %>%
   calculate(stat = "prop")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = p_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = p_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = p_hat, direction = "two_sided")
 ```
@@ -177,9 +177,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "diff in props", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -200,9 +200,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000) %>% 
   calculate(stat = "z", order = c("winter", "summer"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = z_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = z_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = z_hat, direction = "two_sided")
 ```
@@ -230,9 +230,9 @@ null_distn <- fli_small %>%
               p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
   generate(reps = 1000, type = "simulate") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -253,9 +253,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>% 
   generate(reps = 1000, type = "permute") %>% 
   calculate(stat = "Chisq")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = Chisq_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = Chisq_hat, direction = "greater")
 ```
@@ -276,9 +276,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in means", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -299,9 +299,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -325,9 +325,9 @@ null_distn <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "diff in medians", order = c("summer", "winter"))
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = d_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = d_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = d_hat, direction = "two_sided")
 ```
@@ -348,9 +348,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "F")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = F_hat, direction = "greater")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = F_hat, direction = "greater")
 null_distn %>%
   get_p_value(obs_stat = F_hat, direction = "greater")
 ```
@@ -371,9 +371,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "slope")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = slope_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = slope_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = slope_hat, direction = "two_sided")
 ```
@@ -394,9 +394,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "correlation")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = correlation_hat, direction = "two_sided")
 ```
@@ -420,9 +420,9 @@ null_distn <- fli_small %>%
    hypothesize(null = "independence") %>%
    generate(reps = 1000, type = "permute") %>%
    calculate(stat = "t")
-null_distn %>% 
-  visualize() +
-    shade_p_value(obs_stat = t_hat, direction = "two_sided")
+
+visualize(null_distn) +
+  shade_p_value(obs_stat = t_hat, direction = "two_sided")
 null_distn %>%
   get_p_value(obs_stat = t_hat, direction = "two_sided")
 ```
@@ -446,13 +446,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "mean")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical (one mean - standardized)
@@ -471,13 +471,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -497,13 +497,13 @@ boot <- fli_small %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One categorical variable (standardized proportion $z$)
@@ -526,13 +526,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "diff in means", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### One numerical variable, one categorical (2 levels) (t)
@@ -551,13 +551,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -577,13 +577,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "diff in props", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two categorical variables (z)
@@ -602,13 +602,13 @@ boot <- fli_small %>%
   generate(reps = 1000, type = "bootstrap") %>% 
   calculate(stat = "z", order = c("summer", "winter"))
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -628,13 +628,13 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "slope")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 ### Two numerical vars - correlation
@@ -653,14 +653,14 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "correlation")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", 
                             point_estimate = correlation_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
 
 
@@ -682,11 +682,11 @@ boot <- fli_small %>%
    generate(reps = 1000, type = "bootstrap") %>%
    calculate(stat = "t")
 ( percentile_ci <- get_ci(boot) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = percentile_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = percentile_ci)
 ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-boot %>%
-  visualize() +
-    shade_confidence_interval(endpoints = standard_error_ci)
+
+visualize(boot) +
+  shade_confidence_interval(endpoints = standard_error_ci)
 ```
diff --git a/vignettes/two_sample_t.Rmd b/vignettes/two_sample_t.Rmd
index a4ef6534..e4a2b100 100755
--- a/vignettes/two_sample_t.Rmd
+++ b/vignettes/two_sample_t.Rmd
@@ -92,9 +92,9 @@ t_null_perm <- fli_small %>%
   hypothesize(null = "independence") %>%
   generate(reps = 1000, type = "permute") %>%
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_perm %>%
-  visualize() +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_perm) +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Calculate the randomization-based $p$-value
@@ -114,17 +114,16 @@ t_null_theor <- fli_small %>%
   hypothesize(null = "independence") %>%
   # generate() ## Not used for theoretical
   calculate(stat = "t", order = c("h1", "h2"))
-t_null_theor %>%
-  visualize(method = "theoretical") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+
+visualize(t_null_theor, method = "theoretical") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Overlay appropriate $t$ distribution on top of permuted t-statistics
 
 ```{r}
-t_null_perm %>% 
-  visualize(method = "both") +
-    shade_p_value(obs_stat = obs_t, direction = "two_sided")
+visualize(t_null_perm, method = "both") +
+  shade_p_value(obs_stat = obs_t, direction = "two_sided")
 ```
 
 ## Compute theoretical p-value

From be1f24537edb62e8b9539b24028d3b3fb7639191 Mon Sep 17 00:00:00 2001
From: Brian Fannin <captain@pirategrunt.com>
Date: Mon, 24 Sep 2018 16:03:29 -0400
Subject: [PATCH 65/78] Correct spelling of PirateGrunt's lasts name

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 8411bd6b..7e036e33 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -15,7 +15,7 @@ Authors@R: c(
     person("Neal", "Fultz", email = "nfultz@gmail.com", role = "ctb"),
     person("Doug", "Friedman", email = "doug.nhp@gmail.com", role = "ctb"),
     person("Richie", "Cotton", email = "richie@datacamp.com", role = "ctb"),
-    person("Brian", "Fannon", email = "captain@pirategrunt.com", role = "ctb"))
+    person("Brian", "Fannin", email = "captain@pirategrunt.com", role = "ctb"))
 Description: The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework. 
 License: CC0
 Encoding: UTF-8

From 802068b7ea4833fa0d896a08e5c720abafd6ac48 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Sat, 27 Oct 2018 21:41:15 +0300
Subject: [PATCH 66/78] Update formatting in 'get_p_value.R'.

---
 R/get_p_value.R | 27 ++++++++++++---------------
 1 file changed, 12 insertions(+), 15 deletions(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index f081f98a..d313e94d 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -40,8 +40,7 @@ NULL
 
 #' @rdname get_p_value
 #' @export
-get_p_value <- function(x, obs_stat, direction){
-
+get_p_value <- function(x, obs_stat, direction) {
   check_type(x, is.data.frame)
   if(!is_generated(x) & is_hypothesized(x)) {
     stop_glue(
@@ -56,7 +55,8 @@ get_p_value <- function(x, obs_stat, direction){
   pvalue <- simulation_based_p_value(
     x = x, 
     obs_stat = obs_stat,
-    direction = direction)
+    direction = direction
+  )
 
   ## Theoretical-based p-value
   # Could be more specific
@@ -81,26 +81,22 @@ get_pvalue <- function(x, obs_stat, direction) {
   get_p_value(x = x, obs_stat = obs_stat, direction = direction)
 }
 
-simulation_based_p_value <- function(x, obs_stat, direction){
-
-  if(direction %in% c("less", "left")){
+simulation_based_p_value <- function(x, obs_stat, direction) {
+  if (direction %in% c("less", "left")) {
     p_value <- x %>%
       dplyr::summarize(p_value = mean(stat <= obs_stat))
-  }
-  else if(direction %in% c("greater", "right")){
+  } else if (direction %in% c("greater", "right")) {
     p_value <- x %>%
       dplyr::summarize(p_value = mean(stat >= obs_stat))
-  }
-  else{
+  } else {
     p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
   }
 
   p_value
 }
 
-two_sided_p_value <- function(x, obs_stat){
-
-  if(stats::median(x$stat) >= obs_stat){
+two_sided_p_value <- function(x, obs_stat) {
+  if (stats::median(x$stat) >= obs_stat) {
     basic_p_value <- get_percentile(x$stat, obs_stat) +
       (1 - get_percentile(x$stat, stats::median(x$stat) +
           stats::median(x$stat) - obs_stat))
@@ -110,13 +106,14 @@ two_sided_p_value <- function(x, obs_stat){
           stats::median(x$stat) - obs_stat))
   }
 
-  if(basic_p_value >= 1)
+  if (basic_p_value >= 1) {
     # Catch all if adding both sides produces a number
     # larger than 1. Should update with test in that
     # scenario instead of using >=
     return(tibble::tibble(p_value = 1))
-  else
+  } else {
     return(tibble::tibble(p_value = basic_p_value))
+  }
 }
 
 is_generated <- function(x) {

From f10fa5e52ec072162dda3e52feec7dd614e8bd82 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Sat, 27 Oct 2018 22:28:52 +0300
Subject: [PATCH 67/78] Update `get_p_value()` (#205).

Details:
- Changed method of computing two-sided p-value.
- It now returns tibble.
---
 R/get_p_value.R                   | 43 ++++++++-----------
 man/get_p_value.Rd                |  2 +-
 tests/testthat/test-get_p_value.R | 69 ++++++++++---------------------
 3 files changed, 41 insertions(+), 73 deletions(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index d313e94d..7e8119e6 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -8,7 +8,7 @@
 #' @param direction A character string. Options are `"less"`, `"greater"`, or
 #'   `"two_sided"`. Can also use `"left"`, `"right"`, or `"both"`.
 #'
-#' @return A 1x1 data frame with value between 0 and 1.
+#' @return A 1x1 [tibble][tibble::tibble] with value between 0 and 1.
 #'
 #' @section Aliases:
 #' `get_pvalue()` is an alias of `get_p_value()`.
@@ -83,37 +83,30 @@ get_pvalue <- function(x, obs_stat, direction) {
 
 simulation_based_p_value <- function(x, obs_stat, direction) {
   if (direction %in% c("less", "left")) {
-    p_value <- x %>%
-      dplyr::summarize(p_value = mean(stat <= obs_stat))
+    pval <- left_p_value(x[["stat"]], obs_stat)
   } else if (direction %in% c("greater", "right")) {
-    p_value <- x %>%
-      dplyr::summarize(p_value = mean(stat >= obs_stat))
+    pval <- right_p_value(x[["stat"]], obs_stat)
   } else {
-    p_value <- x %>% two_sided_p_value(obs_stat = obs_stat)
+    pval <- two_sided_p_value(x[["stat"]], obs_stat)
   }
 
-  p_value
+  tibble::tibble(p_value = pval)
 }
 
-two_sided_p_value <- function(x, obs_stat) {
-  if (stats::median(x$stat) >= obs_stat) {
-    basic_p_value <- get_percentile(x$stat, obs_stat) +
-      (1 - get_percentile(x$stat, stats::median(x$stat) +
-          stats::median(x$stat) - obs_stat))
-  } else {
-    basic_p_value <- 1 - get_percentile(x$stat, obs_stat) +
-      (get_percentile(x$stat, stats::median(x$stat) +
-          stats::median(x$stat) - obs_stat))
-  }
+left_p_value <- function(vec, obs_stat) {
+  mean(vec <= obs_stat)
+}
 
-  if (basic_p_value >= 1) {
-    # Catch all if adding both sides produces a number
-    # larger than 1. Should update with test in that
-    # scenario instead of using >=
-    return(tibble::tibble(p_value = 1))
-  } else {
-    return(tibble::tibble(p_value = basic_p_value))
-  }
+right_p_value <- function(vec, obs_stat) {
+  mean(vec >= obs_stat)
+}
+
+two_sided_p_value <- function(vec, obs_stat) {
+  left_pval <- left_p_value(vec, obs_stat)
+  right_pval <- right_p_value(vec, obs_stat)
+  raw_res <- 2 * min(left_pval, right_pval)
+  
+  min(raw_res, 1)
 }
 
 is_generated <- function(x) {
diff --git a/man/get_p_value.Rd b/man/get_p_value.Rd
index 83e5cac1..21a7ab73 100644
--- a/man/get_p_value.Rd
+++ b/man/get_p_value.Rd
@@ -19,7 +19,7 @@ extreme than this).}
 \code{"two_sided"}. Can also use \code{"left"}, \code{"right"}, or \code{"both"}.}
 }
 \value{
-A 1x1 data frame with value between 0 and 1.
+A 1x1 \link[tibble:tibble]{tibble} with value between 0 and 1.
 }
 \description{
 Simulation-based methods are (currently only) supported.
diff --git a/tests/testthat/test-get_p_value.R b/tests/testthat/test-get_p_value.R
index b1d78aec..832466a0 100644
--- a/tests/testthat/test-get_p_value.R
+++ b/tests/testthat/test-get_p_value.R
@@ -1,60 +1,36 @@
 context("get_p_value")
 
 set.seed(2018)
-test_df <- tibble::tibble(stat = rnorm(100))
+test_df <- tibble::tibble(
+  stat = sample(c(
+    -5, -4, -4, -4, -1, -0.5, rep(0, 6), 1, 1, 3.999, 4, 4, 4.001, 5, 5
+  ))
+)
 
 test_that("direction is appropriate", {
   expect_error(test_df %>% get_p_value(obs_stat = 0.5, direction = "righ"))
 })
 
-test_that("get_p_value makes sense", {
-  expect_silent(
-    test_df %>% 
-      get_p_value(obs_stat = 0.7, direction = "right")
-    )
-  expect_lt(
-    iris_calc %>%
-      get_p_value(obs_stat = 0.1, direction = "right") %>%
-      dplyr::pull(),
-    expected = 0.1
-  )
-  expect_gt(
-    iris_calc %>%
-      get_p_value(obs_stat = -0.1, direction = "greater") %>%
-      dplyr::pull(),
-    expected = 0.9
-  )
+test_that("get_p_value works", {
+  expect_equal(get_p_value(test_df, 4, "right")[[1]][1], 5/20)
+  expect_equal(get_p_value(test_df, 4, "left")[[1]][1], 17/20)
+  expect_equal(get_p_value(test_df, 4, "both")[[1]][1], 10/20)
+  
+  expect_equal(get_p_value(test_df, 0, "right")[[1]][1], 14/20)
+  expect_equal(get_p_value(test_df, 0, "left")[[1]][1], 12/20)
+  # This is also a check for not returning value more than 1
+  expect_equal(get_p_value(test_df, 0, "both")[[1]][1], 1)
+  
+  expect_equal(get_p_value(test_df, -3.999, "right")[[1]][1], 16/20)
+  expect_equal(get_p_value(test_df, -3.999, "left")[[1]][1], 4/20)
+  expect_equal(get_p_value(test_df, -3.999, "both")[[1]][1], 8/20)
+  
   expect_equal(
-    iris_calc %>%
-      get_p_value(obs_stat = median(iris_calc$stat), direction = "both") %>%
-      dplyr::pull(),
-    expected = 1
-  )
-  expect_lt(
-    iris_calc %>%
-      get_p_value(obs_stat = -0.2, direction = "left") %>%
-      dplyr::pull(),
-    expected = 0.02
-  )
-  expect_gt(
-    iris_calc %>%
-      get_p_value(obs_stat = -0.2, direction = "right") %>%
-      dplyr::pull(),
-    expected = 0.98
+    get_p_value(test_df, 4, "greater"), get_p_value(test_df, 4, "right")
   )
+  expect_equal(get_p_value(test_df, 4, "less"), get_p_value(test_df, 4, "left"))
   expect_equal(
-    iris_calc %>%
-      get_p_value(
-        obs_stat = median(iris_calc$stat) + 1, direction = "two_sided"
-      ) %>%
-      dplyr::pull(),
-    expected = 0
-  )
-  expect_error(
-    iris_calc %>%
-      get_p_value(
-        obs_stat = median(iris_calc$stat) + 1, direction = "wrong"
-      )
+    get_p_value(test_df, 4, "two_sided"), get_p_value(test_df, 4, "both")
   )
 })
 
@@ -69,5 +45,4 @@ test_that("theoretical p-value not supported error", {
       calculate(stat = "F") %>% 
       get_p_value(obs_stat = obs_F, direction = "right")
   )
-  
 })

From ba9e05a012a6d7aee3a12ce69163ddbd0979d8ee Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Sat, 27 Oct 2018 22:35:03 +0300
Subject: [PATCH 68/78] Mention breaking change of two-sided p-value method in
 'NEWS.md'.

---
 NEWS.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index c101e33e..49bc9c27 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # infer 0.3.1.9000
 
+## Breaking changes
+
+- Changed method of computing two-sided p-value to a more conventional one. It also makes `get_pvalue()` and `visualize()` more aligned (#205).
+
 ## Deprecation changes
 
 - Deprecated `p_value()` (use `get_p_value()` instead) (#180).

From 9466153eae0b624e927163c288d6727ef2f1ceb9 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Sat, 27 Oct 2018 22:45:44 +0300
Subject: [PATCH 69/78] Remove `get_percentile()`.

---
 R/visualize.R                   | 16 ++++++----------
 man/visualize.Rd                |  7 ++++---
 tests/testthat/test-visualize.R |  4 ++--
 3 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 119cce3b..186448d7 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -538,16 +538,6 @@ check_shade_confidence_interval_args <- function(color, fill) {
   }
 }
 
-get_percentile <- function(vector, observation) {
-  stats::ecdf(vector)(observation)
-}
-
-mirror_obs_stat <- function(vector, observation) {
-  obs_percentile <- get_percentile(vector, observation)
-  
-  stats::quantile(vector, probs = 1 - obs_percentile)
-}
-
 short_theory_type <- function(x) {
   theory_attr <- attr(x, "theory_type")
   theory_types <- list(
@@ -622,6 +612,12 @@ two_tail_data <- function(obs_stat, direction) {
   }
 }
 
+mirror_obs_stat <- function(vector, observation) {
+  obs_percentile <- stats::ecdf(vector)(observation)
+  
+  stats::quantile(vector, probs = 1 - obs_percentile)
+}
+
 get_viz_method <- function(data) {
   attr(data, "viz_method")
 }
diff --git a/man/visualize.Rd b/man/visualize.Rd
index 56697ee4..5494cb3e 100755
--- a/man/visualize.Rd
+++ b/man/visualize.Rd
@@ -68,11 +68,12 @@ the theoretical distribution (or both!).
 \details{
 In order to make visualization workflow more straightforward and
 explicit \code{visualize()} now only should be used to plot statistics directly.
-That is why arguments, not related to this task, are deprecated and will be
+That is why arguments not related to this task are deprecated and will be
 removed in a future release of \{infer\}.
 
-To add information related to p-value use \code{\link[=shade_p_value]{shade_p_value()}}. To add
-information related to confidence interval use \code{\link[=shade_confidence_interval]{shade_confidence_interval()}}.
+To add to plot information related to p-value use \code{\link[=shade_p_value]{shade_p_value()}}. To add
+to plot information related to confidence interval use
+\code{\link[=shade_confidence_interval]{shade_confidence_interval()}}.
 }
 \examples{
 # Permutations to create a simulation-based null distribution for
diff --git a/tests/testthat/test-visualize.R b/tests/testthat/test-visualize.R
index 53222d9f..26c0e750 100644
--- a/tests/testthat/test-visualize.R
+++ b/tests/testthat/test-visualize.R
@@ -297,8 +297,8 @@ test_that("visualize basic tests", {
   )
 })
 
-test_that("get_percentile works", {
-  expect_equal(get_percentile(1:10, 4), 0.4)
+test_that("mirror_obs_stat works", {
+  expect_equal(mirror_obs_stat(1:10, 4), c(`60%` = 6.4))
 })
 
 test_that("obs_stat as a data.frame works", {

From b71939affd046f063099f6d936d393aa4ca4d940 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Sat, 27 Oct 2018 22:50:15 +0300
Subject: [PATCH 70/78] Fix missed formatting issues in `get_p_value.R`.

---
 R/get_p_value.R | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/R/get_p_value.R b/R/get_p_value.R
index 7e8119e6..0081053d 100644
--- a/R/get_p_value.R
+++ b/R/get_p_value.R
@@ -42,7 +42,7 @@ NULL
 #' @export
 get_p_value <- function(x, obs_stat, direction) {
   check_type(x, is.data.frame)
-  if(!is_generated(x) & is_hypothesized(x)) {
+  if (!is_generated(x) & is_hypothesized(x)) {
     stop_glue(
       "Theoretical p-values are not yet supported.",
       "`x` should be the result of calling `generate()`.",
@@ -52,11 +52,7 @@ get_p_value <- function(x, obs_stat, direction) {
   obs_stat <- check_obs_stat(obs_stat)
   check_direction(direction)
 
-  pvalue <- simulation_based_p_value(
-    x = x, 
-    obs_stat = obs_stat,
-    direction = direction
-  )
+  simulation_based_p_value(x = x, obs_stat = obs_stat, direction = direction)
 
   ## Theoretical-based p-value
   # Could be more specific
@@ -71,8 +67,6 @@ get_p_value <- function(x, obs_stat, direction) {
   #                     obs_stat = obs_stat,
   #                     direction = direction)
   # }
-
-  return(pvalue)
 }
 
 #' @rdname get_p_value

From 7f45b2f750e85e0ad7b14aba7973ef248aea5075 Mon Sep 17 00:00:00 2001
From: Andrew Bray <andrew.bray@gmail.com>
Date: Wed, 7 Nov 2018 17:05:21 -0800
Subject: [PATCH 71/78] switch over to publish at gh-pages

---
 .travis.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index d37dc8cc..702ebfa0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -19,7 +19,7 @@ notifications:
 
 r_packages:
   - devtools
-  
+
 r_github_packages:
   - hadley/pkgdown
   - tidymodels/infer
@@ -33,8 +33,8 @@ deploy:
   keep-history: true
   local-dir: docs
   github_token: $GITHUBTRAVIS
-  target-branch: gh-pages-dev
-#  target-branch: gh-pages
+#  target-branch: gh-pages-dev
+  target-branch: gh-pages
 
 after_success:
   - Rscript -e 'covr::codecov()'

From c7b29bd574b7275f9dce134fd07828850a967acc Mon Sep 17 00:00:00 2001
From: Andrew Bray <andrew.bray@gmail.com>
Date: Wed, 7 Nov 2018 17:06:11 -0800
Subject: [PATCH 72/78] fix typo

---
 R/visualize.R | 120 +++++++++++++++++++++++++-------------------------
 1 file changed, 60 insertions(+), 60 deletions(-)

diff --git a/R/visualize.R b/R/visualize.R
index 186448d7..7095a5c0 100755
--- a/R/visualize.R
+++ b/R/visualize.R
@@ -37,11 +37,11 @@
 #' explicit `visualize()` now only should be used to plot statistics directly.
 #' That is why arguments not related to this task are deprecated and will be
 #' removed in a future release of \\{infer\\}.
-#' 
+#'
 #' To add to plot information related to p-value use [shade_p_value()]. To add
 #' to plot information related to confidence interval use
 #' [shade_confidence_interval()].
-#' 
+#'
 #' @return A ggplot object showing the simulation-based distribution as a
 #'   histogram or bar graph. Also used to show the theoretical curves.
 #'
@@ -97,15 +97,15 @@ visualize <- function(data, bins = 15, method = "simulation",
     data, bins, method, dens_color, obs_stat, obs_stat_color,
     pvalue_fill, direction, endpoints, endpoints_color, ci_fill
   )
-  warn_depricated_args(obs_stat, endpoints)
+  warn_deprecated_args(obs_stat, endpoints)
   endpoints <- impute_endpoints(endpoints)
   obs_stat <- impute_obs_stat(obs_stat, direction, endpoints)
-  
+
   # Add `method` to `data` attributes to enable later possibility of
   # complicated computation of p-value regions (in case `direction = "both"`)
   # in `shade_p_value()`.
   attr(data, "viz_method") <- method
-  
+
   infer_plot <- ggplot(data) +
     simulation_layer(data, bins, ...) +
     theoretical_layer(data, dens_color, ...) +
@@ -113,12 +113,12 @@ visualize <- function(data, bins = 15, method = "simulation",
     shade_p_value(
       obs_stat, direction, obs_stat_color, pvalue_fill, ...
     )
-  
+
   if (!is.null(direction) && (direction == "between")) {
     infer_plot <- infer_plot +
       shade_confidence_interval(endpoints, endpoints_color, ci_fill, ...)
   }
-  
+
   infer_plot
 }
 
@@ -147,14 +147,14 @@ check_visualize_args <- function(data, bins, method, dens_color,
       "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
     )
   }
-  
+
   if (!(method %in% c("simulation", "theoretical", "both"))) {
     stop_glue(
       'Provide `method` with one of three options: `"theoretical"`, `"both"`, ',
       'or `"simulation"`. `"simulation"` is the default.'
     )
   }
-  
+
   if (method == "both") {
     if (!("stat" %in% names(data))) {
       stop_glue(
@@ -162,7 +162,7 @@ check_visualize_args <- function(data, bins, method, dens_color,
         'to `visualize(method = "both")`'
       )
     }
-    
+
     if (
       ("replicate" %in% names(data)) && (length(unique(data$replicate)) < 100)
     ) {
@@ -172,18 +172,18 @@ check_visualize_args <- function(data, bins, method, dens_color,
       )
     }
   }
-  
+
   if (!is.null(obs_stat) && !is.null(endpoints)) {
     warning_glue(
       "Values for both `endpoints` and `obs_stat` were given when only one ",
       "should be set. Ignoring `obs_stat` values."
     )
   }
-  
+
   TRUE
 }
 
-warn_depricated_args <- function(obs_stat, endpoints) {
+warn_deprecated_args <- function(obs_stat, endpoints) {
   if (!is.null(obs_stat)) {
     warning_glue(
       "`visualize()` shouldn't be used to plot p-value. Arguments `obs_stat`, ",
@@ -191,7 +191,7 @@ warn_depricated_args <- function(obs_stat, endpoints) {
       "Use `shade_p_value()` instead."
     )
   }
-  
+
   if (!is.null(endpoints)) {
     warning_glue(
       "`visualize()` shouldn't be used to plot confidence interval. Arguments ",
@@ -199,7 +199,7 @@ warn_depricated_args <- function(obs_stat, endpoints) {
       "Use `shade_confidence_interval()` instead."
     )
   }
-  
+
   TRUE
 }
 
@@ -211,23 +211,23 @@ impute_endpoints <- function(endpoints) {
     )
     endpoints <- endpoints[1:2]
   }
-  
+
   if (is.data.frame(endpoints)) {
     if ((nrow(endpoints) != 1) || (ncol(endpoints) != 2)) {
       stop_glue(
         "Expecting `endpoints` to be a 1 x 2 data frame or 2 element vector."
       )
     }
-    
+
     endpoints <- unlist(endpoints)
   }
-  
+
   endpoints
 }
 
 impute_obs_stat <- function(obs_stat, direction, endpoints) {
   obs_stat <- check_obs_stat(obs_stat)
-  
+
   if (
     !is.null(direction) &&
     (is.null(obs_stat) + is.null(endpoints) != 1)
@@ -237,17 +237,17 @@ impute_obs_stat <- function(obs_stat, direction, endpoints) {
       "or the observed statistic `obs_stat` to be provided."
     )
   }
-  
+
   obs_stat
 }
 
 simulation_layer <- function(data, bins, ...) {
   method <- get_viz_method(data)
-  
+
   if (method == "theoretical") {
     return(list())
   }
-  
+
   if (method == "simulation") {
     if (length(unique(data$stat)) >= 10) {
       res <- list(
@@ -266,21 +266,21 @@ simulation_layer <- function(data, bins, ...) {
       )
     )
   }
-  
+
   res
 }
 
 theoretical_layer <- function(data, dens_color, ...) {
   method <- get_viz_method(data)
-  
+
   if (method == "simulation") {
     return(list())
   }
-  
+
   warn_theoretical_layer(data)
-  
+
   theory_type <- short_theory_type(data)
-  
+
   switch(
     theory_type,
     t = theory_curve(
@@ -302,12 +302,12 @@ theoretical_layer <- function(data, dens_color, ...) {
 
 warn_theoretical_layer <- function(data) {
   method <- get_viz_method(data)
-  
+
   warning_glue(
     "Check to make sure the conditions have been met for the theoretical ",
     "method. {{infer}} currently does not check these for you."
   )
-  
+
   if (
     !is_nuat(data, "stat") &&
     !(attr(data, "stat") %in% c("t", "z", "Chisq", "F"))
@@ -329,7 +329,7 @@ warn_theoretical_layer <- function(data) {
 theory_curve <- function(method, d_fun, q_fun, args_list, dens_color) {
   if (method == "theoretical") {
     x_range <- do.call(q_fun, c(p = list(c(0.001, 0.999)), args_list))
-    
+
     res <- list(
       stat_function(
         data = data.frame(x = x_range), mapping = aes(x),
@@ -344,24 +344,24 @@ theory_curve <- function(method, d_fun, q_fun, args_list, dens_color) {
       )
     )
   }
-  
+
   res
 }
 
 title_labels_layer <- function(data) {
   method <- get_viz_method(data)
   theory_type <- short_theory_type(data)
-  
+
   title_string <- switch(
     method,
     simulation = "Simulation-Based Null Distribution",
     theoretical = "Theoretical {theory_type} Null Distribution",
     both = "Simulation-Based and Theoretical {theory_type} Null Distributions"
   )
-  
+
   x_lab <- switch(method, simulation = "stat", "{theory_type} stat")
   y_lab <- switch(method, simulation = "count", "density")
-  
+
   list(
     ggtitle(glue_null(title_string)),
     xlab(glue_null(x_lab)),
@@ -370,11 +370,11 @@ title_labels_layer <- function(data) {
 }
 
 #' Add information about p-value region(s)
-#' 
+#'
 #' `shade_p_value()` plots p-value region(s) on top of the [visualize()] output.
 #' It should be used as \\{ggplot2\\} layer function (see examples).
 #' `shade_pvalue()` is its alias.
-#' 
+#'
 #' @param obs_stat A numeric value or 1x1 data frame corresponding to what the
 #'   observed statistic is.
 #' @param direction A string specifying in which direction the shading should
@@ -389,7 +389,7 @@ title_labels_layer <- function(data) {
 #'
 #' @return A list of \\{ggplot2\\} objects to be added to the `visualize()`
 #'   output.
-#'   
+#'
 #' @seealso [shade_confidence_interval()] to add information about confidence
 #'   interval.
 #'
@@ -401,11 +401,11 @@ title_labels_layer <- function(data) {
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "t", order = c("1", "0")) %>%
 #'   visualize(method = "both")
-#' 
+#'
 #' viz_plot + shade_p_value(1.5, direction = "right")
 #' viz_plot + shade_p_value(1.5, direction = "both")
 #' viz_plot + shade_p_value(1.5, direction = NULL)
-#' 
+#'
 #' @name shade_p_value
 NULL
 
@@ -415,21 +415,21 @@ shade_p_value <- function(obs_stat, direction,
                           color = "red2", fill = "pink", ...) {
   obs_stat <- check_obs_stat(obs_stat)
   check_shade_p_value_args(obs_stat, direction, color, fill)
-  
+
   res <- list()
   if (is.null(obs_stat)) {
     return(res)
   }
-  
+
   # Add shading
   if (!is.null(direction) && !is.null(fill)) {
     if (direction %in% c("less", "left", "greater", "right")) {
       tail_data <- one_tail_data(obs_stat, direction)
-      
+
       res <- c(res, list(geom_tail(tail_data, fill, ...)))
     } else if (direction %in% c("two_sided", "both")) {
       tail_data <- two_tail_data(obs_stat, direction)
-      
+
       res <- c(res, list(geom_tail(tail_data, fill, ...)))
     } else {
       warning_glue(
@@ -438,7 +438,7 @@ shade_p_value <- function(obs_stat, direction,
       )
     }
   }
-  
+
   # Add vertical line at `obs_stat`
   c(
     res, list(geom_vline(xintercept = obs_stat, size = 2, color = color, ...))
@@ -458,16 +458,16 @@ check_shade_p_value_args <- function(obs_stat, direction, color, fill) {
   }
   check_type(color, is_color_string, "color string")
   check_type(fill, is_color_string, "color string")
-  
+
   TRUE
 }
 
 #' Add information about confidence interval
-#' 
+#'
 #' `shade_confidence_interval()` plots confidence interval region on top of the
 #' [visualize()] output. It should be used as \\{ggplot2\\} layer function (see
 #' examples). `shade_ci()` is its alias.
-#' 
+#'
 #' @param endpoints A 2 element vector or a 1 x 2 data frame containing the
 #'   lower and upper values to be plotted. Most useful for visualizing
 #'   conference intervals.
@@ -478,7 +478,7 @@ check_shade_p_value_args <- function(obs_stat, direction, color, fill) {
 #' @param ... Other arguments passed along to \\{ggplot2\\} functions.
 #' @return A list of \\{ggplot2\\} objects to be added to the `visualize()`
 #'   output.
-#'   
+#'
 #' @seealso [shade_p_value()] to add information about p-value region.
 #'
 #' @examples
@@ -489,10 +489,10 @@ check_shade_p_value_args <- function(obs_stat, direction, color, fill) {
 #'   generate(reps = 100, type = "permute") %>%
 #'   calculate(stat = "t", order = c("1", "0")) %>%
 #'   visualize(method = "both")
-#' 
+#'
 #' viz_plot + shade_confidence_interval(c(-1.5, 1.5))
 #' viz_plot + shade_confidence_interval(c(-1.5, 1.5), fill = NULL)
-#' 
+#'
 #' @name shade_confidence_interval
 NULL
 
@@ -502,12 +502,12 @@ shade_confidence_interval <- function(endpoints, color = "mediumaquamarine",
                                       fill = "turquoise", ...) {
   endpoints <- impute_endpoints(endpoints)
   check_shade_confidence_interval_args(color, fill)
-  
+
   res <- list()
   if (is.null(endpoints)) {
     return(res)
   }
-  
+
   if (!is.null(fill)) {
     res <- c(
       res, list(
@@ -521,7 +521,7 @@ shade_confidence_interval <- function(endpoints, color = "mediumaquamarine",
       )
     )
   }
-  
+
   c(
     res, list(geom_vline(xintercept = endpoints, size = 2, color = color, ...))
   )
@@ -546,9 +546,9 @@ short_theory_type <- function(x) {
     z = c("One sample prop z", "Two sample props z"),
     `Chi-Square` = c("Chi-square test of indep", "Chi-square Goodness of Fit")
   )
-  
+
   is_type <- vapply(theory_types, function(x) {theory_attr %in% x}, logical(1))
-  
+
   names(theory_types)[which(is_type)[1]]
 }
 
@@ -560,7 +560,7 @@ warn_right_tail_test <- function(direction, stat_name) {
       "Proceed with caution."
     )
   }
-  
+
   TRUE
 }
 
@@ -581,7 +581,7 @@ one_tail_data <- function(obs_stat, direction) {
   # Needed to warn about incorrect usage of right tail tests.
   function(data) {
     warn_right_tail_test(direction, short_theory_type(data))
-    
+
     if (direction %in% c("less", "left")) {
       data.frame(x_min = -Inf, x_max = obs_stat)
     } else if (direction %in% c("greater", "right")) {
@@ -598,13 +598,13 @@ two_tail_data <- function(obs_stat, direction) {
   # Also needed to warn about incorrect usage of right tail tests.
   function(data) {
     warn_right_tail_test(direction, short_theory_type(data))
-    
+
     if (get_viz_method(data) == "theoretical") {
       second_border <- -obs_stat
     } else {
       second_border <- mirror_obs_stat(data$stat, obs_stat)
     }
-    
+
     data.frame(
       x_min = c(-Inf, max(obs_stat, second_border)),
       x_max = c(min(obs_stat, second_border), Inf)
@@ -614,7 +614,7 @@ two_tail_data <- function(obs_stat, direction) {
 
 mirror_obs_stat <- function(vector, observation) {
   obs_percentile <- stats::ecdf(vector)(observation)
-  
+
   stats::quantile(vector, probs = 1 - obs_percentile)
 }
 

From 2c542bce91570f3369b4f422bde155625c9cd7c4 Mon Sep 17 00:00:00 2001
From: Andrew Bray <andrew.bray@gmail.com>
Date: Sun, 11 Nov 2018 16:06:28 -0800
Subject: [PATCH 73/78] bump version for cran release

---
 NEWS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 49bc9c27..f3141d62 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# infer 0.3.1.9000
+# infer 0.4.0
 
 ## Breaking changes
 

From ffa63d40f73173626ccaecd40e26eb6b25f0f6e3 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester@datacamp.com>
Date: Sun, 11 Nov 2018 16:12:47 -0800
Subject: [PATCH 74/78] Bump to 0.4.0

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 7e036e33..12bdc562 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: infer
 Type: Package
 Title: Tidy Statistical Inference
-Version: 0.3.1.9000
+Version: 0.4.0
 Authors@R: c(
     person("Andrew", "Bray", email = "abray@reed.edu", role = c("aut", "cre")),
     person("Chester", "Ismay", email = "chester.ismay@gmail.com", role = "aut"),

From 93ded4d59249b439e343ee5f370c32cb8477ecf2 Mon Sep 17 00:00:00 2001
From: echasnovski <evgeni.chasnovski@gmail.com>
Date: Tue, 13 Nov 2018 22:16:36 +0200
Subject: [PATCH 75/78] Exclude possible vector conditions in `bootstrap()`
 (related to #210).

---
 R/generate.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/generate.R b/R/generate.R
index 5ee1e324..400f5b0b 100755
--- a/R/generate.R
+++ b/R/generate.R
@@ -99,7 +99,7 @@ bootstrap <- function(x, reps = 1, ...) {
     # If so, shift the variable chosen to have a mean corresponding
     # to that specified in `hypothesize`
     if (!is.null(attr(attr(x, "params"), "names"))){
-      if (attr(attr(x, "params"), "names") == "mu") {
+      if (identical(attr(attr(x, "params"), "names"), "mu")) {
         col <- as.character(attr(x, "response"))
 #      if (attr(x, "theory_type") != "One sample t") {
         x[[col]] <- x[[col]] - mean(x[[col]], na.rm = TRUE) + attr(x, "params")
@@ -117,7 +117,7 @@ bootstrap <- function(x, reps = 1, ...) {
     }
 
     # Similarly for median
-      else if (attr(attr(x, "params"), "names") == "med") {
+      else if (identical(attr(attr(x, "params"), "names"), "med")) {
         col <- as.character(attr(x, "response"))
         x[[col]] <- x[[col]] -
           stats::median(x[[col]], na.rm = TRUE) + attr(x, "params")
@@ -128,7 +128,7 @@ bootstrap <- function(x, reps = 1, ...) {
 
     # Similarly for sd
     ## Temporarily removed since this implementation does not scale correctly
-    # else if (attr(attr(x, "params"), "names") == "sigma") {
+    # else if (identical(attr(attr(x, "params"), "names"), "sigma")) {
     #   col <- as.character(attr(x, "response"))
     #   x[[col]] <- x[[col]] -
     #     stats::sd(x[[col]], na.rm = TRUE) + attr(x, "params")

From c60e759012ad230e63c54d016ac093e2a8e85284 Mon Sep 17 00:00:00 2001
From: Andrew Bray <andrew.bray@gmail.com>
Date: Tue, 13 Nov 2018 15:03:34 -0800
Subject: [PATCH 76/78] remove inst dir

---
 inst/doc/chisq_test.R                |   71 --
 inst/doc/chisq_test.Rmd              |  135 ---
 inst/doc/chisq_test.html             |  250 -----
 inst/doc/flights_examples.R          |  272 -----
 inst/doc/flights_examples.Rmd        |  358 -------
 inst/doc/flights_examples.html       |  461 ---------
 inst/doc/mtcars_examples.R           |  140 ---
 inst/doc/mtcars_examples.Rmd         |  223 -----
 inst/doc/mtcars_examples.html        |  468 ---------
 inst/doc/observed_stat_examples.R    |  485 ---------
 inst/doc/observed_stat_examples.Rmd  |  692 -------------
 inst/doc/observed_stat_examples.html | 1390 --------------------------
 inst/doc/two_sample_t.R              |   74 --
 inst/doc/two_sample_t.Rmd            |  138 ---
 inst/doc/two_sample_t.html           |  239 -----
 15 files changed, 5396 deletions(-)
 delete mode 100644 inst/doc/chisq_test.R
 delete mode 100644 inst/doc/chisq_test.Rmd
 delete mode 100644 inst/doc/chisq_test.html
 delete mode 100644 inst/doc/flights_examples.R
 delete mode 100644 inst/doc/flights_examples.Rmd
 delete mode 100644 inst/doc/flights_examples.html
 delete mode 100644 inst/doc/mtcars_examples.R
 delete mode 100644 inst/doc/mtcars_examples.Rmd
 delete mode 100644 inst/doc/mtcars_examples.html
 delete mode 100644 inst/doc/observed_stat_examples.R
 delete mode 100644 inst/doc/observed_stat_examples.Rmd
 delete mode 100644 inst/doc/observed_stat_examples.html
 delete mode 100644 inst/doc/two_sample_t.R
 delete mode 100755 inst/doc/two_sample_t.Rmd
 delete mode 100644 inst/doc/two_sample_t.html

diff --git a/inst/doc/chisq_test.R b/inst/doc/chisq_test.R
deleted file mode 100644
index fc1d4f24..00000000
--- a/inst/doc/chisq_test.R
+++ /dev/null
@@ -1,71 +0,0 @@
-## ----include=FALSE-------------------------------------------------------
-knitr::opts_chunk$set(fig.width = 8, fig.height = 3) 
-
-## ----message=FALSE, warning=FALSE----------------------------------------
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>% 
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-
-## ------------------------------------------------------------------------
-obs_chisq <- fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  calculate(stat = "Chisq")
-
-## ------------------------------------------------------------------------
-obs_chisq <- fli_small %>% 
-  chisq_test(formula = origin ~ season) %>% 
-  dplyr::select(statistic)
-
-## ------------------------------------------------------------------------
-obs_chisq <- fli_small %>% 
-  chisq_stat(formula = origin ~ season)
-
-## ------------------------------------------------------------------------
-chisq_null_perm <- fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "Chisq")
-
-visualize(chisq_null_perm) +
-  shade_p_value(obs_stat = obs_chisq, direction = "greater")
-
-## ------------------------------------------------------------------------
-chisq_null_perm %>% 
-  get_p_value(obs_stat = obs_chisq, direction = "greater")
-
-## ------------------------------------------------------------------------
-chisq_null_theor <- fli_small %>%
-  specify(origin ~ season) %>% 
-  hypothesize(null = "independence") %>%
-  # generate() ## Not used for theoretical
-  calculate(stat = "Chisq")
-
-visualize(chisq_null_theor, method = "theoretical") +
-  shade_p_value(obs_stat = obs_chisq, direction = "right")
-
-## ------------------------------------------------------------------------
-visualize(chisq_null_perm, method = "both") +
-  shade_p_value(obs_stat = obs_chisq, direction = "right")
-
-## ------------------------------------------------------------------------
-fli_small %>% 
-  chisq_test(formula = origin ~ season) %>% 
-  dplyr::pull(p_value)
-
diff --git a/inst/doc/chisq_test.Rmd b/inst/doc/chisq_test.Rmd
deleted file mode 100644
index 2693b8c3..00000000
--- a/inst/doc/chisq_test.Rmd
+++ /dev/null
@@ -1,135 +0,0 @@
----
-title: "Chi-squared test example using `nycflights13` `flights` data"
-author: "Chester Ismay"
-date: "`r Sys.Date()`"
-output: 
-  rmarkdown::html_vignette:
-    df_print: kable
-vignette: |
-  %\VignetteIndexEntry{Chi-squared test flights example}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r include=FALSE}
-knitr::opts_chunk$set(fig.width = 8, fig.height = 3) 
-```
-
-**Note**: The `type` argument in `generate()` is automatically filled based on the entries for `specify()` and
-`hypothesize()`. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.
-
-## Data preparation
-
-```{r message=FALSE, warning=FALSE}
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>% 
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-```
-
-* Two numeric - `arr_delay`, `dep_delay`
-* Two categories 
-    - `season` (`"winter"`, `"summer"`), 
-    - `day_hour` (`"morning"`, `"not morning"`)
-* Three categories - `origin` (`"EWR"`, `"JFK"`, `"LGA"`)
-* Sixteen categories - `carrier`
-
-***
-
-# One numerical variable, one categorical (2 levels)
-
-## Calculate observed statistic 
-
-The recommended approach is to use `specify() %>% calculate()`:
-
-```{r}
-obs_chisq <- fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  calculate(stat = "Chisq")
-```
-
-The observed $\chi^2$ statistic is `r obs_chisq`.
-
-Or using `chisq_test` in `infer`
-
-```{r}
-obs_chisq <- fli_small %>% 
-  chisq_test(formula = origin ~ season) %>% 
-  dplyr::select(statistic)
-```
-
-Again, the observed $\chi^2$ statistic is `r obs_chisq`.
-
-Or using another shortcut function in `infer`:
-
-```{r}
-obs_chisq <- fli_small %>% 
-  chisq_stat(formula = origin ~ season)
-```
-
-Lastly, the observed $\chi^2$ statistic is `r obs_chisq`.
-
-## Randomization approach to $\chi^2$-statistic
-
-```{r}
-chisq_null_perm <- fli_small %>%
-  specify(origin ~ season) %>% # alt: response = origin, explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "Chisq")
-
-visualize(chisq_null_perm) +
-  shade_p_value(obs_stat = obs_chisq, direction = "greater")
-```
-
-## Calculate the randomization-based $p$-value
-
-```{r}
-chisq_null_perm %>% 
-  get_p_value(obs_stat = obs_chisq, direction = "greater")
-```
-
-
-## Theoretical distribution
-
-```{r }
-chisq_null_theor <- fli_small %>%
-  specify(origin ~ season) %>% 
-  hypothesize(null = "independence") %>%
-  # generate() ## Not used for theoretical
-  calculate(stat = "Chisq")
-
-visualize(chisq_null_theor, method = "theoretical") +
-  shade_p_value(obs_stat = obs_chisq, direction = "right")
-```
-
-## Overlay appropriate $\chi^2$ distribution on top of permuted statistics
-
-```{r}
-visualize(chisq_null_perm, method = "both") +
-  shade_p_value(obs_stat = obs_chisq, direction = "right")
-```
-
-## Compute theoretical p-value
-
-```{r}
-fli_small %>% 
-  chisq_test(formula = origin ~ season) %>% 
-  dplyr::pull(p_value)
-```
-
diff --git a/inst/doc/chisq_test.html b/inst/doc/chisq_test.html
deleted file mode 100644
index b6f3c74b..00000000
--- a/inst/doc/chisq_test.html
+++ /dev/null
@@ -1,250 +0,0 @@
-<!DOCTYPE html>
-
-<html xmlns="http://www.w3.org/1999/xhtml">
-
-<head>
-
-<meta charset="utf-8" />
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="pandoc" />
-
-<meta name="viewport" content="width=device-width, initial-scale=1">
-
-<meta name="author" content="Chester Ismay" />
-
-<meta name="date" content="2018-09-24" />
-
-<title>Chi-squared test example using nycflights13 flights data</title>
-
-
-
-<style type="text/css">code{white-space: pre;}</style>
-<style type="text/css">
-div.sourceCode { overflow-x: auto; }
-table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
-  margin: 0; padding: 0; vertical-align: baseline; border: none; }
-table.sourceCode { width: 100%; line-height: 100%; }
-td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
-td.sourceCode { padding-left: 5px; }
-code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
-code > span.dt { color: #902000; } /* DataType */
-code > span.dv { color: #40a070; } /* DecVal */
-code > span.bn { color: #40a070; } /* BaseN */
-code > span.fl { color: #40a070; } /* Float */
-code > span.ch { color: #4070a0; } /* Char */
-code > span.st { color: #4070a0; } /* String */
-code > span.co { color: #60a0b0; font-style: italic; } /* Comment */
-code > span.ot { color: #007020; } /* Other */
-code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
-code > span.fu { color: #06287e; } /* Function */
-code > span.er { color: #ff0000; font-weight: bold; } /* Error */
-code > span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
-code > span.cn { color: #880000; } /* Constant */
-code > span.sc { color: #4070a0; } /* SpecialChar */
-code > span.vs { color: #4070a0; } /* VerbatimString */
-code > span.ss { color: #bb6688; } /* SpecialString */
-code > span.im { } /* Import */
-code > span.va { color: #19177c; } /* Variable */
-code > span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
-code > span.op { color: #666666; } /* Operator */
-code > span.bu { } /* BuiltIn */
-code > span.ex { } /* Extension */
-code > span.pp { color: #bc7a00; } /* Preprocessor */
-code > span.at { color: #7d9029; } /* Attribute */
-code > span.do { color: #ba2121; font-style: italic; } /* Documentation */
-code > span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
-code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
-code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
-</style>
-
-
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-<h1 class="title toc-ignore">Chi-squared test example using <code>nycflights13</code> <code>flights</code> data</h1>
-<h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-24</em></h4>
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-<p><strong>Note</strong>: The <code>type</code> argument in <code>generate()</code> is automatically filled based on the entries for <code>specify()</code> and <code>hypothesize()</code>. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.</p>
-<div id="data-preparation" class="section level2">
-<h2>Data preparation</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(nycflights13)
-<span class="kw">library</span>(dplyr)
-<span class="kw">library</span>(ggplot2)
-<span class="kw">library</span>(stringr)
-<span class="kw">library</span>(infer)
-<span class="kw">set.seed</span>(<span class="dv">2017</span>)
-fli_small &lt;-<span class="st"> </span>flights <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">na.omit</span>() <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">sample_n</span>(<span class="dt">size =</span> <span class="dv">500</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">season =</span> <span class="kw">case_when</span>(
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">10</span><span class="op">:</span><span class="dv">12</span>, <span class="dv">1</span><span class="op">:</span><span class="dv">3</span>) <span class="op">~</span><span class="st"> &quot;winter&quot;</span>,
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">4</span><span class="op">:</span><span class="dv">9</span>) <span class="op">~</span><span class="st"> &quot;summer&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour =</span> <span class="kw">case_when</span>(
-    <span class="kw">between</span>(hour, <span class="dv">1</span>, <span class="dv">12</span>) <span class="op">~</span><span class="st"> &quot;morning&quot;</span>,
-    <span class="kw">between</span>(hour, <span class="dv">13</span>, <span class="dv">24</span>) <span class="op">~</span><span class="st"> &quot;not morning&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)</code></pre></div>
-<ul>
-<li>Two numeric - <code>arr_delay</code>, <code>dep_delay</code></li>
-<li>Two categories
-<ul>
-<li><code>season</code> (<code>&quot;winter&quot;</code>, <code>&quot;summer&quot;</code>),</li>
-<li><code>day_hour</code> (<code>&quot;morning&quot;</code>, <code>&quot;not morning&quot;</code>)</li>
-</ul></li>
-<li>Three categories - <code>origin</code> (<code>&quot;EWR&quot;</code>, <code>&quot;JFK&quot;</code>, <code>&quot;LGA&quot;</code>)</li>
-<li>Sixteen categories - <code>carrier</code></li>
-</ul>
-<hr />
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels" class="section level1">
-<h1>One numerical variable, one categorical (2 levels)</h1>
-<div id="calculate-observed-statistic" class="section level2">
-<h2>Calculate observed statistic</h2>
-<p>The recommended approach is to use <code>specify() %&gt;% calculate()</code>:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_chisq &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)</code></pre></div>
-The observed <span class="math inline">\(\chi^2\)</span> statistic is
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.5719</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>.</p>
-<p>Or using <code>chisq_test</code> in <code>infer</code></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_chisq &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">chisq_test</span>(<span class="dt">formula =</span> origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(statistic)</code></pre></div>
-Again, the observed <span class="math inline">\(\chi^2\)</span> statistic is
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">statistic</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.5719</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>.</p>
-<p>Or using another shortcut function in <code>infer</code>:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_chisq &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">chisq_stat</span>(<span class="dt">formula =</span> origin <span class="op">~</span><span class="st"> </span>season)</code></pre></div>
-Lastly, the observed <span class="math inline">\(\chi^2\)</span> statistic is
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.5719</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>.</p>
-</div>
-<div id="randomization-approach-to-chi2-statistic" class="section level2">
-<h2>Randomization approach to <span class="math inline">\(\chi^2\)</span>-statistic</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = origin, explanatory = season</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-
-<span class="kw">visualize</span>(chisq_null_perm) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="calculate-the-randomization-based-p-value" class="section level2">
-<h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.748</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="theoretical-distribution" class="section level2">
-<h2>Theoretical distribution</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">chisq_null_theor &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-
-<span class="kw">visualize</span>(chisq_null_theor, <span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
-<pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="overlay-appropriate-chi2-distribution-on-top-of-permuted-statistics" class="section level2">
-<h2>Overlay appropriate <span class="math inline">\(\chi^2\)</span> distribution on top of permuted statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(chisq_null_perm, <span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_chisq, <span class="dt">direction =</span> <span class="st">&quot;right&quot;</span>)</code></pre></div>
-<pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="compute-theoretical-p-value" class="section level2">
-<h2>Compute theoretical p-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">chisq_test</span>(<span class="dt">formula =</span> origin <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(p_value)</code></pre></div>
-<pre><code>## [1] 0.7513</code></pre>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
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diff --git a/inst/doc/flights_examples.R b/inst/doc/flights_examples.R
deleted file mode 100644
index 0d8022e1..00000000
--- a/inst/doc/flights_examples.R
+++ /dev/null
@@ -1,272 +0,0 @@
-## ----include=FALSE-------------------------------------------------------
-knitr::opts_chunk$set(fig.width = 8, fig.height = 5) 
-
-## ----message=FALSE, warning=FALSE----------------------------------------
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>%
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-
-## ------------------------------------------------------------------------
-x_bar <- fli_small %>%
-  summarize(mean(dep_delay)) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 10) %>%
-  generate(reps = 1000, type = "bootstrap") %>%
-  calculate(stat = "mean")
-ggplot(data = null_distn, mapping = aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = x_bar, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= x_bar) * 2)
-
-## ------------------------------------------------------------------------
-x_tilde <- fli_small %>%
-  summarize(median(dep_delay)) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", med = -1) %>% 
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "median")
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = x_tilde, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= x_tilde) * 2)
-
-## ------------------------------------------------------------------------
-p_hat <- fli_small %>%
-  summarize(mean(day_hour == "morning")) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000, type = "simulate") %>%
-  calculate(stat = "prop")
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = p_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= p_hat) * 2)
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  mutate(day_hour_logical = (day_hour == "morning")) %>%
-  specify(response = day_hour_logical, success = "TRUE") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000, type = "simulate") %>%
-  calculate(stat = "prop")
-
-## ------------------------------------------------------------------------
-d_hat <- fli_small %>%
-  group_by(season) %>%
-  summarize(prop = mean(day_hour == "morning")) %>%
-  summarize(diff(prop)) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "diff in props", order = c("winter", "summer"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= d_hat) * 2) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-Chisq_hat <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  calculate(stat = "Chisq")
-null_distn <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  generate(reps = 1000, type = "simulate") %>% 
-  calculate(stat = "Chisq")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = pull(Chisq_hat), color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= pull(Chisq_hat))) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-Chisq_hat <- fli_small %>%
-  chisq_stat(formula = day_hour ~ origin)
-null_distn <- fli_small %>%
-  specify(day_hour ~ origin, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "Chisq")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = pull(Chisq_hat), color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= pull(Chisq_hat))) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(mean_stat = mean(dep_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(mean_stat)) %>% 
-  pull()
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("summer", "winter"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= d_hat) * 2) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(median_stat = median(dep_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(median_stat)) %>% 
-  pull()
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("summer", "winter"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= d_hat) * 2) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-F_hat <- anova(
-               aov(formula = arr_delay ~ origin, data = fli_small)
-               )$`F value`[1]
-null_distn <- fli_small %>%
-   specify(arr_delay ~ origin) %>% # alt: response = arr_delay, 
-   # explanatory = origin
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "F")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = F_hat, color = "red")  
-null_distn %>% 
-  summarize(p_value = mean(stat >= F_hat)) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
-  broom::tidy() %>% 
-  filter(term == "dep_delay") %>% 
-  pull(estimate)
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "slope")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = slope_hat, color = "red")  
-null_distn %>% 
-  summarize(p_value = mean(stat >= slope_hat) * 2) %>%
-  pull()
-
-## ------------------------------------------------------------------------
-x_bar <- fli_small %>%
-   summarize(mean(arr_delay)) %>%
-   pull()
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "mean") %>%
-   pull()
-c(lower = x_bar - 2 * sd(boot),
-  upper = x_bar + 2 * sd(boot))
-
-## ------------------------------------------------------------------------
-p_hat <- fli_small %>%
- summarize(mean(day_hour == "morning")) %>%
- pull()
-boot <- fli_small %>%
- specify(response = day_hour, success = "morning") %>%
- generate(reps = 1000, type = "bootstrap") %>%
- calculate(stat = "prop") %>%
- pull()
-c(lower = p_hat - 2 * sd(boot),
- upper = p_hat + 2 * sd(boot))
-
-## ------------------------------------------------------------------------
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(mean_stat = mean(arr_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(mean_stat)) %>% 
-  pull()
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "diff in means", order = c("summer", "winter")) %>% 
-   pull()
-c(lower = d_hat - 2 * sd(boot), 
-  upper = d_hat + 2 * sd(boot))
-
-## ------------------------------------------------------------------------
-d_hat <- fli_small %>%
-  group_by(season) %>%
-  summarize(prop = mean(day_hour == "morning")) %>%
-  # Since summer - winter
-  summarize(-diff(prop)) %>%
-  pull()
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "diff in props", order = c("summer", "winter")) %>%
-  pull()
-c(lower = d_hat - 2 * sd(boot), 
-  upper = d_hat + 2 * sd(boot))
-
-## ------------------------------------------------------------------------
-slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
-  broom::tidy() %>% 
-  filter(term == "dep_delay") %>% 
-  pull(estimate)
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "slope") %>% 
-   pull()
-c(lower = slope_hat - 2 * sd(boot), 
-  upper = slope_hat + 2 * sd(boot))   
-
diff --git a/inst/doc/flights_examples.Rmd b/inst/doc/flights_examples.Rmd
deleted file mode 100644
index 059d2da2..00000000
--- a/inst/doc/flights_examples.Rmd
+++ /dev/null
@@ -1,358 +0,0 @@
----
-title: "Randomization Examples using `nycflights13` `flights` data"
-author: "Chester Ismay and Andrew Bray"
-date: "2018-01-05"
-output: 
-  rmarkdown::html_vignette:
-    df_print: kable
-vignette: |
-  %\VignetteIndexEntry{flights example}
-  %\VignetteEncoding{UTF-8}
-  %\VignetteEngine{knitr::rmarkdown}
-editor_options: 
-  chunk_output_type: console
----
-
-**Note**: The `type` argument in `generate()` is automatically filled based on the entries for `specify()` and
-`hypothesize()`. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.
-
-```{r include=FALSE}
-knitr::opts_chunk$set(fig.width = 8, fig.height = 5) 
-```
-
-This vignette is designed to show how to use the {infer} package with {dplyr} syntax. It does not show how to calculate observed statistics or p-values using the {infer} package. To see examples of these, check out the "Computation of observed statistics..." vignette instead.
-
-## Data preparation
-
-```{r message=FALSE, warning=FALSE}
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>%
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-```
-
-* Two numeric - `arr_delay`, `dep_delay`
-* Two categories 
-    - `season` (`"winter"`, `"summer"`), 
-    - `day_hour` (`"morning"`, `"not morning"`)
-* Three categories - `origin` (`"EWR"`, `"JFK"`, `"LGA"`)
-* Sixteen categories - `carrier`
-
-***
-
-# Hypothesis tests
-
-### One numerical variable (mean)
-
-```{r}
-x_bar <- fli_small %>%
-  summarize(mean(dep_delay)) %>%
-  pull()
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 10) %>%
-  generate(reps = 1000, type = "bootstrap") %>%
-  calculate(stat = "mean")
-ggplot(data = null_distn, mapping = aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = x_bar, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= x_bar) * 2)
-```
-
-### One numerical variable (median)
-
-```{r}
-x_tilde <- fli_small %>%
-  summarize(median(dep_delay)) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", med = -1) %>% 
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "median")
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = x_tilde, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= x_tilde) * 2)
-```
-
-### One categorical (one proportion)
-
-```{r}
-p_hat <- fli_small %>%
-  summarize(mean(day_hour == "morning")) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000, type = "simulate") %>%
-  calculate(stat = "prop")
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = p_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= p_hat) * 2)
-```
-
-Logical variables will be coerced to factors:
-
-```{r}
-null_distn <- fli_small %>%
-  mutate(day_hour_logical = (day_hour == "morning")) %>%
-  specify(response = day_hour_logical, success = "TRUE") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000, type = "simulate") %>%
-  calculate(stat = "prop")
-```
-
-
-### Two categorical (2 level) variables
-
-```{r}
-d_hat <- fli_small %>%
-  group_by(season) %>%
-  summarize(prop = mean(day_hour == "morning")) %>%
-  summarize(diff(prop)) %>%
-  pull()
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "diff in props", order = c("winter", "summer"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= d_hat) * 2) %>%
-  pull()
-```
-
-### One categorical (>2 level) - GoF
-
-```{r}
-Chisq_hat <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  calculate(stat = "Chisq")
-null_distn <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  generate(reps = 1000, type = "simulate") %>% 
-  calculate(stat = "Chisq")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = pull(Chisq_hat), color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= pull(Chisq_hat))) %>%
-  pull()
-```
-
-### Two categorical (>2 level) variables
-
-```{r}
-Chisq_hat <- fli_small %>%
-  chisq_stat(formula = day_hour ~ origin)
-null_distn <- fli_small %>%
-  specify(day_hour ~ origin, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "Chisq")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = pull(Chisq_hat), color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= pull(Chisq_hat))) %>%
-  pull()
-```
-
-### One numerical variable, one categorical (2 levels) (diff in means)
-
-```{r}
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(mean_stat = mean(dep_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(mean_stat)) %>% 
-  pull()
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("summer", "winter"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat <= d_hat) * 2) %>%
-  pull()
-```
-
-### One numerical variable, one categorical (2 levels) (diff in medians)
-
-```{r}
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(median_stat = median(dep_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(median_stat)) %>% 
-  pull()
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("summer", "winter"))
-ggplot(null_distn, aes(x = stat)) +
-  geom_bar() +
-  geom_vline(xintercept = d_hat, color = "red")
-null_distn %>%
-  summarize(p_value = mean(stat >= d_hat) * 2) %>%
-  pull()
-```
-
-### One numerical, one categorical (>2 levels) -  ANOVA
-
-```{r}
-F_hat <- anova(
-               aov(formula = arr_delay ~ origin, data = fli_small)
-               )$`F value`[1]
-null_distn <- fli_small %>%
-   specify(arr_delay ~ origin) %>% # alt: response = arr_delay, 
-   # explanatory = origin
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "F")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = F_hat, color = "red")  
-null_distn %>% 
-  summarize(p_value = mean(stat >= F_hat)) %>%
-  pull()
-```
-
-### Two numerical vars - SLR 
-
-```{r}
-slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
-  broom::tidy() %>% 
-  filter(term == "dep_delay") %>% 
-  pull(estimate)
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "slope")
-ggplot(null_distn, aes(x = stat)) +
-  geom_density() +
-  geom_vline(xintercept = slope_hat, color = "red")  
-null_distn %>% 
-  summarize(p_value = mean(stat >= slope_hat) * 2) %>%
-  pull()
-```
-
-## Confidence intervals
-
-### One numerical (one mean)
-
-```{r}
-x_bar <- fli_small %>%
-   summarize(mean(arr_delay)) %>%
-   pull()
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "mean") %>%
-   pull()
-c(lower = x_bar - 2 * sd(boot),
-  upper = x_bar + 2 * sd(boot))
-```
-
-### One categorical (one proportion)
-
-```{r}
-p_hat <- fli_small %>%
- summarize(mean(day_hour == "morning")) %>%
- pull()
-boot <- fli_small %>%
- specify(response = day_hour, success = "morning") %>%
- generate(reps = 1000, type = "bootstrap") %>%
- calculate(stat = "prop") %>%
- pull()
-c(lower = p_hat - 2 * sd(boot),
- upper = p_hat + 2 * sd(boot))
-```
-
-### One numerical variable, one categorical (2 levels) (diff in means)
-
-```{r}
-d_hat <- fli_small %>% 
-  group_by(season) %>% 
-  summarize(mean_stat = mean(arr_delay)) %>% 
-  # Since summer - winter
-  summarize(-diff(mean_stat)) %>% 
-  pull()
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "diff in means", order = c("summer", "winter")) %>% 
-   pull()
-c(lower = d_hat - 2 * sd(boot), 
-  upper = d_hat + 2 * sd(boot))
-```
-
-### Two categorical variables (diff in proportions)
-
-```{r}
-d_hat <- fli_small %>%
-  group_by(season) %>%
-  summarize(prop = mean(day_hour == "morning")) %>%
-  # Since summer - winter
-  summarize(-diff(prop)) %>%
-  pull()
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "diff in props", order = c("summer", "winter")) %>%
-  pull()
-c(lower = d_hat - 2 * sd(boot), 
-  upper = d_hat + 2 * sd(boot))
-```
-
-### Two numerical vars - SLR
-
-```{r}
-slope_hat <- lm(arr_delay ~ dep_delay, data = fli_small) %>% 
-  broom::tidy() %>% 
-  filter(term == "dep_delay") %>% 
-  pull(estimate)
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "slope") %>% 
-   pull()
-c(lower = slope_hat - 2 * sd(boot), 
-  upper = slope_hat + 2 * sd(boot))   
-```
diff --git a/inst/doc/flights_examples.html b/inst/doc/flights_examples.html
deleted file mode 100644
index 084b8af7..00000000
--- a/inst/doc/flights_examples.html
+++ /dev/null
@@ -1,461 +0,0 @@
-<!DOCTYPE html>
-
-<html xmlns="http://www.w3.org/1999/xhtml">
-
-<head>
-
-<meta charset="utf-8" />
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="pandoc" />
-
-<meta name="viewport" content="width=device-width, initial-scale=1">
-
-<meta name="author" content="Chester Ismay and Andrew Bray" />
-
-<meta name="date" content="2018-01-05" />
-
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-<h1 class="title toc-ignore">Randomization Examples using <code>nycflights13</code> <code>flights</code> data</h1>
-<h4 class="author"><em>Chester Ismay and Andrew Bray</em></h4>
-<h4 class="date"><em>2018-01-05</em></h4>
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-<p><strong>Note</strong>: The <code>type</code> argument in <code>generate()</code> is automatically filled based on the entries for <code>specify()</code> and <code>hypothesize()</code>. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.</p>
-<p>This vignette is designed to show how to use the {infer} package with {dplyr} syntax. It does not show how to calculate observed statistics or p-values using the {infer} package. To see examples of these, check out the “Computation of observed statistics…” vignette instead.</p>
-<div id="data-preparation" class="section level2">
-<h2>Data preparation</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(nycflights13)
-<span class="kw">library</span>(dplyr)
-<span class="kw">library</span>(ggplot2)
-<span class="kw">library</span>(stringr)
-<span class="kw">library</span>(infer)
-<span class="kw">set.seed</span>(<span class="dv">2017</span>)
-fli_small &lt;-<span class="st"> </span>flights <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">na.omit</span>() <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">sample_n</span>(<span class="dt">size =</span> <span class="dv">500</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">season =</span> <span class="kw">case_when</span>(
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">10</span><span class="op">:</span><span class="dv">12</span>, <span class="dv">1</span><span class="op">:</span><span class="dv">3</span>) <span class="op">~</span><span class="st"> &quot;winter&quot;</span>,
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">4</span><span class="op">:</span><span class="dv">9</span>) <span class="op">~</span><span class="st"> &quot;summer&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour =</span> <span class="kw">case_when</span>(
-    <span class="kw">between</span>(hour, <span class="dv">1</span>, <span class="dv">12</span>) <span class="op">~</span><span class="st"> &quot;morning&quot;</span>,
-    <span class="kw">between</span>(hour, <span class="dv">13</span>, <span class="dv">24</span>) <span class="op">~</span><span class="st"> &quot;not morning&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)</code></pre></div>
-<ul>
-<li>Two numeric - <code>arr_delay</code>, <code>dep_delay</code></li>
-<li>Two categories
-<ul>
-<li><code>season</code> (<code>&quot;winter&quot;</code>, <code>&quot;summer&quot;</code>),</li>
-<li><code>day_hour</code> (<code>&quot;morning&quot;</code>, <code>&quot;not morning&quot;</code>)</li>
-</ul></li>
-<li>Three categories - <code>origin</code> (<code>&quot;EWR&quot;</code>, <code>&quot;JFK&quot;</code>, <code>&quot;LGA&quot;</code>)</li>
-<li>Sixteen categories - <code>carrier</code></li>
-</ul>
-<hr />
-</div>
-<div id="hypothesis-tests" class="section level1">
-<h1>Hypothesis tests</h1>
-<div id="one-numerical-variable-mean" class="section level3">
-<h3>One numerical variable (mean)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">x_bar &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="kw">mean</span>(dep_delay)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">10</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
-<span class="kw">ggplot</span>(<span class="dt">data =</span> null_distn, <span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> x_bar, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>x_bar) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.794</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-variable-median" class="section level3">
-<h3>One numerical variable (median)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">x_tilde &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="kw">median</span>(dep_delay)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">med =</span> <span class="op">-</span><span class="dv">1</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> x_tilde, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>x_tilde) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.178</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-categorical-one-proportion" class="section level3">
-<h3>One categorical (one proportion)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="kw">mean</span>(day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> p_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>p_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.132</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>Logical variables will be coerced to factors:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour_logical =</span> (day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour_logical, <span class="dt">success =</span> <span class="st">&quot;TRUE&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
-</div>
-<div id="two-categorical-2-level-variables" class="section level3">
-<h3>Two categorical (2 level) variables</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">group_by</span>(season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">prop =</span> <span class="kw">mean</span>(day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="kw">diff</span>(prop)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.758</code></pre>
-</div>
-<div id="one-categorical-2-level---gof" class="section level3">
-<h3>One categorical (&gt;2 level) - GoF</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Chisq_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, 
-              <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;EWR&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;JFK&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;LGA&quot;</span> =<span class="st"> </span>.<span class="dv">34</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, 
-              <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;EWR&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;JFK&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;LGA&quot;</span> =<span class="st"> </span>.<span class="dv">34</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="kw">pull</span>(Chisq_hat), <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span><span class="kw">pull</span>(Chisq_hat))) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.002</code></pre>
-</div>
-<div id="two-categorical-2-level-variables-1" class="section level3">
-<h3>Two categorical (&gt;2 level) variables</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Chisq_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">chisq_stat</span>(<span class="dt">formula =</span> day_hour <span class="op">~</span><span class="st"> </span>origin)
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>origin, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="kw">pull</span>(Chisq_hat), <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span><span class="kw">pull</span>(Chisq_hat))) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.017</code></pre>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-means" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">group_by</span>(season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">mean_stat =</span> <span class="kw">mean</span>(dep_delay)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="co"># Since summer - winter</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="op">-</span><span class="kw">diff</span>(mean_stat)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>()
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = dep_delay, </span>
-<span class="st">  </span><span class="co"># explanatory = season</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&lt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 1.574</code></pre>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-medians" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">group_by</span>(season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">median_stat =</span> <span class="kw">median</span>(dep_delay)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="co"># Since summer - winter</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="op">-</span><span class="kw">diff</span>(median_stat)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>()
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = dep_delay, </span>
-<span class="st">  </span><span class="co"># explanatory = season</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_bar</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> d_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>d_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.068</code></pre>
-</div>
-<div id="one-numerical-one-categorical-2-levels---anova" class="section level3">
-<h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">F_hat &lt;-<span class="st"> </span><span class="kw">anova</span>(
-               <span class="kw">aov</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>origin, <span class="dt">data =</span> fli_small)
-               )<span class="op">$</span><span class="st">`</span><span class="dt">F value</span><span class="st">`</span>[<span class="dv">1</span>]
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>origin) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = arr_delay, </span>
-<span class="st">   </span><span class="co"># explanatory = origin</span>
-<span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> F_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)  </code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>F_hat)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0.351</code></pre>
-</div>
-<div id="two-numerical-vars---slr" class="section level3">
-<h3>Two numerical vars - SLR</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">slope_hat &lt;-<span class="st"> </span><span class="kw">lm</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay, <span class="dt">data =</span> fli_small) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>broom<span class="op">::</span><span class="kw">tidy</span>() <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">filter</span>(term <span class="op">==</span><span class="st"> &quot;dep_delay&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>(estimate)
-null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
-<span class="kw">ggplot</span>(null_distn, <span class="kw">aes</span>(<span class="dt">x =</span> stat)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_density</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> slope_hat, <span class="dt">color =</span> <span class="st">&quot;red&quot;</span>)  </code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">p_value =</span> <span class="kw">mean</span>(stat <span class="op">&gt;=</span><span class="st"> </span>slope_hat) <span class="op">*</span><span class="st"> </span><span class="dv">2</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()</code></pre></div>
-<pre><code>## [1] 0</code></pre>
-</div>
-<div id="confidence-intervals" class="section level2">
-<h2>Confidence intervals</h2>
-<div id="one-numerical-one-mean" class="section level3">
-<h3>One numerical (one mean)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">x_bar &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">summarize</span>(<span class="kw">mean</span>(arr_delay)) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">pull</span>()
-boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(<span class="dt">response =</span> arr_delay) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">pull</span>()
-<span class="kw">c</span>(<span class="dt">lower =</span> x_bar <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot),
-  <span class="dt">upper =</span> x_bar <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>## lower upper 
-## 1.122 8.022</code></pre>
-</div>
-<div id="one-categorical-one-proportion-1" class="section level3">
-<h3>One categorical (one proportion)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">summarize</span>(<span class="kw">mean</span>(day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">pull</span>()
-boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">pull</span>()
-<span class="kw">c</span>(<span class="dt">lower =</span> p_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot),
- <span class="dt">upper =</span> p_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##  lower  upper 
-## 0.4195 0.5125</code></pre>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-means-1" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">group_by</span>(season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">mean_stat =</span> <span class="kw">mean</span>(arr_delay)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="co"># Since summer - winter</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="op">-</span><span class="kw">diff</span>(mean_stat)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>()
-boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">pull</span>()
-<span class="kw">c</span>(<span class="dt">lower =</span> d_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
-  <span class="dt">upper =</span> d_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##  lower  upper 
-## -7.704  6.214</code></pre>
-</div>
-<div id="two-categorical-variables-diff-in-proportions" class="section level3">
-<h3>Two categorical variables (diff in proportions)</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">group_by</span>(season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="dt">prop =</span> <span class="kw">mean</span>(day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># Since summer - winter</span>
-<span class="st">  </span><span class="kw">summarize</span>(<span class="op">-</span><span class="kw">diff</span>(prop)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()
-boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">pull</span>()
-<span class="kw">c</span>(<span class="dt">lower =</span> d_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
-  <span class="dt">upper =</span> d_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))</code></pre></div>
-<pre><code>##    lower    upper 
-## -0.07149  0.11259</code></pre>
-</div>
-<div id="two-numerical-vars---slr-1" class="section level3">
-<h3>Two numerical vars - SLR</h3>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">slope_hat &lt;-<span class="st"> </span><span class="kw">lm</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay, <span class="dt">data =</span> fli_small) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>broom<span class="op">::</span><span class="kw">tidy</span>() <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">filter</span>(term <span class="op">==</span><span class="st"> &quot;dep_delay&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">pull</span>(estimate)
-boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">pull</span>()
-<span class="kw">c</span>(<span class="dt">lower =</span> slope_hat <span class="op">-</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot), 
-  <span class="dt">upper =</span> slope_hat <span class="op">+</span><span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span><span class="kw">sd</span>(boot))   </code></pre></div>
-<pre><code>##  lower  upper 
-## 0.9658 1.0681</code></pre>
-</div>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/inst/doc/mtcars_examples.R b/inst/doc/mtcars_examples.R
deleted file mode 100644
index 8ac3809b..00000000
--- a/inst/doc/mtcars_examples.R
+++ /dev/null
@@ -1,140 +0,0 @@
-## ----include=FALSE-------------------------------------------------------
-knitr::opts_chunk$set(fig.width = 8, fig.height = 5) 
-
-## ----message=FALSE, warning=FALSE----------------------------------------
-library(infer)
-library(dplyr)
-mtcars <- mtcars %>%
-  mutate(cyl = factor(cyl),
-         vs = factor(vs),
-         am = factor(am),
-         gear = factor(gear),
-         carb = factor(carb))
-# For reproducibility         
-set.seed(2018)         
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = mpg) %>% # formula alt: mpg ~ NULL
-  hypothesize(null = "point", mu = 25) %>% 
-  generate(reps = 100, type = "bootstrap") %>% 
-  calculate(stat = "mean")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = mpg) %>% # formula alt: mpg ~ NULL
-  hypothesize(null = "point", med = 26) %>% 
-  generate(reps = 100, type = "bootstrap") %>% 
-  calculate(stat = "median")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-  hypothesize(null = "point", p = .25) %>% 
-  generate(reps = 100, type = "simulate") %>% 
-  calculate(stat = "prop")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in props", order = c("0", "1"))
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(cyl ~ NULL) %>% # alt: response = cyl
-  hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-  generate(reps = 100, type = "simulate") %>%
-  calculate(stat = "Chisq")
-
-## ----warning = FALSE-----------------------------------------------------
-mtcars %>%
-  specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "Chisq")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("0", "1"))
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("0", "1"))
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "F")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "slope")
-
-## ----eval=FALSE----------------------------------------------------------
-#  mtcars %>%
-#    specify(response = mpg) %>% # formula alt: mpg ~ NULL
-#    hypothesize(null = "point", sigma = 5) %>%
-#    generate(reps = 100, type = "bootstrap") %>%
-#    calculate(stat = "sd")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "mean")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "median")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "sd")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(response = am, success = "1") %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "prop")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ am) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "diff in means", order = c("0", "1"))
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(am ~ vs, success = "1") %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "diff in props", order = c("0", "1"))
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ hp) %>% 
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "slope")
-
-## ------------------------------------------------------------------------
-mtcars %>%
-  specify(mpg ~ hp) %>% 
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "correlation")
-
diff --git a/inst/doc/mtcars_examples.Rmd b/inst/doc/mtcars_examples.Rmd
deleted file mode 100644
index 91bc218c..00000000
--- a/inst/doc/mtcars_examples.Rmd
+++ /dev/null
@@ -1,223 +0,0 @@
----
-title: "Examples using `mtcars` data"
-author: "Chester Ismay and Andrew Bray"
-date: "2018-01-05"
-output: 
-  rmarkdown::html_vignette
-vignette: |
-  %\VignetteIndexEntry{mtcars example}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r include=FALSE}
-knitr::opts_chunk$set(fig.width = 8, fig.height = 5) 
-```
-
-**Note**: The `type` argument in `generate()` is automatically filled based on the entries for `specify()` and
-`hypothesize()`. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.
-
-## Data preparation
-
-```{r message=FALSE, warning=FALSE}
-library(infer)
-library(dplyr)
-mtcars <- mtcars %>%
-  mutate(cyl = factor(cyl),
-         vs = factor(vs),
-         am = factor(am),
-         gear = factor(gear),
-         carb = factor(carb))
-# For reproducibility         
-set.seed(2018)         
-```
-
-***
-
-One numerical variable (mean)
-
-```{r}
-mtcars %>%
-  specify(response = mpg) %>% # formula alt: mpg ~ NULL
-  hypothesize(null = "point", mu = 25) %>% 
-  generate(reps = 100, type = "bootstrap") %>% 
-  calculate(stat = "mean")
-```
-
-One numerical variable (median)
-
-```{r}
-mtcars %>%
-  specify(response = mpg) %>% # formula alt: mpg ~ NULL
-  hypothesize(null = "point", med = 26) %>% 
-  generate(reps = 100, type = "bootstrap") %>% 
-  calculate(stat = "median")
-```
-
-One categorical (2 level) variable
-
-```{r}
-mtcars %>%
-  specify(response = am, success = "1") %>% # formula alt: am ~ NULL
-  hypothesize(null = "point", p = .25) %>% 
-  generate(reps = 100, type = "simulate") %>% 
-  calculate(stat = "prop")
-```
-
-Two categorical (2 level) variables
-
-```{r}
-mtcars %>%
-  specify(am ~ vs, success = "1") %>% # alt: response = am, explanatory = vs
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in props", order = c("0", "1"))
-```
-
-One categorical (>2 level) - GoF
-
-```{r}
-mtcars %>%
-  specify(cyl ~ NULL) %>% # alt: response = cyl
-  hypothesize(null = "point", p = c("4" = .5, "6" = .25, "8" = .25)) %>%
-  generate(reps = 100, type = "simulate") %>%
-  calculate(stat = "Chisq")
-```
-
-Two categorical (>2 level) variables
-
-```{r warning = FALSE}
-mtcars %>%
-  specify(cyl ~ am) %>% # alt: response = cyl, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "Chisq")
-```
-
-One numerical variable one categorical (2 levels) (diff in means)
-
-```{r}
-mtcars %>%
-  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("0", "1"))
-```
-
-One numerical variable one categorical (2 levels) (diff in medians)
-
-```{r}
-mtcars %>%
-  specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("0", "1"))
-```
-
-One numerical one categorical (>2 levels) -  ANOVA
-
-```{r}
-mtcars %>%
-  specify(mpg ~ cyl) %>% # alt: response = mpg, explanatory = cyl
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "F")
-```
-
-Two numerical vars - SLR 
-
-```{r}
-mtcars %>%
-  specify(mpg ~ hp) %>% # alt: response = mpg, explanatory = cyl
-  hypothesize(null = "independence") %>%
-  generate(reps = 100, type = "permute") %>%
-  calculate(stat = "slope")
-```
-
-One numerical variable (standard deviation)
-
-**Not currently implemented**
-
-```{r eval=FALSE}
-mtcars %>%
-  specify(response = mpg) %>% # formula alt: mpg ~ NULL
-  hypothesize(null = "point", sigma = 5) %>% 
-  generate(reps = 100, type = "bootstrap") %>% 
-  calculate(stat = "sd")
-```
-
-
-### Confidence intervals
-
-One numerical (one mean)
-
-```{r}
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "mean")
-```
-
-One numerical (one median)
-
-```{r}
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "median")
-```
-
-One numerical (standard deviation)
-
-```{r}
-mtcars %>%
-  specify(response = mpg) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "sd")
-```
-
-One categorical (one proportion)
-
-```{r}
-mtcars %>%
-  specify(response = am, success = "1") %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "prop")
-```
-
-One numerical variable one categorical (2 levels) (diff in means)
-
-```{r}
-mtcars %>%
-  specify(mpg ~ am) %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "diff in means", order = c("0", "1"))
-```
-
-Two categorical variables (diff in proportions)
-
-```{r}
-mtcars %>%
-  specify(am ~ vs, success = "1") %>%
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "diff in props", order = c("0", "1"))
-```
-
-Two numerical vars - SLR
-
-```{r}
-mtcars %>%
-  specify(mpg ~ hp) %>% 
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "slope")
-```
-
-Two numerical vars - correlation
-
-```{r}
-mtcars %>%
-  specify(mpg ~ hp) %>% 
-  generate(reps = 100, type = "bootstrap") %>%
-  calculate(stat = "correlation")
-```
-
diff --git a/inst/doc/mtcars_examples.html b/inst/doc/mtcars_examples.html
deleted file mode 100644
index 084626ae..00000000
--- a/inst/doc/mtcars_examples.html
+++ /dev/null
@@ -1,468 +0,0 @@
-<!DOCTYPE html>
-
-<html xmlns="http://www.w3.org/1999/xhtml">
-
-<head>
-
-<meta charset="utf-8" />
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="pandoc" />
-
-<meta name="viewport" content="width=device-width, initial-scale=1">
-
-<meta name="author" content="Chester Ismay and Andrew Bray" />
-
-<meta name="date" content="2018-01-05" />
-
-<title>Examples using mtcars data</title>
-
-
-
-<style type="text/css">code{white-space: pre;}</style>
-<style type="text/css">
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-<h1 class="title toc-ignore">Examples using <code>mtcars</code> data</h1>
-<h4 class="author"><em>Chester Ismay and Andrew Bray</em></h4>
-<h4 class="date"><em>2018-01-05</em></h4>
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-<p><strong>Note</strong>: The <code>type</code> argument in <code>generate()</code> is automatically filled based on the entries for <code>specify()</code> and <code>hypothesize()</code>. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.</p>
-<div id="data-preparation" class="section level2">
-<h2>Data preparation</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(infer)
-<span class="kw">library</span>(dplyr)
-mtcars &lt;-<span class="st"> </span>mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">cyl =</span> <span class="kw">factor</span>(cyl),
-         <span class="dt">vs =</span> <span class="kw">factor</span>(vs),
-         <span class="dt">am =</span> <span class="kw">factor</span>(am),
-         <span class="dt">gear =</span> <span class="kw">factor</span>(gear),
-         <span class="dt">carb =</span> <span class="kw">factor</span>(carb))
-<span class="co"># For reproducibility         </span>
-<span class="kw">set.seed</span>(<span class="dv">2018</span>)         </code></pre></div>
-<hr />
-<p>One numerical variable (mean)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># formula alt: mpg ~ NULL</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">25</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  26.6
-##  2         2  25.1
-##  3         3  25.2
-##  4         4  24.7
-##  5         5  24.6
-##  6         6  25.8
-##  7         7  24.7
-##  8         8  25.6
-##  9         9  25.0
-## 10        10  25.1
-## # ... with 90 more rows</code></pre>
-<p>One numerical variable (median)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># formula alt: mpg ~ NULL</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">med =</span> <span class="dv">26</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  28.2
-##  2         2  27.2
-##  3         3  26.2
-##  4         4  26  
-##  5         5  26.5
-##  6         6  24.5
-##  7         7  26  
-##  8         8  28.2
-##  9         9  28.2
-## 10        10  23.2
-## # ... with 90 more rows</code></pre>
-<p>One categorical (2 level) variable</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> am, <span class="dt">success =</span> <span class="st">&quot;1&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># formula alt: am ~ NULL</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">25</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate   stat
-##    &lt;fct&gt;      &lt;dbl&gt;
-##  1 1         0.375 
-##  2 2         0.0625
-##  3 3         0.125 
-##  4 4         0.25  
-##  5 5         0.188 
-##  6 6         0.406 
-##  7 7         0.219 
-##  8 8         0.375 
-##  9 9         0.344 
-## 10 10        0.188 
-## # ... with 90 more rows</code></pre>
-<p>Two categorical (2 level) variables</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(am <span class="op">~</span><span class="st"> </span>vs, <span class="dt">success =</span> <span class="st">&quot;1&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = am, explanatory = vs</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>))</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate    stat
-##        &lt;int&gt;   &lt;dbl&gt;
-##  1         1 -0.421 
-##  2         2 -0.167 
-##  3         3 -0.421 
-##  4         4 -0.0397
-##  5         5  0.0873
-##  6         6 -0.0397
-##  7         7 -0.0397
-##  8         8 -0.0397
-##  9         9  0.0873
-## 10        10 -0.167 
-## # ... with 90 more rows</code></pre>
-<p>One categorical (&gt;2 level) - GoF</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(cyl <span class="op">~</span><span class="st"> </span><span class="ot">NULL</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = cyl</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;4&quot;</span> =<span class="st"> </span>.<span class="dv">5</span>, <span class="st">&quot;6&quot;</span> =<span class="st"> </span>.<span class="dv">25</span>, <span class="st">&quot;8&quot;</span> =<span class="st"> </span>.<span class="dv">25</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##    &lt;fct&gt;     &lt;dbl&gt;
-##  1 1         6.75 
-##  2 2         1.69 
-##  3 3         3.19 
-##  4 4         1.69 
-##  5 5         6    
-##  6 6         2.69 
-##  7 7         4.75 
-##  8 8         0.75 
-##  9 9         0.688
-## 10 10        3.69 
-## # ... with 90 more rows</code></pre>
-<p>Two categorical (&gt;2 level) variables</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(cyl <span class="op">~</span><span class="st"> </span>am) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = cyl, explanatory = am</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1 1.34 
-##  2         2 1.63 
-##  3         3 1.63 
-##  4         4 2.63 
-##  5         5 3.90 
-##  6         6 1.74 
-##  7         7 0.126
-##  8         8 1.74 
-##  9         9 1.34 
-## 10        10 1.34 
-## # ... with 90 more rows</code></pre>
-<p>One numerical variable one categorical (2 levels) (diff in means)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>am) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = mpg, explanatory = am</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>))</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate   stat
-##        &lt;int&gt;  &lt;dbl&gt;
-##  1         1 -1.10 
-##  2         2  0.217
-##  3         3 -1.08 
-##  4         4 -3.80 
-##  5         5  3.08 
-##  6         6  0.489
-##  7         7  2.34 
-##  8         8  4.10 
-##  9         9 -1.86 
-## 10        10 -0.210
-## # ... with 90 more rows</code></pre>
-<p>One numerical variable one categorical (2 levels) (diff in medians)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>am) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = mpg, explanatory = am</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>))</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  0.5 
-##  2         2 -1.10
-##  3         3  5.20
-##  4         4  1.8 
-##  5         5  0.5 
-##  6         6  3.3 
-##  7         7 -1.60
-##  8         8 -2.3 
-##  9         9  2.90
-## 10        10 -0.5 
-## # ... with 90 more rows</code></pre>
-<p>One numerical one categorical (&gt;2 levels) - ANOVA</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>cyl) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = mpg, explanatory = cyl</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1 1.43 
-##  2         2 1.65 
-##  3         3 0.318
-##  4         4 0.393
-##  5         5 1.05 
-##  6         6 0.826
-##  7         7 1.32 
-##  8         8 0.833
-##  9         9 0.144
-## 10        10 0.365
-## # ... with 90 more rows</code></pre>
-<p>Two numerical vars - SLR</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>hp) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = mpg, explanatory = cyl</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate     stat
-##        &lt;int&gt;    &lt;dbl&gt;
-##  1         1 -0.0151 
-##  2         2  0.00224
-##  3         3 -0.0120 
-##  4         4  0.00292
-##  5         5  0.0203 
-##  6         6 -0.00730
-##  7         7 -0.0246 
-##  8         8  0.00555
-##  9         9  0.0109 
-## 10        10  0.0176 
-## # ... with 90 more rows</code></pre>
-<p>One numerical variable (standard deviation)</p>
-<p><strong>Not currently implemented</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># formula alt: mpg ~ NULL</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">sigma =</span> <span class="dv">5</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;sd&quot;</span>)</code></pre></div>
-<div id="confidence-intervals" class="section level3">
-<h3>Confidence intervals</h3>
-<p>One numerical (one mean)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  19.6
-##  2         2  21.8
-##  3         3  18.7
-##  4         4  19.2
-##  5         5  21.6
-##  6         6  19.9
-##  7         7  20.7
-##  8         8  19.3
-##  9         9  21.2
-## 10        10  21.3
-## # ... with 90 more rows</code></pre>
-<p>One numerical (one median)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  19.2
-##  2         2  20.1
-##  3         3  21  
-##  4         4  17.8
-##  5         5  20.1
-##  6         6  19.2
-##  7         7  18.4
-##  8         8  19.2
-##  9         9  19.2
-## 10        10  18.0
-## # ... with 90 more rows</code></pre>
-<p>One numerical (standard deviation)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> mpg) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;sd&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1  5.28
-##  2         2  6.74
-##  3         3  5.29
-##  4         4  5.41
-##  5         5  5.56
-##  6         6  5.65
-##  7         7  6.17
-##  8         8  6.40
-##  9         9  6.31
-## 10        10  6.11
-## # ... with 90 more rows</code></pre>
-<p>One categorical (one proportion)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> am, <span class="dt">success =</span> <span class="st">&quot;1&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate  stat
-##        &lt;int&gt; &lt;dbl&gt;
-##  1         1 0.375
-##  2         2 0.406
-##  3         3 0.406
-##  4         4 0.312
-##  5         5 0.312
-##  6         6 0.469
-##  7         7 0.438
-##  8         8 0.281
-##  9         9 0.438
-## 10        10 0.5  
-## # ... with 90 more rows</code></pre>
-<p>One numerical variable one categorical (2 levels) (diff in means)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>am) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>))</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate   stat
-##        &lt;int&gt;  &lt;dbl&gt;
-##  1         1  -9.38
-##  2         2  -5.11
-##  3         3  -4.88
-##  4         4  -5.39
-##  5         5  -9.19
-##  6         6  -7.20
-##  7         7  -5.34
-##  8         8  -3.20
-##  9         9  -5.95
-## 10        10 -11.0 
-## # ... with 90 more rows</code></pre>
-<p>Two categorical variables (diff in proportions)</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(am <span class="op">~</span><span class="st"> </span>vs, <span class="dt">success =</span> <span class="st">&quot;1&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;0&quot;</span>, <span class="st">&quot;1&quot;</span>))</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate   stat
-##        &lt;int&gt;  &lt;dbl&gt;
-##  1         1 -0.352
-##  2         2 -0.15 
-##  3         3 -0.294
-##  4         4 -0.254
-##  5         5 -0.438
-##  6         6 -0.126
-##  7         7 -0.188
-##  8         8  0.167
-##  9         9 -0.143
-## 10        10 -0.5  
-## # ... with 90 more rows</code></pre>
-<p>Two numerical vars - SLR</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>hp) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate    stat
-##        &lt;int&gt;   &lt;dbl&gt;
-##  1         1 -0.0850
-##  2         2 -0.0512
-##  3         3 -0.0736
-##  4         4 -0.0569
-##  5         5 -0.0930
-##  6         6 -0.0659
-##  7         7 -0.0710
-##  8         8 -0.0767
-##  9         9 -0.0556
-## 10        10 -0.0627
-## # ... with 90 more rows</code></pre>
-<p>Two numerical vars - correlation</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mtcars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(mpg <span class="op">~</span><span class="st"> </span>hp) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">100</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)</code></pre></div>
-<pre><code>## # A tibble: 100 x 2
-##    replicate   stat
-##        &lt;int&gt;  &lt;dbl&gt;
-##  1         1 -0.821
-##  2         2 -0.812
-##  3         3 -0.802
-##  4         4 -0.723
-##  5         5 -0.885
-##  6         6 -0.777
-##  7         7 -0.752
-##  8         8 -0.758
-##  9         9 -0.826
-## 10        10 -0.779
-## # ... with 90 more rows</code></pre>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/inst/doc/observed_stat_examples.R b/inst/doc/observed_stat_examples.R
deleted file mode 100644
index e4556a1b..00000000
--- a/inst/doc/observed_stat_examples.R
+++ /dev/null
@@ -1,485 +0,0 @@
-## ----include=FALSE-------------------------------------------------------
-knitr::opts_chunk$set(fig.width = 6, fig.height = 3.5) 
-options(digits = 4)
-
-## ----message=FALSE, warning=FALSE----------------------------------------
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>%
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-
-## ------------------------------------------------------------------------
-( x_bar <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "mean") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 10) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "mean")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = x_bar, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = x_bar, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( t_bar <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "t") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 8) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "t")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = t_bar, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = t_bar, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( x_tilde <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "median") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", med = -1) %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "median")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = x_tilde, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = x_tilde, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( p_hat <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  calculate(stat = "prop") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "prop")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = p_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = p_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  mutate(day_hour_logical = (day_hour == "morning")) %>%
-  specify(response = day_hour_logical, success = "TRUE") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "prop")
-
-## ------------------------------------------------------------------------
-( d_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "diff in props", order = c("winter", "summer")) )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "diff in props", order = c("winter", "summer"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( z_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "z", order = c("winter", "summer")) )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "z", order = c("winter", "summer"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = z_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = z_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( Chisq_hat <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  calculate(stat = "Chisq") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  generate(reps = 1000, type = "simulate") %>% 
-  calculate(stat = "Chisq")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = Chisq_hat, direction = "greater")
-
-## ------------------------------------------------------------------------
-( Chisq_hat <- fli_small %>%
-  specify(formula = day_hour ~ origin) %>% 
-  calculate(stat = "Chisq") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(day_hour ~ origin) %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "Chisq")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = Chisq_hat, direction = "greater")
-
-## ------------------------------------------------------------------------
-( d_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "diff in means", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( t_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "t", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = t_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = t_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( d_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "diff in medians", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( F_hat <- fli_small %>% 
-  specify(arr_delay ~ origin) %>%
-  calculate(stat = "F") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-   specify(arr_delay ~ origin) %>%
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "F")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = F_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = F_hat, direction = "greater")
-
-## ------------------------------------------------------------------------
-( slope_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>% 
-  calculate(stat = "slope") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "slope")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = slope_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = slope_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( correlation_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>% 
-  calculate(stat = "correlation") )
-
-## ------------------------------------------------------------------------
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "correlation")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = correlation_hat, direction = "two_sided")
-
-## ----echo=FALSE, eval=FALSE----------------------------------------------
-#  # **Standardized observed stat**
-#  ( t_hat <- fli_small %>%
-#    specify(arr_delay ~ dep_delay) %>%
-#    calculate(stat = "t") )
-
-## ----echo=FALSE, eval=FALSE----------------------------------------------
-#  null_distn <- fli_small %>%
-#     specify(arr_delay ~ dep_delay) %>%
-#     hypothesize(null = "independence") %>%
-#     generate(reps = 1000, type = "permute") %>%
-#     calculate(stat = "t")
-#  
-#  visualize(null_distn) +
-#    shade_p_value(obs_stat = t_hat, direction = "two_sided")
-#  null_distn %>%
-#    get_p_value(obs_stat = t_hat, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-( x_bar <- fli_small %>% 
-  specify(response = arr_delay) %>%
-  calculate(stat = "mean") )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "mean")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( t_hat <- fli_small %>% 
-  specify(response = arr_delay) %>%
-  calculate(stat = "t") )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "t")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( p_hat <- fli_small %>% 
-   specify(response = day_hour, success = "morning") %>%
-   calculate(stat = "prop") )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
- specify(response = day_hour, success = "morning") %>%
- generate(reps = 1000, type = "bootstrap") %>%
- calculate(stat = "prop")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( d_hat <- fli_small %>%
-  specify(arr_delay ~ season) %>%
-  calculate(stat = "diff in means", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "diff in means", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( t_hat <- fli_small %>%
-  specify(arr_delay ~ season) %>%
-  calculate(stat = "t", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "t", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( d_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "diff in props", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "diff in props", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( z_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "z", order = c("summer", "winter")) )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "z", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( slope_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>%
-  calculate(stat = "slope") )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "slope")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ------------------------------------------------------------------------
-( correlation_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>%
-  calculate(stat = "correlation") )
-
-## ------------------------------------------------------------------------
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "correlation")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", 
-                            point_estimate = correlation_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-
-## ----eval=FALSE, echo=FALSE----------------------------------------------
-#  # **Point estimate**
-#  ( t_hat <- fli_small %>%
-#    specify(arr_delay ~ dep_delay) %>%
-#    calculate(stat = "t") )
-
-## ----eval=FALSE, echo=FALSE----------------------------------------------
-#  boot <- fli_small %>%
-#     specify(arr_delay ~ dep_delay) %>%
-#     generate(reps = 1000, type = "bootstrap") %>%
-#     calculate(stat = "t")
-#  ( percentile_ci <- get_ci(boot) )
-#  
-#  visualize(boot) +
-#    shade_confidence_interval(endpoints = percentile_ci)
-#  ( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-#  
-#  visualize(boot) +
-#    shade_confidence_interval(endpoints = standard_error_ci)
-
diff --git a/inst/doc/observed_stat_examples.Rmd b/inst/doc/observed_stat_examples.Rmd
deleted file mode 100644
index 780512b3..00000000
--- a/inst/doc/observed_stat_examples.Rmd
+++ /dev/null
@@ -1,692 +0,0 @@
----
-title: "Full infer pipeline examples using `nycflights13` `flights` data"
-author: "Chester Ismay"
-date: "Updated on 2018-06-14"
-output: 
-  rmarkdown::html_vignette:
-    df_print: kable
-vignette: |
-  %\VignetteIndexEntry{Full infer pipeline examples}
-  %\VignetteEncoding{UTF-8}
-  %\VignetteEngine{knitr::rmarkdown}
-editor_options: 
-  chunk_output_type: console
----
-
-
-```{r include=FALSE}
-knitr::opts_chunk$set(fig.width = 6, fig.height = 3.5) 
-options(digits = 4)
-```
-
-## Data preparation
-
-```{r message=FALSE, warning=FALSE}
-library(nycflights13)
-library(dplyr)
-library(ggplot2)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  na.omit() %>%
-  sample_n(size = 500) %>% 
-  mutate(season = case_when(
-    month %in% c(10:12, 1:3) ~ "winter",
-    month %in% c(4:9) ~ "summer"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)
-```
-
-* Two numeric - `arr_delay`, `dep_delay`
-* Two categories 
-    - `season` (`"winter"`, `"summer"`), 
-    - `day_hour` (`"morning"`, `"not morning"`)
-* Three categories - `origin` (`"EWR"`, `"JFK"`, `"LGA"`)
-* Sixteen categories - `carrier`
-
-***
-
-# Hypothesis tests
-
-### One numerical variable (mean)
-
-**Observed stat**
-```{r}
-( x_bar <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "mean") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 10) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "mean")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = x_bar, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = x_bar, direction = "two_sided")
-```
-
-### One numerical variable (standardized mean $t$)
-
-**Observed stat**
-```{r}
-( t_bar <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "t") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", mu = 8) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "t")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = t_bar, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = t_bar, direction = "two_sided")
-```
-
-
-### One numerical variable (median)
-
-**Observed stat**
-
-```{r}
-( x_tilde <- fli_small %>%
-  specify(response = dep_delay) %>%
-  calculate(stat = "median") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = dep_delay) %>%
-  hypothesize(null = "point", med = -1) %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "median")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = x_tilde, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = x_tilde, direction = "two_sided")
-```
-
-### One categorical (one proportion)
-
-**Observed stat**
-
-```{r}
-( p_hat <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  calculate(stat = "prop") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = day_hour, success = "morning") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "prop")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = p_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = p_hat, direction = "two_sided")
-```
-
-Logical variables will be coerced to factors:
-
-```{r}
-null_distn <- fli_small %>%
-  mutate(day_hour_logical = (day_hour == "morning")) %>%
-  specify(response = day_hour_logical, success = "TRUE") %>%
-  hypothesize(null = "point", p = .5) %>%
-  generate(reps = 1000) %>%
-  calculate(stat = "prop")
-```
-
-### One categorical variable (standardized proportion $z$)
-
-Not yet implemented.
-
-### Two categorical (2 level) variables
-
-**Observed stat**
-
-```{r}
-( d_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "diff in props", order = c("winter", "summer")) )
-```
-
-
-```{r}
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "diff in props", order = c("winter", "summer"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-```
-
-### Two categorical (2 level) variables (z)
-
-**Standardized observed stat**
-
-```{r}
-( z_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "z", order = c("winter", "summer")) )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000) %>% 
-  calculate(stat = "z", order = c("winter", "summer"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = z_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = z_hat, direction = "two_sided")
-```
-
-Note the similarities in this plot and the previous one.
-
-### One categorical (>2 level) - GoF
-
-**Observed stat**
-
-Note the need to add in the hypothesized values here to compute the observed statistic.
-
-```{r}
-( Chisq_hat <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  calculate(stat = "Chisq") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(response = origin) %>%
-  hypothesize(null = "point", 
-              p = c("EWR" = .33, "JFK" = .33, "LGA" = .34)) %>% 
-  generate(reps = 1000, type = "simulate") %>% 
-  calculate(stat = "Chisq")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = Chisq_hat, direction = "greater")
-```
-
-### Two categorical (>2 level) variables
-
-**Observed stat**
-
-```{r}
-( Chisq_hat <- fli_small %>%
-  specify(formula = day_hour ~ origin) %>% 
-  calculate(stat = "Chisq") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(day_hour ~ origin) %>%
-  hypothesize(null = "independence") %>% 
-  generate(reps = 1000, type = "permute") %>% 
-  calculate(stat = "Chisq")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = Chisq_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = Chisq_hat, direction = "greater")
-```
-
-### One numerical variable, one categorical (2 levels) (diff in means)
-
-**Observed stat**
-
-```{r}
-( d_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "diff in means", order = c("summer", "winter")) )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in means", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-```
-
-### One numerical variable, one categorical (2 levels) (t)
-
-**Standardized observed stat**
-
-```{r}
-( t_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "t", order = c("summer", "winter")) )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = t_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = t_hat, direction = "two_sided")
-```
-
-Note the similarities in this plot and the previous one.
-
-### One numerical variable, one categorical (2 levels) (diff in medians)
-
-**Observed stat**
-
-```{r}
-( d_hat <- fli_small %>% 
-  specify(dep_delay ~ season) %>% 
-  calculate(stat = "diff in medians", order = c("summer", "winter")) )
-```
-
-```{r}
-null_distn <- fli_small %>%
-  specify(dep_delay ~ season) %>% # alt: response = dep_delay, 
-  # explanatory = season
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "diff in medians", order = c("summer", "winter"))
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = d_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = d_hat, direction = "two_sided")
-```
-
-### One numerical, one categorical (>2 levels) -  ANOVA
-
-**Observed stat**
-
-```{r}
-( F_hat <- fli_small %>% 
-  specify(arr_delay ~ origin) %>%
-  calculate(stat = "F") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-   specify(arr_delay ~ origin) %>%
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "F")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = F_hat, direction = "greater")
-null_distn %>%
-  get_p_value(obs_stat = F_hat, direction = "greater")
-```
-
-### Two numerical vars - SLR 
-
-**Observed stat**
-
-```{r}
-( slope_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>% 
-  calculate(stat = "slope") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "slope")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = slope_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = slope_hat, direction = "two_sided")
-```
-
-### Two numerical vars - correlation
-
-**Observed stat**
-
-```{r}
-( correlation_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>% 
-  calculate(stat = "correlation") )
-```
-
-```{r}
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "correlation")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = correlation_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = correlation_hat, direction = "two_sided")
-```
-
-
-### Two numerical vars - SLR (t)
-
-Not currently implemented since $t$ could refer to standardized slope or standardized correlation.
-
-
-```{r echo=FALSE, eval=FALSE}
-# **Standardized observed stat**
-( t_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>% 
-  calculate(stat = "t") )
-```
-
-```{r echo=FALSE, eval=FALSE}
-null_distn <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   hypothesize(null = "independence") %>%
-   generate(reps = 1000, type = "permute") %>%
-   calculate(stat = "t")
-
-visualize(null_distn) +
-  shade_p_value(obs_stat = t_hat, direction = "two_sided")
-null_distn %>%
-  get_p_value(obs_stat = t_hat, direction = "two_sided")
-```
-
-
-## Confidence intervals
-
-### One numerical (one mean)
-
-**Point estimate**
-
-```{r}
-( x_bar <- fli_small %>% 
-  specify(response = arr_delay) %>%
-  calculate(stat = "mean") )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "mean")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = x_bar) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-### One numerical (one mean - standardized)
-
-**Point estimate**
-
-```{r}
-( t_hat <- fli_small %>% 
-  specify(response = arr_delay) %>%
-  calculate(stat = "t") )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(response = arr_delay) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "t")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-
-### One categorical (one proportion)
-
-**Point estimate**
-
-```{r}
-( p_hat <- fli_small %>% 
-   specify(response = day_hour, success = "morning") %>%
-   calculate(stat = "prop") )
-```
-
-```{r}
-boot <- fli_small %>%
- specify(response = day_hour, success = "morning") %>%
- generate(reps = 1000, type = "bootstrap") %>%
- calculate(stat = "prop")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = p_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-### One categorical variable (standardized proportion $z$)
-
-Not yet implemented.
-
-### One numerical variable, one categorical (2 levels) (diff in means)
-
-**Point estimate**
-
-```{r}
-( d_hat <- fli_small %>%
-  specify(arr_delay ~ season) %>%
-  calculate(stat = "diff in means", order = c("summer", "winter")) )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "diff in means", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-### One numerical variable, one categorical (2 levels) (t)
-
-**Standardized point estimate**
-
-```{r}
-( t_hat <- fli_small %>%
-  specify(arr_delay ~ season) %>%
-  calculate(stat = "t", order = c("summer", "winter")) )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(arr_delay ~ season) %>%
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "t", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-
-### Two categorical variables (diff in proportions)
-
-**Point estimate**
-
-```{r}
-( d_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "diff in props", order = c("summer", "winter")) )
-```
-
-```{r}
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "diff in props", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = d_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-### Two categorical variables (z)
-
-**Standardized point estimate**
-
-```{r}
-( z_hat <- fli_small %>% 
-  specify(day_hour ~ season, success = "morning") %>%
-  calculate(stat = "z", order = c("summer", "winter")) )
-```
-
-```{r}
-boot <- fli_small %>%
-  specify(day_hour ~ season, success = "morning") %>%
-  generate(reps = 1000, type = "bootstrap") %>% 
-  calculate(stat = "z", order = c("summer", "winter"))
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = z_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-
-### Two numerical vars - SLR
-
-**Point estimate**
-
-```{r}
-( slope_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>%
-  calculate(stat = "slope") )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "slope")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = slope_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-### Two numerical vars - correlation
-
-**Point estimate**
-
-```{r}
-( correlation_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>%
-  calculate(stat = "correlation") )
-```
-
-```{r}
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "correlation")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", 
-                            point_estimate = correlation_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
-
-
-### Two numerical vars - t
-
-Not currently implemented since $t$ could refer to standardized slope or standardized correlation.
-
-
-```{r eval=FALSE, echo=FALSE}
-# **Point estimate**
-( t_hat <- fli_small %>% 
-  specify(arr_delay ~ dep_delay) %>%
-  calculate(stat = "t") )
-```
-
-```{r eval=FALSE, echo=FALSE}
-boot <- fli_small %>%
-   specify(arr_delay ~ dep_delay) %>% 
-   generate(reps = 1000, type = "bootstrap") %>%
-   calculate(stat = "t")
-( percentile_ci <- get_ci(boot) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = percentile_ci)
-( standard_error_ci <- get_ci(boot, type = "se", point_estimate = t_hat) )
-
-visualize(boot) +
-  shade_confidence_interval(endpoints = standard_error_ci)
-```
diff --git a/inst/doc/observed_stat_examples.html b/inst/doc/observed_stat_examples.html
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rel="stylesheet" type="text/css" />
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-<h1 class="title toc-ignore">Full infer pipeline examples using <code>nycflights13</code> <code>flights</code> data</h1>
-<h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>Updated on 2018-06-14</em></h4>
-
-
-
-<div id="data-preparation" class="section level2">
-<h2>Data preparation</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(nycflights13)
-<span class="kw">library</span>(dplyr)
-<span class="kw">library</span>(ggplot2)
-<span class="kw">library</span>(stringr)
-<span class="kw">library</span>(infer)
-<span class="kw">set.seed</span>(<span class="dv">2017</span>)
-fli_small &lt;-<span class="st"> </span>flights <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">na.omit</span>() <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">sample_n</span>(<span class="dt">size =</span> <span class="dv">500</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">season =</span> <span class="kw">case_when</span>(
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">10</span><span class="op">:</span><span class="dv">12</span>, <span class="dv">1</span><span class="op">:</span><span class="dv">3</span>) <span class="op">~</span><span class="st"> &quot;winter&quot;</span>,
-    month <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="dv">4</span><span class="op">:</span><span class="dv">9</span>) <span class="op">~</span><span class="st"> &quot;summer&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour =</span> <span class="kw">case_when</span>(
-    <span class="kw">between</span>(hour, <span class="dv">1</span>, <span class="dv">12</span>) <span class="op">~</span><span class="st"> &quot;morning&quot;</span>,
-    <span class="kw">between</span>(hour, <span class="dv">13</span>, <span class="dv">24</span>) <span class="op">~</span><span class="st"> &quot;not morning&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(arr_delay, dep_delay, season, 
-         day_hour, origin, carrier)</code></pre></div>
-<ul>
-<li>Two numeric - <code>arr_delay</code>, <code>dep_delay</code></li>
-<li>Two categories
-<ul>
-<li><code>season</code> (<code>&quot;winter&quot;</code>, <code>&quot;summer&quot;</code>),</li>
-<li><code>day_hour</code> (<code>&quot;morning&quot;</code>, <code>&quot;not morning&quot;</code>)</li>
-</ul></li>
-<li>Three categories - <code>origin</code> (<code>&quot;EWR&quot;</code>, <code>&quot;JFK&quot;</code>, <code>&quot;LGA&quot;</code>)</li>
-<li>Sixteen categories - <code>carrier</code></li>
-</ul>
-<hr />
-</div>
-<div id="hypothesis-tests" class="section level1">
-<h1>Hypothesis tests</h1>
-<div id="one-numerical-variable-mean" class="section level3">
-<h3>One numerical variable (mean)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( x_bar &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">10.4</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">10</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)</code></pre></div>
-<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAFQCAIAAADYxlZnAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3de3wTZb7H8Wcml6allAotrQhUSguFimIKoshVEAGhLIJdD7siLtgFYVkpergJlgPailyqi+g5iKLg5SXqyu4qgoIIuJwFbLULVCxS8chFWgQp9Jpkzh9zNif2mpYm0yf9vP/KPJk8+T0zSb6ZSyaKpmkCAADZqEYXAABAYxBgAAApEWAAACkRYAAAKRFgAAApEWAAAClJE2CXLl1avHhx7969Q0NDO3ToMHTo0Pfff9/zNwAJCQm//e1vm/x5+/TpM3HixEY80Ef1CCGmTZumeDCZTPHx8fPmzSstLfXF09VtyJAhY8aMqfGu++67T1GU//zP/6zS/uWXXyqKsnnzZm/691z+dayLrKysKsukc+fOf/jDH4qKijxna6qV4u7n8uXLiqK8+OKLV9+n8OVrBghIZqML8EpJScltt92Wl5c3bty4X/3qV5cuXfroo4/Gjx+/cOHCJ598Up+ndevWISEhBhaZnp6+cuXKy5cv+6eeefPmmc1mIURJScn+/ftXrFhRUFDw9ttv++4ZG2fRokUpKSnXXHONH57rwQcf7NChgxDiwoULBw8eXLt27datW/ft29e5c2d9Bi9XSpVVWV1TrVw/v2aAACNHgD377LNHjx7dsmWL+wv4M888M2nSpMzMzClTpsTHxwshDh48aGiNVfm6nvT0dJvNpt/WNC0lJWXLli15eXk9evTw6fM2iMViOX/+/BNPPPHcc8/54ekefvjhPn36uCc3bNjw+9//fuHChe6tvaZaKT5auc3tNQw0c3LsQvzHP/7Rpk2bCRMmuFtMJtP8+fNdLtc//vEPAwtrJhRFmTRpkhDin//8p9G1/EL79u2nTJmybt06QwqbOnXqv/3bv7311lsFBQX+f3YAviZHgGmadvny5e+++86zsVevXnl5eaNGjdInb7jhBvfxgwEDBkyfPv3DDz/s379/eHj4sGHDCgoK8vPzR4wYER4eHhMT81//9V/ufuLi4qZNm+bZs9lsXr58eY1lbNq06ZZbbmnTpk27du2GDh26Z88e/a4hQ4YsXbr0ypUriqJkZmZWqUcIsWvXrqFDh4aHh8fFxU2dOvWnn35y36VXm5OTc8sttwQHB8fFxTXimIp+OLBjx471liqEKCwsnDFjRkxMTHBwcGJiYpXDVEePHk1OTo6KioqIiJgwYcLRo0c9n+WFF15ISkpq06ZNlT5rk5GRERIS8sc//rG2i5Z5v/wbITU11el0/vWvf9UnPVdKbQuh+qq89dZbH3nkkc8//7xfv376w6usXCHEmjVrbrzxRv3FduDAAW9GZ/hrBpCdHAF27733Op3OXr16paWl5ebm6h+FZrM5ISGhXbt2NT7k73//++zZs++5555Zs2bt27dvxIgRgwcP7t69+7JlyywWy/Tp0/Py8hpaRlZW1uTJkxVFmTlz5vjx4/Py8u6++279NIFVq1bdf//9QUFBH3/88X333VflgX/+85/vvPPOs2fPzpo164477nj99df79OnjeYjlu+++mzhx4tChQ5955pnQ0NAZM2bs3LnT+8I0Tdu8eXOrVq2SkpLqLVUIMX78+FdffbV///5paWk2m2369OnvvvuuftehQ4f69u2bn5+fmpp6//33f/rpp7fddtvXX3+t35uenv7www9rmjZr1qzw8PCRI0e676pNdHT0kiVLPv30U/dT+FNiYqIQ4uTJk9Xvqm0h1Lgqf/jhh/HjxwshbrnllupdPf/88//xH/8xePDg+++//8svvxw4cOD+/fvrrc3Y1wwQCDQZuFyurKys9u3b6zVfe+21Dz744AcffOB0Ot3zJCYm/uY3v9Fv33777VarNT8/X598+OGHhRBPPfWUPvnZZ58JIV599VV9smvXrlOnTvV8OpPJtGzZMv12UlLShAkT9Nu33nrrzTff7H7SXbt2CSF27NihTz7xxBOtWrWqXk9lZWVcXFxiYmJxcbF+1/bt24UQGRkZ7mqFEB988IE+efbsWUVRFixYUNvSmDp1qhBi7ty58+bNmzdv3uzZs/v27RscHPzuu++656mj1FOnTnk+e1lZWXx8vL6F5HK5Bg8enJSUVFZWpt9bUFAQFhaWkpKiadrp06dtNtvw4cPLy8v1e5ctWyaEuPvuu2us89e//vV1112naVp5eXm3bt1iYmJKSko0TcvJyRFCbNq0qaHL3/N2FWvWrBFCHDx4sEq7y+Wy2Wz33HOPPuleKXUsBK3aquzXr58Q4plnnnG3uPspLi4WQlgslsOHD+t3ffPNNyEhIXfccYc3o/PbawYISHJsgSmK8sc//vGHH37Ys2fPokWLOnbs+Morr9x9992DBg26cOFCjQ/p06dPXFycfrt79+5CCPeX3ISEBCFESUlJQ8vYvn373r17VfX/Fpq+QVNeXl73o/Lz848fPz5nzpzQ0FC9ZcSIEXa7/cMPP3TPExsb694XGhUVFR0drZfncDiKPHh2u2rVqqeffvrpp59+7rnnDh48qKrq0aNHXS5XvaXabDZFUbZv33769GkhRFBQ0DfffJOVlSWEOH/+/GeffTZlypTS0tKLFy9evHgxPDx8xIgRu3fvFkJ8+umnZWVlS5YssVqterdz5szx5qw5q9WalZV18uTJFStW1Dtzk1MUxb0c3OpYCDWKiopKS0ur7d6JEyfqm3pCiPj4+AceeGD37t0VFRWNrvlqXjNAyyFHgOksFsvAgQOXL19+4MCBnJyc5OTkzz//fMmSJTXO7Hnetv755W6p/nHmpbCwsCNHjmRlZU2ZMqVnz54pKSnePOrEiRPiX/uy3BITE/V2XWxsrKIoVQoWQhw+fDjSg2cPpaWl7q8hhYWFc+fOXbx4sftHBXWU2rZt2xUrVugnlw8ePHjp0qXHjh3T78rPzxdC/OEPf7jGwzvvvFNUVKRp2vHjx4UQvXv3dnfVqlUr97eEuo0aNWrMmDGZmZk17s3znYsXL5aWlsbExFRpr2Mh1Kh79+51vGxuvPFGz8mbbrrJ5XJVOWTbIFfzmgFaDgle9EVFRRMnTvzggw88G3v37v3ee+9169ZN37XShCorK51OZ413LVy4sF+/fuvXr+/UqdPq1av//ve/e9OhpmlCCM/PGiGEqqoOh8M9GRQUVONj4+LiPvVQ21NERESkp6f37Nnz5Zdf9qbURx99tKCgICsr65prrnnmmWd69uz57LPPCiH0ga9YseLTX9q5c6emafrPzqqoMq46rF692ul0Pvroo3XPVsfyb4QjR44IIa6//vrqd9W2EGrUoN+x6VlS4zr1cnRX85oBWg4JAiwsLOy9996rfgqAyWSy2Wxt2rS5+qdw73kTQtT2xfn8+fOZmZmLFy8+cuTIsmXLRo4c2bp1a286j42NFUJ4nssnhDhy5Ig32y6hoaFDPNQxp6IoHTp0uHLlSr2lFhYWHjp0qG3btrNmzXr//fd/+OGHAQMGzJ8/3+FwdO3aVQjRqlUrzyetqKgoLy9XVbVLly5CiK+++srdVWlpqb5Z5o34+Pi0tLR33nlHPyDnyZvl3zgvvvii2WweO3ZslfY6FkIjnqXKjwS++uori8XiPiO0EaO7mtcM0HJIEGBWqzU5OXnTpk3vvfeeZ/vf/va33NzcYcOGXWX/NpstLy9P+9dJ3uvWratxtm+//VbTNM+9Ou+//77415dlnedHlVt8fHxsbOyaNWvchyg+/vjjQ4cOjRw58ior93Ty5Mn9+/fffPPN9ZaanZ3dt29f9297w8PDb7jhBkVRNE2Ljo622+2rVq368ccf9XsPHTo0atSoTz75RAhxxx13BAcHL1u2rLKyUr/3ueee0yPTS4sWLYqOjl66dKlno5fLvxE2bNjw1ltvTZo0qfouxDoWgt5S46qszTvvvOM+G/Pbb7/duHHjuHHjTCaT8GJ0Br5mANnJcSWO1atXHzhwYMKECf379+/Vq1dQUNDRo0d37tyZkJDw+OOPX2XnQ4YMef755ydPnjxo0KB9+/Z9+eWX+uWIqkhMTGzfvv2///u/FxQUREdH/+1vfzt8+LAQ4o033rjhhhu6dOlisVhKS0tXrlw5fPhwzwNFFovl6aefTklJ6dev38SJE3/88ceXX375+uuvf+SRR66m7PT0dPc+vfPnz7/77rslJSXp6en1ltq/f/9OnTo98sgjX331VYcOHfbv3//hhx9OnTrVYrEIIVatWjVixIh+/fqlpKRYLJaXXnopMjJyzpw5QoioqKh58+alp6fffvvto0aNOnbs2F//+lc9Mr3UunXrFStWTJ482bPRy+XvjXXr1umPvXjx4sGDBw8cONC5c2f3cUFPdS+E2lZlbXr16jVgwAB9XK+99pqqqvqKqHd0fn7NAIHGvyc9Np5+nkK3bt1sNlvbtm3tdvuyZcsuXrzonqHKafSe53b/6U9/EkJcuHDB3ZUQ4oUXXtAnf/7559/97ncRERHR0dH33XffuXPn9J+L6fd6nrp98ODBgQMHtmrVKj4+/tFHHy0pKZk2bVrr1q1ff/11TdOOHj1666232my25557rko9mqbt2LFj0KBBbdq06dKly4MPPnj+/Hn3XVWq1TTtuuuuc5/SXZ1+Gr2n1q1b9+/f/7PPPnPPU2+p48aNi4qKstlsCQkJTz75pOcpIQcOHLjzzjvbtm0bFRV17733un+NoGmay+V64YUX7HZ769atBw0atGvXrt///vf1nkbvyel03nbbbcLjNHrvl3+9p9G7KYrSsWPHmTNnnjt3znM2z5VSx0Kosir79es3bty4GvvRT6Pfvn37kiVLEhIS2rZtO2rUqKNHj7rnrHt0fnvNAAHp//eZAAAgEQmOgQEAUB0BBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkJIcF/MFWpTyl18WHn/sonbsaBk92sB6gOZJgmshluX+4q9yVVW1Wq0VFRUN+sMLGamqWuU/DAOVzWZzOBzyjvRyh3ZezhkWFnbp0qV6ZzN16iTKyv5/sv8A6wsvNbI4I1itViFERUWF0YX4nP5ZZHQVPmc2m81mc5nHa9L/bDd2r97ILkQAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUuJQU4G9Wq1VRlDpmcCqK5wVyFEUxmUxedq5pWsBfpAbQEWCAX5nN5rCwsLrnufjLSUVRLBaL909RWVnpdDobXhogGQIMMMBTTz1VUFBQ672VlZ7vzAsXLmTv2eNlzwMHDqx78w4IGAQYYICysrIrV67Udm+VS2xrmibvlY4B3+EkDgCAlJp+C2z37t1btmy5fPlyx44dZ86c2aFDByFEdnb2hg0bSktL7XZ7amqq/m8LNTYCAOCNJt4C+/7779evX7948eINGzYkJCRkZWUJIUpKStasWTN37tz169dfvHhx27ZttTUCzYF+0oT3WnvNbDaHhIQYPT4gQDTxFlhRUdGdd94ZHR0thBgyZMj27duFEDk5ObGxsbGxsUKI0aNHv/HGG+PGjauxsWmLARrHZDKZTKbi4mIv5y86fdr7zkNDQyMjIxtVF4BfaOIAs9vtdrtdCFFRUfHmm28OHDhQCFFYWNi+fXt9hqioqKKiotoadSUlJR999JF+u1u3bt1NNs+n0M+wUlU14E+1UhSlQT8AklqzGqn+0jp48KCX87+052PvOx84cOCMGTMaU5bXDF+YiqJomtZ8VqhPtYRhqqoqDB1pldOa3HxyFuL+/ftfeeWVfv36TZkypfpz67+yrLFRd+nSpaeeekq//cADD9xwV3L1pzCbW8r5ky3h7SH+tdFjdBUBojksTEVR9E+9gNdChimEaNCPEZtWbb/Nb+IY0DTtxRdfLCgoWLx4cadOnfTGiIiI3Nxc/XZRUVFERERtjbr27dvv2rVLvx0UFFR27DvPp1BV1Wq1VlRUBPzlBlRVVVW1JZw/bbPZHA5H8xmp2WyW+huS4QtTPyGroqLCwBr8Q/8sMroKn9PfEWVlZQbWYKupsYm/O+Tk5OTl5WVkZLjTSwiRlJSUn59/5swZTdN27NgxYMCA2hr/ryZVDfuXoKCgpq0QABAYmvhr5uHDh0+ePJmSkqJPhoSEbNq0KSQkJC0tLTMz0+Fw9OjRY+zYsfpd1RsBAPBSEwfY5MmTJ0+eXL3dfXJHvY0AAHijpRx+BAAEGAIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAls9EF1M9isXhOKooihDCbzZqmGVSRn+gjrTL8QKWqavMZqb7k5WX4wmw5L11FUVrIMIWhK9TlctXYLkGAORwOz0lVVVVVdTgcAR9gqqoqiuJ0Oo0uxOdMJpPL5Wo+IzWZTKoq8c4Jwxem/klX5Z0bkCwWS0sYpv6OaIYjlSDAqgSVezLgA0zTNEVRAn6Ybi1npH7QHBZmc6jBD1rIMEWzHKnEXzMBAC0ZAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJASAQYAkBIBBgCQEgEGAJCS2RedOp3OGTNmrFy5MiwsTG+ZO3duQUGBoihCiJEjRz700ENCiOzs7A0bNpSWltrt9tTUVKvV6otiAAABqekDbNu2bbt27Tp79qy7RdO0M2fOvPXWW54RVVJSsmbNmqVLl8bExGRkZGzbtm3cuHFNXgwAIFA1/S7Ezp07p6SkWCwWd0thYaEQYtWqVampqVlZWcXFxUKInJyc2NjY2NhYk8k0evTovXv3NnklAIAA1vRbYImJiUIIk8nkbvn5558TEhKmTp0aHh6+bt269evXp6WlFRYWtm/fXp8hKiqqqKjIPX9xcfHGjRv120lJSbeER3n2r++HNJlMqhrgB/AURVEUxWz2yW7e5kZV1eYzUtlfWoYvTP1N2nxWqO+0kHeo/o4wcKSaptXY7o+C4uPjlyxZot+eNGnS7NmzNU2rUpDL5XLfLisr++STT/Tbbdq0ua1fh+p9yv4R4yU9w4yuwh8URfH80oOrYfjCdH/LNLAG/zB8UfuTgSP1DAhP/giwEydOaJrWtWtXIYTZbNaXQkRERG5urj5DUVFRRESEe/7IyMitW7e6J8tyj3n2pqqq1WqtrKysbUgBQ1VVVVUdDofRhficzWZzOp0NHanvvsQYvgVzlRqxMJuWfrS7oqLCwBr8w2q1toRhms1ms9lcXl5uYA01hqc/tmOKi4szMjIuXLigadrWrVv79++vKEpSUlJ+fv6ZM2c0TduxY8eAAQP8UAkChsVisfqM1OkFtBz+eKPeeOONycnJ8+fP1zStZ8+eqampQoiQkJC0tLTMzEyHw9GjR4+xY8f6oRIEkuLi4n/+85++6Ll79+7t2rXzRc8AmpCvAuztt99231YUJTk5OTk5uco8drvdbrf7qAAEPJfLVVZW5qOefdEtgKbVIk6FAAAEHgIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlLpkDBBT9+ui+u+6q4RdaBNwIMCDQ/PTTT/q/7jW56OhoLhSJ5oPXIhBozp8//z//8z++6LlNmzatW7f2Rc9AI3AMDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUJLuZrsVg8JxVFEUKYzWZN0wyqyE/0kVYZfqBSVbVBI1VVvnsZw5s11XJeuoqitJBhCkNXaG2f9hIEWGVlpeekqqpWq9XhcLhcLqNK8g9VVVVVbQn/vWQymVwuV4NG2hI+NZonl8tV5S1ZndVqFdXeuQHJarW2hGGazWZVVY0daY1ZxddYAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlOoJsOHDhzudTs+Ws2fPjh8/3pclAQBQv5p/B7Zv3759+/YJIXbu3JmZmWkymdx3HT9+fO/evX6qDgCAWtQcYMePH//oo4/02zt27NB/hq1TVXXVqlX+KA0AgNrVHGBTpkyZMmWKEKJPnz47d+40myW4YAcAoEWpJ5kOHTpUWlr63XffVWmPi4vzVUUAAHihngDbsmXL/fffX15eXqU94C+kCwBo5uo5C3HevHm/+tWvvv7668Jf8k9xAADUpp4tsAsXLixevLh79+7+qQYAAC/VswU2dOjQvLw8/5QCAID36tkCe+yxx2bMmHH48OGkpKTg4GB3+/Dhw31cGAAAdaknwIYOHSqEyMzMrNJeVlbmq4oAAPBCPQFGUAEAmqd6AqyoqKjG9oiICB8UAwCAt+oJsMjIyBrb+R0YAMBY9QRYfn6++3ZpaenBgwf/9Kc/bd682cdVIRCoqup5Fc26KYriec1ob+ZvVFEAAkc9AVblklG9evWKjIycPXv2zp07fVkVAoHFYvE+ZkwmU4MCDAAafJXea6+99sCBA74oBYHnhx9+OHHihC96vuWWW3zRLQCJ1BNghw8f9py8cOHC8uXLO3bsWPejnE7njBkzVq5cGRYWprdkZ2dv2LChtLTUbrenpqZardbaGhFIXC6Xw+EwugoAgameAOvVq1eVloiIiLqPgW3btm3Xrl1nz551t5SUlKxZs2bp0qUxMTEZGRnbtm0bN25cjY2NHgYAoKWp51JSxdWcO3furrvuquMhnTt3TklJsVgs7pacnJzY2NjY2FiTyTR69Gj9D51rbAQAwEv1bIGFhoa6XK69e/fm5eU5HI7ExMTBgwfXfWQ+MTFRCOF5QL6wsLB9+/b67aioKP23ZTU26i5evOi+9sfgwYNHXP+LSwnrz242mwP+VH59pJ5fBeTCiYIBSVXVel+Tsr90vacoSgsZpjB0hdb2aV9PgJ07d2706NE5OTkxMTGqqhYUFNjt9g8//LC234d589wul6u2RvftS5cu6bfLy8tr+xxsCZ+PLWGMkE69L0tFUTRNayGv3pYwTH2MBo60kQH2yCOPmM3mEydOxMTECCFOnjx57733zpkzp0E/BYuIiMjNzdVvFxUV6VfxqLFR17Zt23Xr1rkny3KPefamqqrVanU4HJ6ZF5BUVVVVVd6TIIKCgowuAU3P5XJVVlbWPY9+QlZFRYVfKjKS1WptCcM0m81ms9nYkdaYVfUcA9u5c2dmZqaeXkKImJiYp59++pNPPmnQEyclJeXn5585c0bTtB07dgwYMKC2RgAAvNTg34E1QkhISFpaWmZmpsPh6NGjx9ixY2trBADAS/UE2LBhw+bNm/f222/rG2Hff//9vHnzhg0bVm+/b7/9tuek3W632+1V5qmxEQAAb9SzC/HZZ58VQnTt2jUuLi4uLi42NlYIkZWV5Y/SAACoXf1Xo//v//5v/TR6IURCQsKgQYNawlk3AIBmrp4Aq6ioePLJJ8+fP7927VohxM033zxkyJAnn3wyJCTEL+UBAFCzenYhLly48MUXX+zTp48+mZqa+vbbby9YsMD3hQEAUJd6tsC2bNmyevXq3/zmN/rkjBkzYmNjH3zwQf3YGIAWxWw2K4piNtfzueG+XE5D+3e5XAH/+040oXq2wK5cudKlSxfPltDQ0PLycl+WBKCZstls3szmdDqdTmdDO/fmIlWAp3q+Ig0dOjQ9Pf3NN99s166dEOLixYvLli0bMmSIP0oD0PwUFhYeOXLEFz3HxcV16tTJFz0jUNUTYGvXrh0xYkTHjh179uxpNpuPHDly3XXX7dq1yz/FAQBQm3oCLCoqKjs7e9u2bYcPH66srJw3b97YsWPZzAcAGK7+o6wmk2nMmDFjxozxQzUAAHipnpM4AABonggwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlBr8n9/+Z7Vaqzc24t/KZaQoiqrK+iVD/195wHuKotT4fm+2VFWVq+DG0d/LBo7U5XLV2C5BDFRUVHhO6q8Yh8NR25AChqqqqqo6HA6jC2mkoKAgo0uAZDRNq/J+b+asVqtcBTeO2Ww2m83GjrTGrJL12z0AoIUjwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUjL752nmzp1bUFCgKIoQYuTIkQ899JAQIjs7e8OGDaWlpXa7PTU11Wq1+qcYAEAA8EeAaZp25syZt956yzOiSkpK1qxZs3Tp0piYmIyMjG3bto0bN84PxQAAAoM/diEWFhYKIVatWpWampqVlVVcXCyEyMnJiY2NjY2NNZlMo0eP3rt3rx8qAQAEDH9sgf38888JCQlTp04NDw9ft27d+vXr09LSCgsL27dvr88QFRVVVFTknr+wsHDatGn67QkTJtzfb1D1Pi0Wix8qN5yiKCaTyegqGknfYwx4T1GUoKAgo6toAOkKvhoGjtTlctXY7o8Ai4+PX7JkiX570qRJs2fP1jRN0zTPeTzrCwoK6tevn367Y8eOVUpXFEVRlOo9BB7Zhylv9MJAtX1UNU+qqspVcOPon7oGjtTlctX4aeKPADtx4oSmaV27dhVCmM1m/XMtIiIiNzdXn6GoqCgiIsI9f1hY2MKFC92TZbnHPHtTVdVqtTocjoB/3aiqqqqqw+EwupBGUlXOcUXDaJpWWVlpdBUNYLVa5Sq4ccxms6qqxo60xn1u/viIKS4uzsjIuHDhgqZpW7du7d+/v6IoSUlJ+fn5Z86c0Wj6UjAAAAtSSURBVDRtx44dAwYM8EMlAICA4Y8tsBtvvDE5OXn+/PmapvXs2TM1NVUIERISkpaWlpmZ6XA4evToMXbsWD9UAgAIGP4IMEVRkpOTk5OTq7Tb7Xa73e6HAgAAgYejFAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAACkRYAAAKRFgAAAp+eNaiGi2rFar7zrnDy3RIMHBwYqi+PQ1WVFR4bvO4X8EWMulKIqqqj/99FNZWZkv+u/QoYMvukWgCgoKcjqdP/74oy86t9lsbdu21f8k1hf9wxAEWEt3+vTpc+fO+aLna6+91hfdIoBVVlZ+/fXXvug5MjKybdu2vugZBuIYGABASgQYAEBKBBgAQEoEGABASgQYAEBKBBgAQEoEGABASgQYAEBKBBgAQEoSXInDYrF4TupX2DObzQF/SRh9pFWGD6DRzOYm/sRTFKUlvEMN/yxyuVw1tksQYJWVlZ6TqqparVaHw1HbkAKGqqqqqjocDh/1ryiKyWTyUedAM+RwOJr2i6/Vaq3yARWQzGazqqrGjrTG8GQXIgBASgQYAEBKBBgAQEoEGABASgQYAEBKBBgAQEoEGABAShL8DgwAmoSiKPpvcpuQqv7fZkDA/zK1GSLAAAS+0NBQIYTVam3ynt19VlRUkGF+RoABCHz6dtKXX37pi8tJWCyW3r17N/m2HepFgAFoKS5fvlxRUdHk3fpiww7eIMCaO94bAFAjAqxZUxRFVdWzZ8/6Yt+62Wxu3759k3cLAP5BgEng22+/LS8vb/JuQ0NDCTAA8uJ3YAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAAClxGj0AXBX9IlImk8l9Yd8m53Q6udBidQQYAFwVi8UihCgpKfHFdaqEEOHh4aqqEmDVEWAA0AQKCgoKCwt90XP//v3NZj6ra2DkQsnOzt6wYUNpaandbk9NTZX3on8+2m+gqirXtwaA2hgWYCUlJWvWrFm6dGlMTExGRsa2bdvGjRvnu6fzXRKYzWaTyeSjzgFA+OavON09CyE0TfNyzobypudGMyzAcnJyYmNjY2NjhRCjR49+4403fBdgVqvVdwdXhRDl5eVffPGFL3qOiYm57rrrfNEzAFnon2CGf1EOCgpq3APLysqathI3xafxWIf333//1KlTM2fOFEKcOnVq0aJFGzdu1O86e/bsmDFj9NsPPPDAH+5KNqRCwFvdYpq2v4shIVppqXvSMmBQ6IbXmvYpAIm4XC41oUv1dsO2wKoEp+cJNmFhYQsXLtRvd+vW7XKHdp5zmkym4ODgkpIS78/J8emXF33T3hcnCOnHwDRN893ZRz49tcn7zoOCghwOh9Pp9L5z7/d7NEKD16nX/xVgsVgqKysbuthdwUHlMdFezuzTderlYtfPymvofx/77q2k88WS0Veojzp3M3ydmkwmk8nUuHMsXS5Xg97atXUSVlO7YQEWERGRm5ur3y4qKoqIiHDfFRIScs8997gni4qKPB9osViCg4MrKiocDod/SjWKxWIxm82lHt/EA1VQUFB5eXlLGGlYWFhxcXG9s5l++WnicDgqvHhU89GmTRtN07wZqey8XKGyCwkJCQ4OboYjNexKHElJSfn5+WfOnNE0bceOHQMGDDCqEgCAjAzbAgsJCUlLS8vMzHQ4HD169Bg7dqxRlQAAZGTk78DsdrvdbjewAACAvLiYLwBASgQYAEBKBBgAQEoEGABASgQYAEBKBBgAQEqGXQux0c6cOfPOO+/cd999kZGRRteCprFu3br+/fv37t3b6EKai7JVq4THhWbULl2sKSkG1tNQW7duNZvNd999t9GFoGkcOnQoOzs7NTXV6EKqku9P0s6dO/fqq6+OGDGCAAsYr732Wnh4OAHmZps71+gSrsqOHTtsNhsBFjByc3PfeOONZhhg7EIEAEiJAAMASEm+Y2AVFRWFhYWRkZFWq9XoWtA0Tp8+HRYWFhoaanQhaBpFRUWKorRr167+WSGD4uLi4uLiDh06GF1IVfIFGAAAgl2IAABJSRlgX3zxxUMPPWR0FWgCH3zwwdSpUx944IHly5f//PPPRpeDxnM6nampqZcuXXK37N69e+bMmQ888MCiRYtOnz5tYG1ohOor9Pvvv1+wYIG+Qs+fP29gbW7yBdi5c+eef/559nwGgFOnTr366qsZGRkvv/zyNddc89ZbbxldERpp27Zt8+fPP3v2rLvl+++/X79+/eLFizds2JCQkJCVlWVgeWio6ivU4XA88cQTv/71r1955ZXY2NiNGzcaV93/kyzAKisrV65cOWnSJKMLQRMICgoymUwOh8PpdDqdTofHT3chl86dO6ekpFgsFndLUVHRnXfeGR0dbTabhwwZwhaYXKqv0Ozs7KioqN69e6uqOnny5N/97ncGlucm2Q+ZX3rppYEDByYkJBhdCJpARETExIkTp0+fbrPZwsLCVq9ebXRFaKTExEQhhMlkcre4/662oqLizTffHDhwoGHFoeGqr9Aff/wxJCRk2bJl3333XUxMTDP5UbNMW2C7d+++dOnSmDFjjC4ETSMvL2/37t1r16597bXXhg4dumXLFqMrQhPbv3//rFmz2rVrN23aNKNrwVUpLy//5ptv7rnnnrVr13bu3LmZfN2UKcBycnLy8vIeeuihxx9/vKioaNq0aRcuXDC6KDReTk5Onz59OnfubLPZxowZ8/HHH3NoM2BomvbCCy/8+c9/Xrx48dSpUz2/y0NGbdu2TUxMTExMDA4OHjNmzIkTJ5rDu1WmXYhz5szRb/zwww/p6ekvvfSSsfXgKsXFxW3evHnUqFGRkZGfffZZp06dFEUxuig0Df3r5po1a4iuwJCUlLR58+Yff/wxIiJiz549CQkJzeHdKlOAIcD07dv31KlTCxYsUBSlQ4cOaWlpRleEJnP48OGTJ0+m/Osi+iEhIZs2bTK2JFyNNm3azJgxY/ny5ZcvX46JiZk9e7bRFQnBlTgAAJKS6RgYAABuBBgAQEoEGABASgQYAEBKBBgAQEoEGABASgQYAEBKBBjQTG3cuHHfvn1XPw8QqAgwoJkiwIC6EWAAACkRYIDx3nvvvZtuuik4ODgmJmblypVCiD59+nz22WcLFiwYPny4EOLYsWNjxoyJjIxs3br1wIEDs7Ozq88DtDRcCxEwWEFBQXx8/Jw5c+699949e/Y89thje/fuvfXWW4cNGzZixIj58+ebTKaEhIRrr7328ccfF0IsXbr0ypUrX3zxhcPh8JzH6HEA/sbV6AGDnThxwul0pqamxsfH9+3bt0ePHh07djSbzYqimEwmk8nkcrmmT5+enJwcGxsrhDh9+rT+10Ke8xg9CMAABBhgsNtvv33s2LE33XTTqFGjhg0b9tvf/jYsLMxzBlVVZ8yYsWfPnvfee++LL77YsWNHc/grJsBwHAMDDGaz2f7yl7/k5uba7fbXX3+9U6dOf/nLXzxnuHz58u233/7oo49WVlbOnDlz3bp1RpUKNCsEGGCwXbt2LVy4sGvXrosWLfr8889HjBjxyiuveM6we/fuvLy8gwcPLliwYMCAAU6n06hSgWaFXYiAwUwmU0ZGRps2be666668vLz9+/enpqYKIVRVPX78+NmzZ8PCwkpKSjZv3jxy5MiDBw8+/vjjV65cOXXq1HXXXeeeJzo62uhxAH6nATDas88+26VLF6vV2qlTp8cee6y8vFzTtI0bN7Zr127cuHEulys9Pb19+/Zt27a99957jx07lpiY2LdvX895jB4BYABOowcASIljYAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAACkRYAAAKRFgAAApEWAAACkRYAAAKf0vwG1DEplejz0AAAAASUVORK5CYII=" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.801</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-variable-standardized-mean-t" class="section level3">
-<h3>One numerical variable (standardized mean <span class="math inline">\(t\)</span>)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( t_bar &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">6.93</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">mu =</span> <span class="dv">8</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)</code></pre></div>
-<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_bar, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-variable-median" class="section level3">
-<h3>One numerical variable (median)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( x_tilde &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-2</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">med =</span> <span class="op">-</span><span class="dv">1</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;median&quot;</span>)</code></pre></div>
-<pre><code>## Setting `type = &quot;bootstrap&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> x_tilde, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.076</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-categorical-one-proportion" class="section level3">
-<h3>One categorical (one proportion)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( p_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.466</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
-<pre><code>## Setting `type = &quot;simulate&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> p_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.101</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>Logical variables will be coerced to factors:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour_logical =</span> (day_hour <span class="op">==</span><span class="st"> &quot;morning&quot;</span>)) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour_logical, <span class="dt">success =</span> <span class="st">&quot;TRUE&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, <span class="dt">p =</span> .<span class="dv">5</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)</code></pre></div>
-<pre><code>## Setting `type = &quot;simulate&quot;` in `generate()`.</code></pre>
-</div>
-<div id="one-categorical-variable-standardized-proportion-z" class="section level3">
-<h3>One categorical variable (standardized proportion <span class="math inline">\(z\)</span>)</h3>
-<p>Not yet implemented.</p>
-</div>
-<div id="two-categorical-2-level-variables" class="section level3">
-<h3>Two categorical (2 level) variables</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.0205</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
-<pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.65</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="two-categorical-2-level-variables-z" class="section level3">
-<h3>Two categorical (2 level) variables (z)</h3>
-<p><strong>Standardized observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( z_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.4605</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;winter&quot;</span>, <span class="st">&quot;summer&quot;</span>))</code></pre></div>
-<pre><code>## Setting `type = &quot;permute&quot;` in `generate()`.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> z_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.633</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>Note the similarities in this plot and the previous one.</p>
-</div>
-<div id="one-categorical-2-level---gof" class="section level3">
-<h3>One categorical (&gt;2 level) - GoF</h3>
-<p><strong>Observed stat</strong></p>
-<p>Note the need to add in the hypothesized values here to compute the observed statistic.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( Chisq_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, 
-              <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;EWR&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;JFK&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;LGA&quot;</span> =<span class="st"> </span>.<span class="dv">34</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">10.4</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;point&quot;</span>, 
-              <span class="dt">p =</span> <span class="kw">c</span>(<span class="st">&quot;EWR&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;JFK&quot;</span> =<span class="st"> </span>.<span class="dv">33</span>, <span class="st">&quot;LGA&quot;</span> =<span class="st"> </span>.<span class="dv">34</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;simulate&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.005</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="two-categorical-2-level-variables-1" class="section level3">
-<h3>Two categorical (&gt;2 level) variables</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( Chisq_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">formula =</span> day_hour <span class="op">~</span><span class="st"> </span>origin) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">9.027</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;Chisq&quot;</span>)
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> Chisq_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.007</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-means" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">2.266</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.456</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-t" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (t)</h3>
-<p><strong>Standardized observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( t_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.7542</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> t_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.487</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>Note the similarities in this plot and the previous one.</p>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-medians" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in medians)</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">2</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(dep_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># alt: response = dep_delay, </span>
-<span class="st">  </span><span class="co"># explanatory = season</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in medians&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> d_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.029</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="one-numerical-one-categorical-2-levels---anova" class="section level3">
-<h3>One numerical, one categorical (&gt;2 levels) - ANOVA</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( F_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>origin) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">1.084</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>origin) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;F&quot;</span>)
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> F_hat, <span class="dt">direction =</span> <span class="st">&quot;greater&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.353</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="two-numerical-vars---slr" class="section level3">
-<h3>Two numerical vars - SLR</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( slope_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">1.017</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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gEGAJCSHF/mCwCQguf7752X/nyjpUcP7dJfZ60rEnwXYvmBX34qV1VVm83mcDhq9WsXoVPaoondbi8uLg53IRfFxcUJIc6fPx/uQi6KiYkpKyurwx+ovBIWiyU+Pv7cuXPen4MJL0VRYmJijK8DNoPY2FhVVc+dOxfuQi6Kjo4uLy83yeDRNK1hw4bnz593OBzhruWiuLg4U/2nWyyWoqIiIYTy2WfqyJG+c61PPWsZc/8VriLyVzdWncgpRACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJT4Kimzs9lsiqLUcGFVVYUQERERNVxe13XzfLMAANQKAWZqNpvN+HaoWomNja35wqb6dhwAqDkCTAIzZ840vmSsbsXFxS1ZsqTmh3cAYCoEmATKyspKSkrqvFmr1VrnbQJAveEiDgCAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQs4S4gOIvllyIVRRFCaJqmqqaIXrvdbrFYoqOjQ9S+pmkhatkrIiLCt4frlsViiYqK0nU9RO3XijFmIiMjbTZbuGsRQghFUUI6eGrLYrEoimKeeqxWq6qqHo8n3IUI8fOeJzIy0mq1hruWizRNM8/Gslgsqqoa9XgiIx2XztVU9Qp3MtXtQyQIMN8RrCiKpmkej8ck+0SHw6GqakVFRYjat1qtod7bOp1Op9MZosZVVXU4HCbZB2maFhER4XQ6XS5XuGsRQghFUUI6eGrLeF1onnqMweN2u8NdiBBCqKoaGRkZ0n+W2rJYLObZWJqmKYpysZ4qXaTr+hXuBK6SADNeRF95d9QVl8vl8XhCt0OshwPNkNav67rb7TbJPsjgdrvNE2C6rpukGCGEruumqscYmSYZPMa5EPMMHiGE2TaWqqpGPYrbXWm35QnZHtsUJ+IAAKgtAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICVLiNr98ssvV6xYsXLlSuPu/v37V69eXVZWlp6enpmZabPZqpsIAEBNhOQI7PTp06+88oqu68bd0tLSpUuXTp8+feXKlUVFRZ9++ml1EwEAqKG6DzCn07l48eKxY8d6p+Tm5iYnJycnJ2uaNnjw4J07d1Y3EQCAGqr7U4irVq3q1atXWlqad0pBQUHTpk2N24mJiYWFhdVNNJSXl+/atcu4ff3117fWNO8sRVGEEKqqGjfCzmazaZoWERERovYtllCd462fVWiaZrPZPB5P6FZRc6qqCiGsVqtxwwxUVQ3d4KktTdMURTFVPSYcPCbZ8wjzDR5vPbrV6r50rqooms9u/DJ4z+dVUsc7r+3bt58/f37IkCHff/99des2RqTfiYaioqKnnnrKuP3ggw8+9uthlYsO/W69hqwxMUIIq9Ua7kIuX1RUVEjbN1vn2O32cJdwCbP1T2xsbLhL+IXZ3hcP9T9LbZlqY4mf63HZ7Rcuna5p2hWO8+pex9RxEuTm5ubl5f3mN79xuVxFRUUTJ05ctGhRQkLCgQMHjAUKCwsTEhKEEH4nGpo0afLRRx8Zt2NjYyuOn/LOUlXVarU6HI7qArmelRUVRUVFlZaWhqh9q9UaExMTosYNJSUlDocjRI3b7fby8nKTvIjWNC0uLu7ChQsulyvctQghhKIodru9pKQk3IVcFB0drarqhQsXgi9aL6KioioqKkwyeFRVbdCgQXFxsdPpDHctF8XExBQXF4e7iovsdrvFYjl//rwQQlQZQi6XS1RUXOEq/B5s1nGATZ061bhx4sSJrKysVatWCSE6der02muvnTx5MikpafPmzT179qxuokHTtObNm3vvlusnvbe9uWWSAPN4PLquu93u4Itelis87q4Jj8cTuvp1XQ9p+7VinPwxVT0hHTy1peu62eoxz8YymKoes20sbz2Kx1PpHL0esj12fZyLs9vt06ZNy87Odrlc7dq1Gzp0aHUTAQCooVAFWIsWLYzDL0N6enp6enqlZfxOBACgJsxyORYAALVCgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkFCTA+vfv73a7faecOnVqxIgRoSwJAIDgLH6n7tq1a9euXUKILVu2ZGdna5rmnXX06NGdO3fWU3UAAFTDf4AdPXr0z3/+s3F78+bNiqJ4Z6mqumTJkvooDQCA6vkPsPHjx48fP14I0blz5y1btlgs/hcDACBcgiTTvn37ysrKvvvuu0rTU1JSQlURAAA1ECTA1q9f/8ADD1RUVFSarut6yEoCACC4IFchzpo16+677/7HP/5RcKn6KQ4AgOoEOQI7e/bsnDlzbrzxxvqpBgCAGgpyBJaRkZGXl1c/pQAAUHNBjsBmzpw5adKkgwcPdurUKSoqyju9f//+IS7sF76fQjMu6FdV1ffK/jCKiIjQNC0yMjJE7dfD9Z9WqzV0nalpWkREhMfjCVH7taKqqhDCZrP5jqgwUhQlpIOntjRNUxTFVPWYcPAYN8xAVVVTbSxvPbrN5rp0rqooV/hPV91VF0H2jxkZGUKI7OzsStPLy8uvpJqrhtGtobukpR4ultF1PaRrCXX7NefdWCapR5isGIOp6jFP/5hw8AiTbSwR+v1hVUECzAxB5ftdVsbLH4/HY5LXZQ6Hw2KxVL1Ks67ouu574BsKLpcrdPVbrVaHw1Hp28jCxTicdTqdTqcz3LUIIYSiKFarNXSdX1vG4YV56rFYLOYZPJqmRUdHO51Oh8MR7louioiIMM/GMk7kGPUoTmelo1SProdoOwYJsMLCQr/TExISQlAMAAA1FSTAmjRp4ne62Q5dAQDXmiABduTIEe/tsrKyvXv3/v73v3/77bdDXBUAAEEECbBKXxnVsWPHJk2aPP7441u2bAllVQAABFHrS0Kvu+66PXv2hKIUAABqLsgR2MGDB33vnj179vnnn2/RokUoSwIAILggAdaxY8dKUxISEngPDAAQdkEC7MKFC5WmREdHm+RbMAAA17IgARYTE+PxeHbu3JmXl+dyuTp06NCnTx8CDAAQdkEC7PTp04MHD87NzW3durWqqvn5+enp6Rs3bqzu82EAANSPIFchPvnkkxaL5dixY8eOHTt69OixY8cURZk6dWr9FAcAQHWCHIFt2bLlv//7v1u3bm3cbd269QsvvDBmzJjQFwYAQCBm+WkAAABqJUiA9evXb9asWcePHzfu/utf/5o1a1a/fv1CXxgAAIEECbCXXnpJCNG2bduUlJSUlJTk5GQhRE5OTn2UBgBA9YJ/G/3//u//GpfRCyHS0tJ69+7NZfQAgLALEmAOh2P+/PlnzpxZtmyZEOLWW2/t27fv/Pnz7XZ7vZQHAIB/QU4hzp49e8WKFZ07dzbuZmZm/uEPf3j66adDXxgAAIEECbD169e/+OKL48ePN+5OmjTp9ddfX79+fcjrAgAgoCABVlJS0qZNG98pMTExFRUVoSwJAIDgggRYRkZGVlbWmTNnjLtFRUXz5s3r27dvyOsCACCgIBdxLFu2bMCAAS1atGjfvr3FYjl06FDz5s23bt1aP8UBAFCdIAGWmJi4f//+Tz/99ODBg06nc9asWUOHDrVarfVTHAAA1QkSYEIITdOGDBkyZMiQeqgGAIAa4rsQAQBSIsAAAFIiwAAAUiLAAABSIsAAAFIiwAAAUgp+GT0C0zStYcOGIWqcX64BgOoQYFdKURSHw/HXv/41FI23a9cuJSUlFC0DgOwIsDpQUlLy7rvvhqLlMWPGEGAA4BfvgQEApESAAQCkRIABAKRU9++Bbd++ff369cXFxS1atJg8eXKzZs2EEPv371+9enVZWVl6enpmZqbNZqtuIgAANVHHR2D/+te/Vq5cOWfOnNWrV6elpeXk5AghSktLly5dOn369JUrVxYVFX366afVTQQAoIbq+AissLDwzjvvTEpKEkL07dt306ZNQojc3Nzk5OTk5GQhxODBg9etWzd8+HC/E41G3G73qVOnjNuxsbERPp+F8n4uyiQfkFJV1SSVXDZVVTVNC1HjiqKoqllOUxuVhPT51oqiKIqimKQYYcp6GDwBmG1j/VJPla2mhGyPXccBlp6enp6eLoRwOBzvvvtur169hBAFBQVNmzY1FkhMTCwsLKxuoqGgoMAbZg8++OBjvx5WaS3mOdkYER8f7hKuVHR0dHR0dOjaj4iICF3jlyE2NjbcJVzCPIPZELpP5V+GyMjIcJdwiZiYmHCXcAlTbSzxcz2u2NgLl063WCxXuB/weDx+p4fkc2C7d+9+4403unbtOn78eCGErutVS/E70dCoUaPly5cbt6+77jrHuXLvLEVRrFar0+ms9PBwqbhwwW63h7uKyxTS3DK43e6ysjK32x3qFdWEpmkxMTElJSUulyvctQghhKIoUVFRpaWl4S7kIrvdrihKSUlJuAu5KDIy0ul0mmTwqKoaGxtrnsEjhIiOjjbPxoqKitI0rbi4WAghqlTldrsdDscVrsLvC706DjBd11esWJGfnz9nzpyWLVsaExMSEg4cOGDcLiwsTEhIqG7ixUJttttuu817t/zsN97bxoG8ruvVBXI9M0+UXgbjeH/FihVffvllKNpfsGBBfHy8efZBxpZyuVxOpzPctQghhKIoERERJilGCOHxeFRVNU89NpvNPIPH+Gdxu93m6R+Px2OeYiIiIryDR3G5Kp1D9IRsj13HAZabm5uXl7d06VLfk7OdOnV67bXXTp48mZSUtHnz5p49e1Y3EfWvoqIiRK/j5I12AFKo4wA7ePDg8ePHR48ebdy12+1r16612+3Tpk3Lzs52uVzt2rUbOnSoMavqRAAAaqiOA2zcuHHjxo2rOt17cUfQiQAA1IRZrlIFAKBWCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQIMACAlAgwAICUCDAAgJQs4S4gOFX9JWUVRTH++k4MI4vFYpQEvxRF0TTNJF2kaZrxV9f1cNcihBCKoiiKYrGY5X/QbPWoqmqe/y9jh6Npmnn6x2wb65d6NK3SP5h6xXvs6v5nzfL8A/AbYCYZ1ub5BzMnY0ybJDCMgWSeTWa8DrNareEu5CJjH2SqeiwWi0leqhpjxngNZBKmHTy6xeK8dO6VH3JIHGAul8t7W1VVTdM8Ho/H4wljSV7l5eU2my3cVZiXx+OpqKhwu93hLkQIISwWS1RUVEVFhdPpDL506BmHp2VlZeEu5CIjLcxTj6qq5eXlJhk8mqbZ7XaHw+FwOMJdy0VWq9U8G8s4NjXqUSoqKoWV2+NRfHbjl8dvVpvi1Q0AALVFgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgN26pCEAAAqeSURBVAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKREgAEApESAAQCkRIABAKRkCXcBuGrFx8dbLJaGDRuGqP2KiooLFy6EqHEA5keAIVRUVf373//+xRdfhKLxjIyMVq1ahaJlALIIZ4Dt379/9erVZWVl6enpmZmZNpstjMUgFI4fP75169ZQtNy+fXsCDLjGhe09sNLS0qVLl06fPn3lypVFRUWffvppuCoBAMgobEdgubm5ycnJycnJQojBgwevW7du+PDhIVqXxWLRNC0ULdvi41WVC2HCwG63q6oaGRlZ84cYW8pms9VkMHg8HofDcfn1AQi9sAVYQUFB06ZNjduJiYmFhYXeWadOnRoyZIhx+8EHH3zs18MqPdY8JxsVVRVCJCQkrFu3LnRrWbZsWegaf+KJJ0LX+JAhQ7ybMhRiYmJq+5CoqKhQVHLZIiIiwl3CJRISEsJdwi/MtrHi4uLCXcIlTLWxxM/1uBo0qHRtldViiajNa82qPB6P3+lhCzBd133v+tYXFxc3e/Zs4/YNN9xQ3Kyxd5amaVFRUaWlpdU9H79UVQ3REZioqLBYLB6Pp1b11IqqqjVv3Gq1CiGcTmcoGq8to9tdLlelbV0nFEURVUZR0IfYbDan0xn0KSuKoihKPWxTq9Va841V28ZrqyaD5zK6veYqdbvFYnG73XW4risZ7UEHT6j/lao2XleDp062qcViUVXVOGnhqVJVRYNop89u/DJ4PB6/rx3CFmAJCQkHDhwwbhcWFvq+lLDb7ffcc4/3ru/BmdVqjYqKcjgcLper3koNQFVVu91eXFwc7kIuMl4hmufi8piYmLKyMrfbHe5ChBDCYrHYbLbS0tI6z4zLoyhKTEyMeTZWbGysqqrmqSc6Orq8vNwkg0fTNJvNVlZWZp4Ty3FxcebZWDExMRaLxahHKS2t9LaKy+XylJeHYr1he/+mU6dOR44cOXnypK7rmzdv7tmzZ7gqAQDIKGxHYHa7fdq0adnZ2S6Xq127dkOHDg1XJQAAGYXzc2Dp6enp6elhLAAAIC8uAQcASIkAAwBIiQADAEiJAAMASIkAAwBIiQADAEhJCdG3woTIyZMn33///fvuu69JkybhrsWMPv74Y13Xhw2r/O2REEKcOXNm3bp1I0eObNasWbhrMaNNmzYVFxePHDky3IWY0fnz59esWTN06NDrr78+3LWY0datW3/88ccxY8YIITzHjzvee893rrV/f61Tp1CsV7IftDx9+vSaNWsGDBhAgPm1ZcsWj8dDgPn1008/rVmzpkePHgSYXzt27Dh9+jQB5ldxcfGaNWtuvfVWAsyv3bt3Hzp0yAgwtXXryFmz6me9nEIEAEiJAAMASEmy98AcDkdBQUGTJk3M85NgpnLmzBkhROPGV/TLBVcrp9N5+vRpBk91fvrpJ7fbzcl5v9xu96lTpxo3blyr31C9dpw9e9bpdHp/4rHeSBZgAAAYOIUIAJCSSa9C3L9//+rVq8vKytLT0zMzMyud89m+ffv69euLi4tbtGgxefJk46Ky6dOn5+fnG78uOnDgwN/85jfhKT3EAveM37mBH3KVYeQEELhz/PYDg8fw0UcfvfXWW967TqfzzTffbNSo0bUzeIQQbrd70qRJixcvNn4411fY9jy6+ZSUlNx///3ffvuty+WaN2/ehx9+6Dv3+PHjY8eOPXnypNPpfOutt2bOnKnrusfjGTNmTEVFRZhKrieBe8bv3MAPucowcgII3Dl++4HB49cXX3wxf/58/VoaPLqub9y4ccaMGUOHDj137lylWWHc85jxFGJubm5ycnJycrKmaYMHD965c6fv3MLCwjvvvDMpKclisfTt2/eHH34QQhQUFAghlixZkpmZmZOTY55f2q5bgXvG79zAD7nKMHICCNw5fvuBwVNVeXn5W2+9lZmZKa6lwSOEaNWq1ejRo61Wa9VZYdzzmDHACgoKvFezJCYmFhYW+s5NT08fP368EMLhcLz77ru9evUSQpw7dy4tLW3ChAnLli0TQqxcubK+i64XgXvG79zAD7nKMHICCNw5fvuBwVPVunXrBg0alJCQIK6lwSOE6NChQ5cuXTRNqzorjHseswTYtm3bpk2bNm3atK+//lq/9MJIj8dTdfndu3dPmTKlcePGEydOFEKkpqbOnTu3adOmNptt7Nixe/bs0a/GqysD94zfuTXpzKsGIyeAwJ3jtx8YPJX83//93/79+wcNGmTcvXYGT2Bh3POY5SKOjIyMjIwM4/b58+cPHDhg3C4sLDRe7Hjpur5ixYr8/Pw5c+a0bNnSmHjs2DFd19u2bSuEsFgsfl8mXAUSEhIC9IzfuYEfcpUJ/GSv5ZEjgnWO335g8FSybdu222+/XVUvvu6/dgZPYGHc85jlCMxXp06djhw5cvLkSV3XN2/e3LNnT2P6iRMnKioqcnNz8/LyFixY4N0HCSEuXLiwYMGCs2fP6rr+0Ucfde/e3bgu6CoTuGf8zq3uIVclRk4AgTvHbz8weMTP/SOE0HV9165d3bp18z7k2hk81Qn7nsekH2Tev3//mjVrXC5Xu3btHn74YeOdw7vvvvt3v/vdvn37PvjgA4vl4rGj3W5fu3atrusff/zxJ598out6+/btMzMz7XZ7WJ9BqATomfbt2/ud63fi1YqRE0CAzmnXrp3ffmDweP+5jhw5kp2dvWrVKm9KXVODxzB69OhVq1Z5L6MP+57HpAEGAEBgZjyFCABAUAQYAEBKBBgAQEoEGABASgQYAEBKBBgAQEoEGABASgQYYFJvvvnmrl27rnwZ4GpFgAEmRYABgRFgAAApEWBA+G3YsOHmm2+Oiopq3br14sWLhRCdO3fesWPH008/3b9/fyHEN998M2TIkCZNmsTGxvbq1Wv//v1VlwGuNXwXIhBm+fn5qampU6dOvffeez/77LOZM2fu3LmzW7du/fr1GzBgwFNPPaVpWlpa2nXXXffss88KIZ577rmSkpIvv/zS5XL5LhPu5wHUN7P8HhhwzTp27Jjb7c7MzExNTe3SpUu7du1atGhhsVgURdE0TdM0j8fzyCOPDBs2LDk5WQjxww8/TJ06VQjhu0y4nwQQBgQYEGY9evQYOnTozTffPGjQoH79+t1///3en6swqKo6adKkzz77bMOGDV9++eXmzZuvtd+dAvziPTAgzCIjI//4xz8eOHAgPT39nXfeadmy5R//+EffBYqLi3v06DFjxgyn0zl58uTly5eHq1TAVAgwIMy2bt06e/bstm3bPvPMM59//vmAAQPeeOMN3wW2b9+el5e3d+/ep59+umfPnm63O1ylAqbCKUQgzDRNW7BgQYMGDX7961/n5eXt3r07MzNTCKGq6tGjR0+dOhUXF1daWvr2228PHDhw7969zz77bElJyffff9+8eXPvMklJSeF+HkC90wGE20svvdSmTRubzdayZcuZM2dWVFTouv7mm282btx4+PDhHo8nKyuradOmjRo1uvfee7/55psOHTp06dLFd5lwPwMgDLiMHgAgJd4DAwBIiQADAEiJAAMASIkAAwBIiQADAEiJAAMASIkAAwBIiQADAEiJAAMASIkAAwBI6f8DzxKZfbuOuJwAAAAASUVORK5CYII=" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> slope_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="two-numerical-vars---correlation" class="section level3">
-<h3>Two numerical vars - correlation</h3>
-<p><strong>Observed stat</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( correlation_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.8943</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
-
-<span class="kw">visualize</span>(null_distn) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">null_distn <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> correlation_hat, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="two-numerical-vars---slr-t" class="section level3">
-<h3>Two numerical vars - SLR (t)</h3>
-<p>Not currently implemented since <span class="math inline">\(t\)</span> could refer to standardized slope or standardized correlation.</p>
-</div>
-<div id="confidence-intervals" class="section level2">
-<h2>Confidence intervals</h2>
-<div id="one-numerical-one-mean" class="section level3">
-<h3>One numerical (one mean)</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( x_bar &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> arr_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.572</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(<span class="dt">response =</span> arr_delay) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;mean&quot;</span>)
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">1.436</td>
-<td align="right">7.819</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img 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B3HcREREbW1tc4fDQlMoaGh165dcw7+aK19+5di+QThwT1D9ZGK1/UrjuNUKpXFYvFjDR4xDMPzfICva5VKxbKs1Wr1dyHuSD8f044/RuoLarVaFEX3q/unyuOi49dn0U8T0cOm8n1pv9Lr9RaLJcCXpMFgqK+vV/LVbTAYmjbiFCIAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUImCqxAZhlF+dgzDKDzfNpBXGLDVMgx+9RvopvwuiJb9j9+LpCDA9Hq9krOTVolGo1Gr1UrOt7V4npcvGQ3jeo21dBW7skX9hrQkeT7QtzGWZf27oDxiWZZhmAAvUlrdLBvQJ3Wkfa77JckQIn/DxXGcwrsgnue1Wm3gv+1Tq9XSvRMKsNvtzbYH+s6FEFJfX6/k7DiOCwoKMpvNAX5vUGhoqHzJmK1mlwkEQfDvU+A4jmXZAF+MFN0HFuBFdpj7wFxyQxAEhXdBVNwHptForFarkveBBQcHN20M6LdLAAAALaHgCAygg5FOCXo/vTSxl6drRFF0OByepwOgHwIMQFEsy7bt41XvPwOz2WwBfgIKoF0gwAD8oKSkpK6uzhc9Dx8+3O/XhgEoAwEG4AeCILR0YRUAeAkXcQAAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlfCLzACuWJbleV+9NBiG8VHPAJ0NAgzAFcuyLMtWVFT4onOtVhsREeGLngE6GwQYQPO+//57X3QbHR2NAANoF/gMDAAAqIQAAwAAKiHAAACASj75DEwQhJkzZ65ZsyY0NFRqWbBgQXl5uXT91fjx4x9//HFCSHFx8aZNm8xms9FozMrKUqvVvigGAAA6pPYPsL179+7fv//SpUvOFlEUL168+N5778kjymQyrVu3bvny5TExMXl5eXv37k1PT2/3YgAAoKNq/1OIffr0ycjIUKlUzpbKykpCyNq1a7OysgoKCurq6gghJSUlsbGxsbGxHMdNnDjx0KFD7V4JAAB0YO1/BJacnEwI4TjO2VJbW5uQkJCZmRkeHr5hw4aNGzdmZ2dXVlZGRkZKE0RFRVVVVTmnb2xs/Pbbb6XH3bt3d56HVAbLsoQQ393H2l5YlpW/S+AF14JZhpGei79IZ4z9W4NHDMMw/l5Q7c4vz4iW1U1aWSTDMPIXmgKkl3aAL0lCCMdxii0ZURSbbVdiNx0fH79s2TLp8fTp0+fOnSuKoktBDofD+fjKlStPPfWU9PiRRx6ZM2eOAkW60Ov1ys+0tcLCwpyP9ZzNZSzH84HwsWIg1OARFUV6j+M4+TtIkGMYxv3qZgiR75t4npe/0JQRFBSk8BzbQKvVarVaZebV2NjYbLsSAXbu3DlRFPv160cI4XleemkZDIbjx49LE1RVVRkMBuf0Xbt23bZtm/NxTU2NAkU6sSwbGhpaX19vt9uVnG9r6fX6hoYG52C9pd5lAsFub2mtK0P6Qib/1uANnudd1rVPv0pKAXa7Xf6OUBnSEYPy820VlUoliqL7l7bLW3273a7wLkir1Vqt1gBfkuHh4SaTSbFXtyiKzb7tUOJVWldX98orr6xevTo8PHzXrl2pqakMw6SkpPzlL3+5ePFidHR0YWHhsGHDnNOrVKrExETnoPzsogKkfBUEIcADzOV1KAiCywQOUfTva0A6XRPgr0OGYcQmCyrwz954pPxip2J1S+d+WlWkx8Brdw6HQxCEpq/oQONwOPy+k1QiwG666aa0tLRFixaJopiUlJSVlUUI0el02dnZ+fn5drs9MTFx8uTJClQCAAAdhq8C7P3333c+ZhgmLS0tLS3NZRqj0Wg0Gn1UAAAAdGzUnyoBAIDOCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJV4fxcA0BYsy3Ic115dqVQqeQvDMO3SMwD4FAIMqCSll8lk8kXnWq2WZXFyAiDQIcCAVvX19d98840veh44cGC3bt180TMAtCMEGECHwjAMz/PtdX61KUEQ7Ha7jzoHaBUEGEBHc+XKlbq6Ol/0HB0dzfPYaUCgwLYI0NFUV1f/9NNPvug5LCwsJCTEFz0DtAE+qQYAACohwAAAgEoIMAAAoBICDAAAqETBRRwKf2gsfQuDTqdzOBxKzre1eJ6XLxkdZ3OZgOM4ly+YUJi0JH1UA2409pemX1wikVZ3gK8XlmVFUXS/TTKEiLJBjuMU3gVJd0GIouh5Ur/SaDSK7WFaunODggCrr69XcnYcx6nVarPZbLO5RkJACQkJkS8Zs9XsMoEgCP59ChzHsSxrt9t98VL0bzZ3ZqIoNrtdSXeeCYKgeEWtwDBMS/U7uWysgiAovAvS6/UWiyXAl2RQUJDVarVYLIrNUa/XN22kIMAUficizU4UxcB/BySvMGCrDdjCoG064QpVfhdEy/7H70UG9PE+AABASxBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABU4n3RqSAIM2fOXLNmTWhoqNRSXFy8adMms9lsNBqzsrLUanVLjdBhsCxLCOF5n2xjUucA0Jm1/15g7969ixYtunTpkrPFZDKtW7duwYIFGzdurKmp2bt3b0uN0JGwLGu3222+4e8n10kxDMMwDNscN6O8xzCMv58i0KT93x336dMnIyMjPz/f2VJSUhIbGxsbG0sImThx4rvvvpuent5sY7sXA/5VUVFx5swZX/Scmprqi27BPb1ez7Ks706WiKJotVp91Dl0PO0fYMnJyYQQjuOcLZWVlZGRkdLjqKioqqqqlhol1dXV8+bNkx5PmjRp6tSp7V6kG9J7wODgYFEUlZxva3EcFx4e7hwMbhDIL7+ZgOd4/56VxbvpDunKlStnz571Rc+9e/eOjo726UbLsqwoiu5nwRAif+XzPC9/oSlAeosQ4PsfQohOp9NoNMrMq6WTLj75fMKFy5pwOBwtNUpUKlViYqL0uFu3bna73fc1/ophGI7jBEGQlxSApBN0zkFBEFwmEIkY+K8BoI7NZqurq/NFz42NjaTJnqF9SZ23ahaiKCq8C+J5XhCEAH/xSkU23e34SEszUiLADAbD8ePHpcdVVVUGg6GlRkloaOjixYudg/KDMwVwHBcUFGQ2mwP8g5bQ0ND6+nrnoNlqdplAEAT/PgVcZwGtJYqiTzda6cjG/SxcckMQBPkLTQF6vd5isSiWDW2j0WisVqvFYlFsjsHBwU0bPexixo4d67IcL126NGXKlFbNOCUlpays7OLFi6IoFhYWDhs2rKVGAAAALzV/BHb48OHDhw8TQvbt25efny//QOvMmTOHDh1q1Tx0Ol12dnZ+fr7dbk9MTJw8eXJLjQAAAF5qPsDOnDnzySefSI8LCwvln8azLLt27VqP/b7//vvyQaPRaDQaXaZpthEAAMAbzQfYjBkzZsyYQQgZNGjQvn37fHQvKgAAQJt5SKZjx46ZzeYff/zRpT0uLs5XFQEAAHjBQ4Bt3779oYceanprYYBf4gkAAB2eh6sQc3Jy7rnnnu+//77yt5QpDgAAoCUejsCuXr26dOnSAQMGKFMNAACAlzwcgY0ePbq0tFSZUgAAALzn4Qjs6aefnjlz5okTJ1JSUrRarbN97NixPi4MAADAHQ8BNnr0aEKI/KvlJUp+gwgAAEBTHgIMQQUAAIHJQ4C19EW68u/eBQAAUJ6HAOvWrVuz7bgPDAAA/MtDgJWVlTkfm83moqKiV1555e233/ZxVQAAAB54CDCXr4waOHBgt27d5s6du2/fPl9WBQAA4EGrf3Kwe/fuR48e9UUpAAAA3vNwBHbixAn54NWrV59//vlevXr5siQAAADPPATYwIEDXVoMBgM+AwMAAL/zEGB1dXUuLXq9Xv77lgAAAH7hIcCCg4MdDsehQ4dKS0vtdntycvLIkSMRYAAA4HceAuzy5csTJ04sKSmJiYlhWba8vNxoNO7Zs6el+8MAAACU4eEqxHnz5vE8f+7cuXPnzp05c+bcuXMMw8yfP1+Z4gAAAFri4Qhs3759f//732NiYqTBmJiYF1988YEHHvB9YQAAAO60+j4wAACAQOAhwMaMGZOTk3P+/Hlp8H//939zcnLGjBnj+8IAAADc8RBg69evJ4T069cvLi4uLi4uNjaWEFJQUKBEaQAAAC3z/G30//73v6XL6AkhCQkJI0aMwGX0AADgdx4CrLGxceXKldXV1a+++ioh5NZbbx01atTKlSt1Op0i5QEAADTPwynExYsXv/HGG4MGDZIGs7Ky3n///Weeecb3hQEAALjjIcC2b9/+0ksvzZgxQxqcOXPmW2+9tX37dp/XBQAA4JaHU4gNDQ19+/aVtwQHB1utVl+W5EqlUik5O5ZlCSE872HJ+B3LsvIlwwuuBbMMIz0Xf8FnpdAGPt1opW2yVbNgGEb5XRDP8/598XqD4zjFlowois22e9hNjx49Ojc3929/+1vXrl0JITU1NStWrBg1alS71+eGwlkibTccxyk50zZgGEa+ZJoWzPg7wABaS5mNtrUBpvwuiOd5h8Oh5EzbQKpTmXm1tDQ8zP7VV18dN25cr169kpKSeJ4/efJkz5499+/f74MKW2Q2m5WcHcdxWq3WarXabDYl59taKpVKvmSaHhYLDofdble2qN8I/DcBEGhEUfTpRsuybGtn4XA4FN4FsSxrsVgEQVBypq2l1+ttNpvFYlFsjiEhIU0bPQRYVFRUcXHx3r17T5w4YbPZcnJyJk+erPABNQAAQFOeDwA5jps0adKkSZMUqAYAAMBL+IwEAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBLv7wIAAAghJDg4mGEYjUbju1k4HA7fdQ7KQ4B1ajzPMwzjo8591zN0SDzP22y2s2fP+qLzkJCQnj17+qJn8CMEWOfFMAzP82az2W63+6L/kJAQX3QLHZggCBUVFb7ouVu3bgiwjgcB1tmdPXv28uXLvuh59OjRvugWAECCizgAAIBKCDAAAKCSQqcQFyxYUF5eLn2qP378+Mcff5wQUlxcvGnTJrPZbDQas7Ky1Gq1MsUAAEAHoESAiaJ48eLF9957Tx5RJpNp3bp1y5cvj4mJycvL27t3b3p6ugLFAABAx6DEKcTKykpCyNq1a7OysgoKCurq6gghJSUlsbGxsbGxHMdNnDjx0KFDClQCAAAdhhJHYLW1tQkJCZmZmeHh4Rs2bNi4cWN2dnZlZWVkZKQ0QVRUVFVVlXN6k8n0ySefSI/79+9/ww03KFCkE8uyhBC1Ws1xnJLzbS2WZeW3fKpFc9MJ3D8F3KcFnY20zbfqpe3yQlMAz/NBQUGBf8+1SqVSbF4tLQ0lAiw+Pn7ZsmXS4+nTp8+dO1cURVEU5dPI67t27doLL7wgPX7kkUfmzJmjQJEutFqt8jNtreDgYOdjLbG4jOU4TsktDCDwMQzDMIz0JrXFaQiR75s4jpO/0JRBxSs3KCgoKChImXk1NjY2265EgJ07d04UxX79+hFCeJ6X3v4YDIbjx49LE1RVVRkMBuf00dHRx44dcw7KD84UwHFcREREbW2tzWZTcr6tFRoaeu3aNedgrbXWZQKbzWaxuLoz26YAAA2pSURBVKaaHMMwim1/AIHA4XCIouj+pS3+dtBmsym8C9Lr9RaLRRAEJWfaWgaDob6+3v0ept3n2LRRic/A6urq8vLyrl69Korirl27UlNTGYZJSUkpKyu7ePGiKIqFhYXDhg1ToBIAAOgwlDgCu+mmm9LS0hYtWiSKYlJSUlZWFiFEp9NlZ2fn5+fb7fbExMTJkycrUAkAAHQYSgQYwzBpaWlpaWku7Uaj0Wg0KlAAAAB0PPgmDgAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBKCDAAAKASAgwAAKiEAAMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASkr8oCW0GcMwWq2WYZimoziO0+v1zkENY3WdgGV53t36bbZbAABaIMACmkql0ul0VVVVDofD/ZQmq8mlRXA4bDabm39hWZbjuOstEQDATxBgFMjNzb1y5Yr7abheBt39w+Ut53/8sa72jJt/CQ4Ovu2229qhPgAAf8BnYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJVyFCAAdn3TXo/S3VXePsCwbFBTkcTJRFBsbG9tcHrQNAgwAOr7g4GBCCMMwDMOwrLszTwwhomyQ47iQkBBvZnHt2jVkmMIQYADQ8UnHXseOHfOYMV16MKzsO2rKysrmvfaR+38JDQ197rnn8NU2ykOAAUBnYbFYPB8kiVoiS6LGxsaay5fd/wcOvPwFF3EAAACVKDgCCwsLU3J20nkAvV4viqLHiX3N/cl6AAgcOp1Oo9Fcfz8cx6lUqkDY/7in1Wq9ubylXbT0ta4UBFhtba2Ss+M4LiIioqGhwf034SpDrVaHhob6uwoA8MxkMlmtrj8K0QZ6vd5isQiCcP1d+Y7BYDCbzRaLRbE56nS6po14gw8AAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlRBgAABAJQQYAABQCQEGAABUQoABAACVEGAAAEAlBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQAAlXh/FwAAQDeWZQkhWq1WrVZff288z3McJ4qivNFisdhstuvvvINBgAEAXJeQkBBCSEVFxbVr13zR/4ABA9RqNQKsKQQYAEA72LFjx9GjR33R8/r164ODg33RM+0QYNdLo9Ho9Xp/VwEAHVZYWJhKpdJoND7q32q11tfX+6hzn0KAXS+O48xm865du3zR+c0335yUlOSLngGAFhzHnT59uqSkxBedp6am9ujRwxc9KwAB1g5MJtPu3bt90XNwcDACDADOnDnjo51MTEwMvQGGy+gBAIBKCDAAAKBSpziFqFKpeN7bZyrd0hEUFOTlv3jfMwAAtKNOsfPV6XQMw1itVm8mdjgcDQ0N3nfOcVxb6wIAgLbrFAFGCCkqKnrllVd80fNzzz0XHh7ui54BAMANfwZYcXHxpk2bzGaz0WjMyspql29hAQCATsJvAWYymdatW7d8+fKYmJi8vLy9e/emp6f7qxgAgM5Jo9GwLNuGu6RVKpU3kwmC4LsvwfJbgJWUlMTGxsbGxhJCJk6c+O677yLAAAAU1rt3b47j2vBVVUFBQUFBQR4nE0Wxurq6TaV5xrh857Fidu7ceeHChVmzZhFCLly4sGTJks2bN0ujLl26NGnSJOnxI488MmfOHL9USJey+qo1PxyQt3SLuCEitLu/6gGg1JmfjjocgnMwISRyfvxIP9YDhJDGxsZmP2Py2xGYS3A6HA7n49DQ0MWLF0uP+/fvf/1f0sWyrPfXCjIMI33xs7wk99MzDOPlxG3AsmyznatUKvmBedOD9DvU+juDu7Wt83bBcRzP815e/NlaDMOQJltR2/A8b7fbXTr3yzptCc/zLMs2Njb6ovNWcbPYpftPrmfWCix2nucdDofL6nbxJ4a1kF8DzOFweLMNt+Ni53leEAT5QvbXOnUjKCjIbrcLguBxSofD4c1kHjsJrAAzGAzHjx+XHldVVRkMBuconU537733OgerqqqULIzjOLVabTKZAvzHC0JDQ+vq6pyDJqvJZQKr1SqfQHlBQUEhISH19fX+Osr3BsuyOp0uwL/JNDg4WKVS+XdteiSdTfLR+5X2Eh4ebrfb3a9ul81VEASFl7xer7dYLNe/0/epoKAgi8VisVj8W4bfvokjJSWlrKzs4sWLoigWFhYOGzbMX5UAAACN/HYEptPpsrOz8/Pz7XZ7YmLi5MmT/VUJAADQyJ/3gRmNRqPR6McCAACAXvgyXwAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACo5LfvQgxYNTU127ZtS09P79Onj79raYWrNvPRK+flLQNCIm/QdfFXPYSQ77///rPPPnviiSfwQznX6eDBgxcuXPj973/v70Ko9/e//91gMIwZM8bNNPsul9nFX78Fo4taPziit+9Lo4nD4XjttdfuvPPO5ORk/1bSWX7Q0nvXrl3bsmXL4MGD6QqwCJX2d1EJ/q7iN86ePbtly5Y//OEPCLDr9PXXX3/zzTcIsOu3e/fu/v37uw+wMZHxitVDKUEQtmzZ0rNnT78HGE4hAgAAlRBgAABAJXwG5sput//yyy8Gg8Gb32oDN0wm09WrV7t37y790Aa0WU1NTWNjY2RkpL8Lod7ly5dVKlVERIS/C6GbKIoVFRXh4eF6vd6/lSDAAACASnhrDAAAVOrsVyEWFxdv2rTJbDYbjcasrCyX6+XcjwW5gwcPbt++vb6+vlevXrNmzerRo4d87IIFC8rLy6Xffh0/fvzjjz/upzIp4H5ZYZv0xq5du7Zu3eoctNlsmzdv7tLl17tKsEF6QxCEmTNnrlmzJjQ0VGoJuB2m2Ik1NDQ8+OCDZ8+etdvtK1as2Llzp/djQe78+fPTp0+/ePGizWbbunXr008/LR/rcDgeeOABq9Xqr/Io4n5ZYZtsg6+//nrlypXyFmyQ3tizZ8/ChQsnT55cW1srtQTgDrNTn0IsKSmJjY2NjY3lOG7ixImHDh3yfizIVVVV3XXXXdHR0TzPjxo1qqKiQj62srKSELJ27dqsrKyCggKFf6CdLu6XFbbJ1rJYLFu3bs3KypI3YoP0Rp8+fTIyMlQqlbMlAHeYnTrAKisrnVd2RUVFVVVVeT8W5IxG44wZMwghjY2Nf/vb34YPHy4fW1tbm5CQkJmZ+eqrrxJCNm7c6JciqeB+WWGbbK133313woQJBoNB3ogN0hvJycmDBw/mOM7ZEoA7zE4dYOJvr8B0OBzej4Wmjhw5Mnv27K5duz722GPy9vj4+GXLlkVGRqrV6unTpx89elTEta8tcL+ssE22yk8//VRcXDxhwgSXdmyQbROAO8xOHWAGg8H5NqGqqsrlbZr7sSAniuLrr7/+0UcfLV26NDMzU/6ujRBy7ty5s2fPSo95nncZC3LulxW2yVY5cODA7bff3vQ2RGyQbROAO8xOHWApKSllZWUXL14URbGwsHDYsGFS+88//2y1WlsaC02VlJSUlpbm5eX17v2brz2VlmRdXV1eXt7Vq1dFUdy1a1dqaqp09Rc01dKywjbZWqIoHj58eOjQofJGbJDXIwB3mJ39Rubi4uItW7bY7fbExMQnnnhC+sTynnvueeGFF5KSkpodC01t3br1ww8/5Pn/vytDp9Nt27aN/GdJJiYm7t69++OPPxZFMSkpKSsrS6fT+bXewCWKYrPLCttka5WVleXn57/55pvycMIG2VoZGRlvvvmm/DL6gNphdvYAAwAASnXqU4gAAEAvBBgAAFAJAQYAAFRCgAEAAJUQYAAAQCUEGAAAUAkBBgAAVEKAAQSozZs3Hz58+PqnAeioEGAAAQoBBuAeAgwAAKiEAAPwvx07dtx8881arTYmJmbNmjWEkEGDBn3++efPPPPM2LFjCSGnT5+eNGlSt27dQkJChg8fXlxc3HQagM4G34UI4Gfl5eXx8fHz58+/7777vvjii6effvrQoUNDhw4dM2bMuHHjFi1axHFcQkJC9+7dn332WULI8uXLGxoavvnmG7vdLp/G388DQGm8vwsA6OzOnTsnCEJWVlZ8fPzgwYMTExN79erF8zzDMBzHcRzncDiefPLJtLS02NhYQkhFRcX8+fMJIfJp/P0kAPwAAQbgZ3fcccfkyZNvvvnmCRMmjBkz5sEHH3T+eoWEZdmZM2d+8cUXO3bs+OabbwoLC/H7VQAEn4EB+J1Go/nHP/5x/Phxo9H4zjvv9O7d+x//+Id8gvr6+jvuuGPhwoU2m23WrFkbNmzwV6kAAQUBBuBn+/fvX7x4cb9+/ZYsWfLll1+OGzfur3/9q3yCgwcPlpaWFhUVPfPMM8OGDRMEwV+lAgQUnEIE8DOO4/Ly8sLCwn73u9+VlpYeOXIkKyuLEMKy7JkzZy5duhQaGmoymd5+++3x48cXFRU9++yzDQ0NFy5c6Nmzp3Oa6Ohofz8PAMWJAOBv69ev79u3r1qt7t2799NPP221WkVR3Lx5c9euXdPT0x0OR25ubmRkZJcuXe67777Tp08nJycPHjxYPo2/nwGAH+AyegAAoBI+AwMAACohwAAAgEoIMAAAoBICDAAAqIQAAwAAKiHAAACASggwAACgEgIMAACohAADAAAqIcAAAIBK/welaSFLxAgnjwAAAABJRU5ErkJggg==" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> x_bar) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">1.267</td>
-<td align="right">7.877</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="one-numerical-one-mean---standardized" class="section level3">
-<h3>One numerical (one mean - standardized)</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( t_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(<span class="dt">response =</span> arr_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">2.679</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(<span class="dt">response =</span> arr_delay) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>)
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.9338</td>
-<td align="right">4.362</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.9141</td>
-<td align="right">4.444</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="one-categorical-one-proportion-1" class="section level3">
-<h3>One categorical (one proportion)</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( p_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.466</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">specify</span>(<span class="dt">response =</span> day_hour, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st"> </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;prop&quot;</span>)
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.42</td>
-<td align="right">0.508</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> p_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.4218</td>
-<td align="right">0.5102</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="one-categorical-variable-standardized-proportion-z-1" class="section level3">
-<h3>One categorical variable (standardized proportion <span class="math inline">\(z\)</span>)</h3>
-<p>Not yet implemented.</p>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-diff-in-means-1" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (diff in means)</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.7452</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in means&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-7.167</td>
-<td align="right">6.079</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-7.296</td>
-<td align="right">5.806</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels-t-1" class="section level3">
-<h3>One numerical variable, one categorical (2 levels) (t)</h3>
-<p><strong>Standardized point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( t_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.2182</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>season) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-2.236</td>
-<td align="right">1.718</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> t_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-2.183</td>
-<td align="right">1.746</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="two-categorical-variables-diff-in-proportions" class="section level3">
-<h3>Two categorical variables (diff in proportions)</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( d_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0205</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;diff in props&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.0648</td>
-<td align="right">0.1083</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> d_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-0.0676</td>
-<td align="right">0.1087</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="two-categorical-variables-z" class="section level3">
-<h3>Two categorical variables (z)</h3>
-<p><strong>Standardized point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( z_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>)) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.4605</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(day_hour <span class="op">~</span><span class="st"> </span>season, <span class="dt">success =</span> <span class="st">&quot;morning&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;z&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;summer&quot;</span>, <span class="st">&quot;winter&quot;</span>))
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-1.479</td>
-<td align="right">2.501</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> z_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">-1.522</td>
-<td align="right">2.443</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="two-numerical-vars---slr-1" class="section level3">
-<h3>Two numerical vars - SLR</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( slope_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">1.017</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;slope&quot;</span>)
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.9728</td>
-<td align="right">1.074</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, <span class="dt">point_estimate =</span> slope_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.9653</td>
-<td align="right">1.069</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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aUPlsJxXHh4eG1tbVNTk3JLkQjZ/cOXtlN0mrCIsH7ta02lUll/2YQJGo3GYrG07ze3/EIQBFEU2/TTKv6lVqslSfLXqPjxl0LbK6sODoqIanazhtFoNJTSxsZGRQvzIoPBIAhCTU2NvwvxlPy7iW362SP/CgoK0uv1jqv9iIgIx5mxCxEAAJiEAAMAACZ5fxfiJ598sm/fvrq6OvkHmeSf7ysoKNi1a1dDQ4PRaMzKylKr1a1NBAAA8ISXt8B++OGHnTt3rl27dteuXfHx8Xl5eYQQk8m0devW5cuX79y5s6am5vDhw61NBAAA8JCXt8AqKyvvuuuu6OhoQsj48eOPHDlCCCksLIyNjY2NjSWETJs2bc+ePenp6U4nyo1YLJby8nL5dnBwMM/z3i3SKUopIYTjOEUX5+THQ+m/Ft0OlFKO4xj6SVL6b/4upG3YKjhwephS6vbdxHGcJ7MFDrl7GSqYuR7mOI4QYldwa2s5LweY0Wg0Go2EkObm5v/5n/8ZM2YMIaSioiIyMlKeISoqSj69xOlEWUVFhTXM5syZs3jxYu8W6YJer9fr9cq1LxFCSn81heM4jUbT7gaZ2+/K8zxD7yVGdWREeZFKperWrZsnc+p0OqWL8S4Pn1fg0Gq1/i6hbex6uLm52elsipxGf+rUqTfeeGPUqFFz584lDuEpn5TsdKIsPDx8+/bt8u2bbrrp+vXrShRph1IaEhJiMpkUPQXZ8VOEJIqtvTZuCYLA0CnphBC1Wm2xWCwWi78L8RTP86IoMrSNKwgCISRARkVLS4vbN69KpaKUtvst4HtBQUE8z9fV1fm7EE/xPK9SqRj6ooJGo9FqtXYjR5Ikpx/WvRxgkiTt2LGjtLR07dq1vXv3lidGREScO3dOvl1ZWSmfzu90okytVo8cOdJ612ffAyOEWCwWXweYRNr9NSNJkhj6ihIhRJIktmqW99AyVDAJpFHhyTfS5B1cDH2dUaPRcBzHUMGiKPI8z1DB8ocwDwv28kkchYWFRUVFOTk51vQihCQlJRUXF5eVlUmSlJ+fn5yc3NpEAAAAD3l5C+z8+fNXrlzJyMiQ7+p0urffflun02VnZ+fm5prN5sGDB6empsoPOU6EzkT+cG07xbsHwNnauQcAXuflAJs9e/bs2bMdp1tP7nA7EToN+fCG3UQvnsQhduDwIQB0AgxcCxHYdfXq1ZKSEvfztV18fLzTa6MBQNeBAAMFiaKo0Blx2HkIAAgwYJJWq+U4Trmvwfnxmu4A4CEEGDBJo9G0tLRUV1cr0bhOpzMYDAgwgACHAANWmUym8+fPK9Fyr169Bg4cqETLAOBF+DkVAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASAgwAAJiEAAMAACYhwAAAgEkIMAAAYBICDAAAmIQAAwAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASfpEZwB7HcYQQnucppRzHUUq9274kSaIoerdNgC4IAQZgLyQkhBCiUqmUW0RzczMyDKCDGAiw4OBgHyxF/pSt1WrVarVyS5EcpnAcbfeKkuM4RVeyHeT1DRefkSs/efKkEo0bDIZhw4YJgiBJjsOhoyillLZ/RHmXIAhu37zyBm6AFOwJQRA4jvPNSskrKKU8z8s7FZjA8zxxWO2bzWanMzMQYLW1tT5YCsdx4eHhjY2NTU1Nyi3FcY0lilJLS0v7WlOpVO3+Xx9g6D3jVGNjoxLNyitrs9msxBaYWq2WpPaPKO8ym81u37wajYZSqlBXK8FgMAiC4JuVklfwPK/Vauvr6/1diKeCgoKc9rBer3ecme1VDAAAdFkIMAAAYBICDAAAmIQAAwAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASAgwAAJiEAAMAACYhwAAAgEkIMAAAYBICDAAAmIQAAwAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASAgwAAJiEAAMAACYhwAAAgEmCEo1aLJb58+dv3rw5JCREnrJ8+fLS0lJKKSFkypQpjz32GCGkoKBg165dDQ0NRqMxKytLrVYrUQwAAHRK3g+ww4cPHzt2rLy83DpFkqSysrK9e/faRpTJZNq6deuGDRtiYmJycnIOHz6cnp7u9WIAAKCz8n6A9enTJyMjIzc31zqloqKCELJly5bS0tKEhITMzMzg4ODCwsLY2NjY2FhCyLRp0/bs2WMNMIvFYs2/4OBgnue9XqQjeeuQ4zhFFyc5WfC/Ft0OlFKO4yTJSasQ4Cil7X7d/dVyW1FK3b6bOI7zZLbAIXcvQwUz18McxxFC7ApubS3n/QBLTEy0W/z169fj4+MzMzPDwsK2b9++c+fO7OzsioqKyMhIeYaoqKjKykrr/BUVFdYwmzNnzuLFi71eZGv0er1er1eufYkQUvqrKRzHaTSadjeI/a6MUqlUyjXekRHlRSqVqlu3bp7MqdPplC7Guzx8XoFDq9X6u4S2sevh5uZmp7MpcgzMTlxc3Lp16+Tbs2bNWrJkiSRJdokqiqL1dlhYmHUDrm/fvrW1tT4oklJqMBgaGhrMZrNyS3H8FCGJUktLS/ta43neYrF0sCTlCIIvRhejzGazEpvOgiBIkhQgo8JsNrt98wqCQClt91vA97RaLc/z9fX1/i7EUxzHqVSqpqYmfxfiKbVardFo7EaOKIpOP6z7YhVTUlIiSVL//v0JIYIgyBtnERER586dk2eorKyMiIiwzq/VaidPnmy9a7txphx5u9VsNiv6SjsJsA6sbjiOC5BVlVMIMBdEUbT90OYtPM8HToCJoujJu4lSytDqVaVSsVUwz/McxzFUsLxTysOCfXEafW1tbU5OTnV1tSRJBw8eHD16NKU0KSmpuLi4rKxMkqT8/Pzk5GQfVAIAAJ2GLz4jDxs2LC0tbdWqVZIkJSQkZGVlEUJ0Ol12dnZubq7ZbB48eHBqaqoPKgEAgE5DqQB79913rbcppWlpaWlpaXbzGI1Go9GoUAEAANC54UocAADAJAQYAAAwCQEGAABMQoABAACTEGAAAMAkBBgAADAJ10ro0pi7QhoAgBUCrOuSr1n+ww8/3LhxQ4n25cs6AwAoBAHW1d24cePatWtKtIwAAwBF4RgYAAAwCQEGAABMQoABAACTEGAAAMAkBBgAADAJAQYAAExCgAEAAJMQYAAAwCQEGAAAMAkBBgAATEKAAQAAkxBgAADAJAQYAAAwCQEGAABMQoABAACTGPg9sNDQUJ8tS6fTKfojxZLDFI6jarW6fa1R2v7/Bf8SBEXeehzHSZIUIKNCEAS3b16O4wghGo3GJxV5Ac/zlFJfrpQ6iFLKcZxC400J8pCw6+GWlhanMzPwrK5fv+6DpXAcFx4ebjKZmpqalFuKY4CJotTc3Ny+1lQqVWuvqycopQytODoZs9ksiqLXm1Wr1ZIkdWRUeJHZbHb75tVoNJTSxsZG35TUcQaDQRAE36yUvILnea1WW19f7+9CPBUUFKTX6x17WKfTOc6MXYgAAMAkBBgAADAJAQYAAExCgAEAAJMQYAAAwCQEGAAAMAkBBgAATEKAAQAAkxBgAADAJAQYAAAwCQEGAABMQoABAACTEGAAAMAkBBgAADDJTYBNnjzZYrHYTikvL58xY4aSJQEAALjn/PfATpw4ceLECULI0aNHc3NzeZ63PnT58uXjx4/7qDoAAIBWOA+wy5cvf/TRR/Lt/Px8Sqn1IY7jtmzZ4ovSAAAAWuc8wObOnTt37lxCyPDhw48ePcrQz1EDAEAX4SaZzp4929DQ8P3339tNHzBggFIVAQAAeMBNgO3bt+/hhx9uamqymy5JkmIlAQAAuOfmLMSVK1fec88933zzTcWv+aY4AACA1rjZAquurl67du2gQYN8Uw0AAICH3GyBTZgwoaioyDelAAAAeM7NFtiTTz45f/788+fPJyUlBQUFWadPnjxZ4cIAAABccRNgEyZMIITk5ubaTW9sbFSqIgAAAA+4CbD2BZXFYpk/f/7mzZtDQkLkKQUFBbt27WpoaDAajVlZWWq1urWJAAAAnnBzDKyyFS7+5fDhw6tWrSovL7dOMZlMW7duXb58+c6dO2tqag4fPtzaRAAAAA+52QLr0aOH0+kuvgfWp0+fjIwM272OhYWFsbGxsbGxhJBp06bt2bMnPT3d6UR5flEU6+rq5Nsajcb2QlbKsS7FN4vzFkopvpMHtgJqALsthv6bb+rxCrYKZrGHicfD2E2AFRcXW283NDScOXPm5Zdf3r17t4t/SUxMJITYXv+3oqIiMjJSvh0VFSVvwDmdKLt27dr06dPl23PmzFm8eLEnz8QrgoODFW1fIoRc+dUUjue0Wm27G7TtZ2CIojvMOzKivEitVnfv3t2TOfV6vdLFeJeHzytwBMiQ8JxdDzc3NzudzU2A2V0yaujQoT169FiyZMnRo0c9L8VuE0EUxdYmysLCwqwbcH379q2trfV8We1GKTUYDA0NDWazWbmlOG4rSaLU0tLSvtZ4nrf7sZu2UqlUHfl3aDez2azEprMgCJIkdXBUeIvZbHb75hUEgVLa7reA72m1Wp7n6+vr/V2IpziOU6lUjldTClhqtVqj0diNHFEUnX7ma/NVem+66abTp0+36V8iIiLOnTsn366srIyIiGhtokyr1dqepu/6kJu3cBxHCDGbzYq+0k4CrAOrG47jOrKqYm6vQucgbzRzHKfQvl95r7LtJ0J/EUXRk3cTpZSh1atKpWKrYJ7nOY5jqGCO4zQajYcFuwmw8+fP296trq5+5plnevXq1aaCkpKSXn311bKysujo6Pz8/OTk5NYmAnR68s6curo6hbb1w8PDOY4LhAADUJqbABs6dKjdlIiICNfHwBzpdLrs7Ozc3Fyz2Tx48ODU1NTWJgJ0Ed9+++2NGzeUaFn+7iZAV+AmwBx3Yev1ek92Pb377ru2d41Go9FotJvH6UQAAABPuAkwg8EgiuLx48eLiorMZnNiYuK4ceNw7AQAAPzOTYBdu3Zt2rRphYWFMTExHMeVlpYajcZDhw619v0wAAAA33BzJY6lS5cKglBSUlJSUnL58uWSkhJK6bJly3xTHAAAQGvcbIEdPXr0f//3f2NiYuS7MTExzz333IMPPqh8YQAAAK642QIDAAAITG4CbNKkSStXrrxy5V+XP/rhhx9Wrlw5adIk5QsDAABwxU2Avfjii4SQ/v37DxgwYMCAAfK1d/Py8nxRGgAAQOvcX43+73//u3waPSEkPj5+7NixOI0eAAD8zk2ANTc3P/vss1VVVdu2bSOE3HbbbePHj3/22Wd1Op1PygMAAHDOzS7E1atX79ixY/jw4fLdrKysd99996mnnlK+MAAAAFfcBNi+ffteeOGFuXPnynfnz5//+uuv79u3T/G6AAAAXHITYPX19f369bOdYjAYGLoyPwAAdFZuAmzChAnr16+vqqqS79bU1GzcuHH8+PGK1wUAAOCSm5M4tm3blpKS0qtXr4SEBEEQLly4cPPNNx87dsw3xQEAALTGTYBFRUUVFBQcPnz4/PnzLS0tK1euTE1Nxe/QAwCA37kJMEIIz/PTp0+fPn26D6oBAADwEK6FCAAATEKAAQAAkxBgAADAJAQYAAAwCQEGAABMQoABAACTEGAAAMAkBBgAADDJ/ReZ/S40NNRny9LpdFqtVrn2JYcpHEfVanX7WqO0/f8LnRjP8xzn/8+mgiC4ffPKdWo0Gp9U5AU8z1NKfblS6iBKKcdxgsDAql4mDwm7Hm5paXE6MwPP6vr16z5YCsdx4eHhJpNJ0WvtOwaYKErNzc3ta02lUrX2unqCUsrQigM8Z7FYzGazv6sgZrPZ7ZtXo9FQShsbG31TUscZDAZBEHyzUvIKnue1Wm19fb2/C/FUUFCQXq937GGnv6Ls/49pAAAA7YAAAwAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASAgwAAJiEAAMAACYhwAAAgNZ+jBcAAA+sSURBVEkIMAAAYBICDAAAmMTA1egBoE0U/fkMURRFUVSocYA2QYABdCqUUvknoBRq32KxIMAgQCDAADqb4uLiH3/8UYmWjUZjcHCwEi0DtAOOgQEAAJMQYAAAwCTsQgxolFIXR+M5jlOr1ZIktbvx9tYFAOB/CLCARinleb6urq7dKeUCx3F6vd7rzQIA+AYCjAFff/11U1OT15s1GAwjR470erMAAL6BY2AAAMAkBBgAADAJAQYAAExCgAEAAJN8dBLH8uXLS0tL5fO2p0yZ8thjjxFCCgoKdu3a1dDQYDQas7Ky1Gq1b4oBAIBOwBcBJklSWVnZ3r17bSPKZDJt3bp1w4YNMTExOTk5hw8fTk9P90ExAADQOfgiwCoqKgghW7ZsKS0tTUhIyMzMDA4OLiwsjI2NjY2NJYRMmzZtz5491gATRbGurk6+rdFofPN9W+tSAurrvQFVDABp45h0OzP9t44V5VNsFcxiDxOPh5kvAuz69evx8fGZmZlhYWHbt2/fuXNndnZ2RUVFZGSkPENUVFRlZaV1/mvXrk2fPl2+PWfOnMWLF/ugSJnSFyqVCCFXfjWF4zmtVqvoQgG8iOM8HbFqtbp79+6ezMncF+o9fF6Bg7mVjF0PNzc3O53NFwEWFxe3bt06+fasWbOWLFkiSZLdpSVsf6AhJCRk9erV8u2BAwdat8YURSnV6/WNjY1ms1m5pTheTkMSpZaWltbm5ziO53nl6gFoK1EULRaLJ3OazWa3b15BECilLt4CgUaj0fA8bzKZ/F2IpziOU6lUSlwJQSEqlUqj0diNHFEUnZ4k4YsAKykpkSSpf//+hBBBEOQ1ckRExLlz5+QZKisrIyIirPPrdLp7773Xetd240w58nWVWlpaFH2lnQSYJLlYHUiShACDgOJ6xNoSRbGxsdH1PPIxArezBQ45cRkqmOd5tgqmlGo0Gg8L9sVp9LW1tTk5OdXV1ZIkHTx4cPTo0ZTSpKSk4uLisrIySZLy8/OTk5N9UAkAAHQavtgCGzZsWFpa2qpVqyRJSkhIyMrKIoTodLrs7Ozc3Fyz2Tx48ODU1FQfVAIAAJ2GLwKMUpqWlpaWlmY33Wg0Go1GHxQAAACdD67EAQAATEKAAQAAkxBgAADAJAQYAAAwCQEGAABMQoABAACTEGAAAMAkBBgAADAJAQYAAExCgAEAAJMQYAAAwCQEGAAAMAkBBgAATEKAAQAAk3zxcyoA0Dmo1Wr5J+o9mVkQBIPB4Hoe+QfHBeFfKyKz2czQbweD3yHAAMBTarXaRcZIv77b2NhYVlbmeePh4eHBwcEIMPAcAgwA2uCf//znhQsXnD7Uq3+Q7d2ioqI1B97wvOVZs2ZNmzatQ8VBF4NjYAAAwCQEGAAAMAkBBgAATEKAAQAAkxBgAADAJAQYAAAwCQEGAABMYuB7YMHBwT5YCqWUEKLVatVqtXJLkRymcBx1cV0DuSqALoJS6pv3e7sJgsBxXIAXaYtSyvM8xzGzrSJfnMWuh81ms9OZGQiwuro6HyyF4zi1Wt3Y2Njc3KzcUhwDTBSllpaW1uZna+QBdJAkSb55v7ebXq9XqVQBXqQtnuc1Go3JZPJ3IZ7SarWCIDj2sF6vd5yZgQCTJMfVvoJLUXRxbW3aN88dIHAE/piXJCnwi7SSS2WoYJmHBePTPQAAMAkBBgAATEKAAQAAkxg4BhbggoKCnB5ddEoihPzwqykcz2m1Wq9XBQDQ6SHAOorjuLq6ur1793oys0QIMYbaTqmrrf2m6pvW5o+MjAwPD+9ghQAAnRICzAsaGxuPHTvm0ayUBhvvsfvfyvIbrc0eFBSEAAMAcArHwAAAgEkIMAAAYBICDAAAmIQAAwAAJiHAAACASQgwAABgEk6jB4CAEBQURClV7nv9kiQ1NTUp1Dj4BQIMAAJC3759KaUGg0G5RbS0tIiiqFz74GMIMAAIFJWVlUuWLFGi5REjRixbtkyJlsGPcAwMAACYhAADAAAmIcAAAIBJOAYGAJ2fIAiEEI1G4+Fv1beG53mOc/4TSE1NTR1sHNoKAQYAnV9MTAwhxPOf7nPN6amSoig2Nzd7pX3wEAIMADo/SikhZOHChdXV1V5vPCwsbPv27fIiwJdwDAwAAJiEAAMAACYhwAAAgEn+PAZWUFCwa9euhoYGo9GYlZWlVqsVWpAgCPI5SC7I+69VKlVbd2TzPN/+ygAAoL38FmAmk2nr1q0bNmyIiYnJyck5fPhwenq6QsvS6/UqlcqTOZW7kCgAdG48z7v9oNw+HMdJktS+c/Q5juM4zkVhlFJKqXKXiBRFUbnG/RZghYWFsbGxsbGxhJBp06bt2bNHuQAjhJw6derll19WouXf/e53YWFhSrQMAEzQ6XTyX/lGANJoNP5atCRJVVVVCjVO/fXNuwMHDvz0008LFy4khPz0009r1qx588035YfKy8unT58u354zZ87ixYv9UqESJELmFeyznWLQhffsMchf9QB4UfGVU7Zrk1tCey7of6ffqoFOpLm52ekxJr9tgdkFp+02ZkhIyOrVq+XbAwcOrKur6+CyOI7z5EiVRqNpx68ttGkD3PHDQn9Bk2no4eJfOI5rrXGVStXS0uJRlW1vvOMcG1er1RaLxWKxdLxx+VClQh+/rK8pz/OiKHp9Kcp1u3wQV7mv07ru9id+fVcURbe/v8VxHKVUHhJK78vySrcLgsBxnGMP+/it5DlKKc/zZrPZxQxEsbcSIUQUxTa95VUqlUajsVvti6IYWAEWERFx7tw5+XZlZWVERIT1IZ1Od++991rvVlZW+qAejuM0Gk1jY6OiP3nnOEbMZnNtbW37WgsODq6rq2Po6jXh4eHNzc0mk8nfhXhKp9M1Nze7ePMHmtDQUFEU2z2ivMuTsa3RaCiljY2Nvimp4wwGgyAIAdLDnuB5XqvV1tfX+7sQT1FK5VWxJzP77TT6pKSk4uLisrIySZLy8/OTk5P9VQkAALDIb1tgOp0uOzs7NzfXbDYPHjw4NTXVX5UAAACL/Pk9MKPRaDQa/VgAAACwC1fiAAAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJfrsWYqBpaGh47bXXpkyZEhcXp+iCjvzyje3dSE3wbWE3K7rEwPHaa68NGzZs5MiR/i6k03r//ff1ev2UKVP8svSuMLaPHTtWXl4+a9YsfxfSaX311VcnT55csGCBJzP783tgAaWxsfGtt94aNGiQ0gH2m6h4RdsPZHv37hVFEQGmnI8++qhHjx7+CrCuMLb//ve//+Mf/0CAKefixYt/+tOfPAww7EIEAAAmIcAAAIBJOAb2L6IolpWVdevWLWB/kq4TKCsr0+v1ISEh/i6k06qoqOB5Pjw83N+FdFo1NTXNzc2RkZH+LqTTqquru3HjRs+ePT2ZGQEGAABMwi5EAABgUtc6C7GgoGDXrl0NDQ1GozErK8vuJz4PHjz4wQcfcBw3ZMiQxx9/XKPREEKWL19eWloq/2jplClTHnvsMf+Uzg6LxTJ//vzNmzc77ip02v+uXxRw1NYexhhuKxc97PRRjOG2amsPtzqGpS6jvr7+oYce+u6778xm88aNGw8cOGD76Jdffrlo0aJffvnFYrHs2LFj9+7dkiSJovjggw82NTX5qWT2HDp0aMWKFampqdevX7d7yGn/u35RwFFbexhjuK1c9LDTRzGG26qtPexiDHehXYiFhYWxsbGxsbE8z0+bNu348eO2j166dGnIkCGRkZEcx02cOPHkyZOEkIqKCkLIli1bsrKy8vLyGPodcX/p06dPRkaGSqVyfMhp/7t+UcBRW3sYY7itXPSw00cxhtuqrT3sYgx3oQCrqKiwnjsUFRVVWVlp+2j//v2/+OKLH3/88fr164cPH66qqiKEXL9+PT4+PjMzc9u2bYSQnTt3+r5stiQmJo4YMYLneceHnPa/6xcFHLW1hzGG28pFDzt9FGO4rdrawy7GcBc6Bib9+nxLURRt744YMeLq1avPPPOMSqW64447tFotISQuLm7dunXyDLNmzVqyZIkkSfJ+WGgrp/3v+kWBNnHamRjDSsMYVpqLMdyFtsAiIiKsH44qKysjIiJsH21paUlJSXn11Ve3bds2cODAqKgoQkhJScl3330nzyAIQmsfGcATTvvf9YsCbeK0MzGGlYYxrDQXY7gLBVhSUlJxcXFZWZkkSfn5+cnJyfL0q1evNjU1Xb16dd68edXV1c3NzX/+859TUlIIIbW1tTk5OdXV1ZIkHTx4cPTo0fjo2g5yDzvt/9ZeFGgTFz2MMewVcg87fQhj2Ctc9LCLMdyFdiHqdLrs7Ozc3Fyz2Tx48ODU1FR5+qJFi37/+98nJCTMnDlz6dKler1+woQJEydOJIQMGzYsLS1t1apVkiQlJCRkZWX59RmwytrDjv3f2osCbeKihzGGvcLaw44PYQx7hYsedjGGcSUOAABgUhfahQgAAJ0JAgwAAJiEAAMAACYhwAAAgEkIMAAAYBICDAAAmIQAAwAAJiHAAALUm2++eeLEiY7PA9BZIcAAAhQCDMA1BBgAADAJAQbgf/v377/llluCgoJiYmI2b95MCBk+fPinn3761FNPTZ48mRBy6dKl6dOn9+jRIzg4eMyYMQUFBY7zAHQ1uBYigJ+VlpbGxcUtW7bs/vvv/+yzz5588snjx4/ffvvtkyZNSklJWbVqFc/z8fHxN91009NPP00I2bBhQ319/Zdffmk2m23n8ffzAPC1LnQ1eoDAVFJSYrFYsrKy4uLiRowYMXjw4F69egmCQCnleZ7neVEU582bl5aWFhsbSwj5+eefly1bRgixncffTwLADxBgAH525513pqam3nLLLVOnTp00adJDDz0UEhJiOwPHcfPnz//ss8/279//5Zdf5ufn4ze9AAiOgQH4nVar/ctf/nLu3Dmj0fjOO+/07t37L3/5i+0MdXV1d95554oVK1paWhYuXLh9+3Z/lQoQUBBgAH527Nix1atX9+/ff82aNZ9//nlKSsobb7xhO8Mnn3xSVFR05syZp556Kjk52WKx+KtUgICCXYgAfsbzfE5OTmho6G9+85uioqJTp07JvznLcdzly5fLy8tDQkJMJtPu3bunTJly5syZp59+ur6+/qeffrr55put80RHR/v7eQD4nAQA/vbiiy/269dPrVb37t37ySefbGpqkiTpzTff7N69e3p6uiiK69evj4yMDA8Pv//++y9dupSYmDhixAjbefz9DAD8AKfRAwAAk3AMDAAAmIQAAwAAJiHAAACASQgwAABgEgIMAACYhAADAAAmIcAAAIBJCDAAAGASAgwAAJiEAAMAACb9PziGqIcOhWhiAAAAAElFTkSuQmCC" /><!-- --></p>
-</div>
-<div id="two-numerical-vars---correlation-1" class="section level3">
-<h3>Two numerical vars - correlation</h3>
-<p><strong>Point estimate</strong></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( correlation_hat &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.8943</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boot &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>dep_delay) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">   </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;bootstrap&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">   </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;correlation&quot;</span>)
-( percentile_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">2.5%</th>
-<th align="right">97.5%</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.8502</td>
-<td align="right">0.9218</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> percentile_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">( standard_error_ci &lt;-<span class="st"> </span><span class="kw">get_ci</span>(boot, <span class="dt">type =</span> <span class="st">&quot;se&quot;</span>, 
-                            <span class="dt">point_estimate =</span> correlation_hat) )</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">lower</th>
-<th align="right">upper</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.858</td>
-<td align="right">0.9306</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(boot) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_confidence_interval</span>(<span class="dt">endpoints =</span> standard_error_ci)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="two-numerical-vars---t" class="section level3">
-<h3>Two numerical vars - t</h3>
-<p>Not currently implemented since <span class="math inline">\(t\)</span> could refer to standardized slope or standardized correlation.</p>
-</div>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/inst/doc/two_sample_t.R b/inst/doc/two_sample_t.R
deleted file mode 100644
index ea06c5c3..00000000
--- a/inst/doc/two_sample_t.R
+++ /dev/null
@@ -1,74 +0,0 @@
-## ----include=FALSE-------------------------------------------------------
-knitr::opts_chunk$set(fig.width = 8, fig.height = 3) 
-
-## ----message=FALSE, warning=FALSE----------------------------------------
-library(nycflights13)
-library(dplyr)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  sample_n(size = 500) %>% 
-  mutate(half_year = case_when(
-    between(month, 1, 6) ~ "h1",
-    between(month, 7, 12) ~ "h2"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, half_year, 
-         day_hour, origin, carrier)
-
-## ------------------------------------------------------------------------
-obs_t <- fli_small %>%
-  specify(arr_delay ~ half_year) %>%
-  calculate(stat = "t", order = c("h1", "h2"))
-
-## ------------------------------------------------------------------------
-obs_t <- fli_small %>% 
-  t_test(formula = arr_delay ~ half_year, alternative = "two_sided",
-         order = c("h1", "h2")) %>% 
-  dplyr::pull(statistic)
-
-## ------------------------------------------------------------------------
-obs_t <- fli_small %>% 
-  t_stat(formula = arr_delay ~ half_year, order = c("h1", "h2"))
-
-## ------------------------------------------------------------------------
-t_null_perm <- fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("h1", "h2"))
-
-visualize(t_null_perm) +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-t_null_perm %>% 
-  get_p_value(obs_stat = obs_t, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-t_null_theor <- fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  # generate() ## Not used for theoretical
-  calculate(stat = "t", order = c("h1", "h2"))
-
-visualize(t_null_theor, method = "theoretical") +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-visualize(t_null_perm, method = "both") +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-
-## ------------------------------------------------------------------------
-fli_small %>% 
-  t_test(formula = arr_delay ~ half_year,
-         alternative = "two_sided",
-         order = c("h1", "h2")) %>% 
-  dplyr::pull(p_value)
-
diff --git a/inst/doc/two_sample_t.Rmd b/inst/doc/two_sample_t.Rmd
deleted file mode 100755
index e4a2b100..00000000
--- a/inst/doc/two_sample_t.Rmd
+++ /dev/null
@@ -1,138 +0,0 @@
----
-title: "Two sample $t$ test example using `nycflights13` `flights` data"
-author: "Chester Ismay"
-date: "`r Sys.Date()`"
-output: 
-  rmarkdown::html_vignette:
-    df_print: kable
-vignette: |
-  %\VignetteIndexEntry{Two sample t test flights example}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r include=FALSE}
-knitr::opts_chunk$set(fig.width = 8, fig.height = 3) 
-```
-
-**Note**: The `type` argument in `generate()` is automatically filled based on the entries for `specify()` and
-`hypothesize()`. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.
-
-## Data preparation
-
-```{r message=FALSE, warning=FALSE}
-library(nycflights13)
-library(dplyr)
-library(stringr)
-library(infer)
-set.seed(2017)
-fli_small <- flights %>% 
-  sample_n(size = 500) %>% 
-  mutate(half_year = case_when(
-    between(month, 1, 6) ~ "h1",
-    between(month, 7, 12) ~ "h2"
-  )) %>% 
-  mutate(day_hour = case_when(
-    between(hour, 1, 12) ~ "morning",
-    between(hour, 13, 24) ~ "not morning"
-  )) %>% 
-  select(arr_delay, dep_delay, half_year, 
-         day_hour, origin, carrier)
-```
-
-* Two numeric - `arr_delay`, `dep_delay`
-* Two categories 
-    - `half_year` (`"h1"`, `"h2"`), 
-    - `day_hour` (`"morning"`, `"not morning"`)
-* Three categories - `origin` (`"EWR"`, `"JFK"`, `"LGA"`)
-* Sixteen categories - `carrier`
-
-***
-
-# One numerical variable, one categorical (2 levels)
-
-## Calculate observed statistic 
-
-The recommended approach is to use `specify() %>% calculate()`:
-
-```{r}
-obs_t <- fli_small %>%
-  specify(arr_delay ~ half_year) %>%
-  calculate(stat = "t", order = c("h1", "h2"))
-```
-
-The observed $t$ statistic is `r obs_t`.
-
-Or using `t_test` in `infer`
-
-```{r}
-obs_t <- fli_small %>% 
-  t_test(formula = arr_delay ~ half_year, alternative = "two_sided",
-         order = c("h1", "h2")) %>% 
-  dplyr::pull(statistic)
-```
-
-The observed $t$ statistic is `r obs_t`.
-
-Or using another shortcut function in `infer`:
-
-```{r}
-obs_t <- fli_small %>% 
-  t_stat(formula = arr_delay ~ half_year, order = c("h1", "h2"))
-```
-
-The observed $t$ statistic is `r obs_t`.
-
-## Randomization approach to t-statistic
-
-```{r}
-t_null_perm <- fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  generate(reps = 1000, type = "permute") %>%
-  calculate(stat = "t", order = c("h1", "h2"))
-
-visualize(t_null_perm) +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-## Calculate the randomization-based $p$-value
-
-```{r}
-t_null_perm %>% 
-  get_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-
-## Theoretical distribution
-
-```{r }
-t_null_theor <- fli_small %>%
-  # alt: response = arr_delay, explanatory = half_year
-  specify(arr_delay ~ half_year) %>%
-  hypothesize(null = "independence") %>%
-  # generate() ## Not used for theoretical
-  calculate(stat = "t", order = c("h1", "h2"))
-
-visualize(t_null_theor, method = "theoretical") +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-## Overlay appropriate $t$ distribution on top of permuted t-statistics
-
-```{r}
-visualize(t_null_perm, method = "both") +
-  shade_p_value(obs_stat = obs_t, direction = "two_sided")
-```
-
-## Compute theoretical p-value
-
-```{r}
-fli_small %>% 
-  t_test(formula = arr_delay ~ half_year,
-         alternative = "two_sided",
-         order = c("h1", "h2")) %>% 
-  dplyr::pull(p_value)
-```
-
diff --git a/inst/doc/two_sample_t.html b/inst/doc/two_sample_t.html
deleted file mode 100644
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-
-<meta name="author" content="Chester Ismay" />
-
-<meta name="date" content="2018-09-24" />
-
-<title>Two sample t test example using nycflights13 flights data</title>
-
-
-
-<style type="text/css">code{white-space: pre;}</style>
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-<h1 class="title toc-ignore">Two sample <span class="math inline">\(t\)</span> test example using <code>nycflights13</code> <code>flights</code> data</h1>
-<h4 class="author"><em>Chester Ismay</em></h4>
-<h4 class="date"><em>2018-09-24</em></h4>
-
-
-
-<p><strong>Note</strong>: The <code>type</code> argument in <code>generate()</code> is automatically filled based on the entries for <code>specify()</code> and <code>hypothesize()</code>. It can be removed throughout the examples that follow. It is left in to reiterate the type of generation process being performed.</p>
-<div id="data-preparation" class="section level2">
-<h2>Data preparation</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(nycflights13)
-<span class="kw">library</span>(dplyr)
-<span class="kw">library</span>(stringr)
-<span class="kw">library</span>(infer)
-<span class="kw">set.seed</span>(<span class="dv">2017</span>)
-fli_small &lt;-<span class="st"> </span>flights <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">sample_n</span>(<span class="dt">size =</span> <span class="dv">500</span>) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">half_year =</span> <span class="kw">case_when</span>(
-    <span class="kw">between</span>(month, <span class="dv">1</span>, <span class="dv">6</span>) <span class="op">~</span><span class="st"> &quot;h1&quot;</span>,
-    <span class="kw">between</span>(month, <span class="dv">7</span>, <span class="dv">12</span>) <span class="op">~</span><span class="st"> &quot;h2&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">mutate</span>(<span class="dt">day_hour =</span> <span class="kw">case_when</span>(
-    <span class="kw">between</span>(hour, <span class="dv">1</span>, <span class="dv">12</span>) <span class="op">~</span><span class="st"> &quot;morning&quot;</span>,
-    <span class="kw">between</span>(hour, <span class="dv">13</span>, <span class="dv">24</span>) <span class="op">~</span><span class="st"> &quot;not morning&quot;</span>
-  )) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">select</span>(arr_delay, dep_delay, half_year, 
-         day_hour, origin, carrier)</code></pre></div>
-<ul>
-<li>Two numeric - <code>arr_delay</code>, <code>dep_delay</code></li>
-<li>Two categories
-<ul>
-<li><code>half_year</code> (<code>&quot;h1&quot;</code>, <code>&quot;h2&quot;</code>),</li>
-<li><code>day_hour</code> (<code>&quot;morning&quot;</code>, <code>&quot;not morning&quot;</code>)</li>
-</ul></li>
-<li>Three categories - <code>origin</code> (<code>&quot;EWR&quot;</code>, <code>&quot;JFK&quot;</code>, <code>&quot;LGA&quot;</code>)</li>
-<li>Sixteen categories - <code>carrier</code></li>
-</ul>
-<hr />
-</div>
-<div id="one-numerical-variable-one-categorical-2-levels" class="section level1">
-<h1>One numerical variable, one categorical (2 levels)</h1>
-<div id="calculate-observed-statistic" class="section level2">
-<h2>Calculate observed statistic</h2>
-<p>The recommended approach is to use <code>specify() %&gt;% calculate()</code>:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
-<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-The observed <span class="math inline">\(t\)</span> statistic is
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">stat</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.8685</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>.</p>
-<p>Or using <code>t_test</code> in <code>infer</code></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">t_test</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year, <span class="dt">alternative =</span> <span class="st">&quot;two_sided&quot;</span>,
-         <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(statistic)</code></pre></div>
-<p>The observed <span class="math inline">\(t\)</span> statistic is 0.8685.</p>
-<p>Or using another shortcut function in <code>infer</code>:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obs_t &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">t_stat</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
-The observed <span class="math inline">\(t\)</span> statistic is
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">statistic</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.8685</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>.</p>
-</div>
-<div id="randomization-approach-to-t-statistic" class="section level2">
-<h2>Randomization approach to t-statistic</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># alt: response = arr_delay, explanatory = half_year</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">generate</span>(<span class="dt">reps =</span> <span class="dv">1000</span>, <span class="dt">type =</span> <span class="st">&quot;permute&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
-<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_perm) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="calculate-the-randomization-based-p-value" class="section level2">
-<h2>Calculate the randomization-based <span class="math inline">\(p\)</span>-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_perm <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">get_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<div class="kable-table">
-<table>
-<thead>
-<tr class="header">
-<th align="right">p_value</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.43</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="theoretical-distribution" class="section level2">
-<h2>Theoretical distribution</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">t_null_theor &lt;-<span class="st"> </span>fli_small <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># alt: response = arr_delay, explanatory = half_year</span>
-<span class="st">  </span><span class="kw">specify</span>(arr_delay <span class="op">~</span><span class="st"> </span>half_year) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">hypothesize</span>(<span class="dt">null =</span> <span class="st">&quot;independence&quot;</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="co"># generate() ## Not used for theoretical</span>
-<span class="st">  </span><span class="kw">calculate</span>(<span class="dt">stat =</span> <span class="st">&quot;t&quot;</span>, <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>))</code></pre></div>
-<pre><code>## Warning: Removed 15 rows containing missing values.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_theor, <span class="dt">method =</span> <span class="st">&quot;theoretical&quot;</span>) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="overlay-appropriate-t-distribution-on-top-of-permuted-t-statistics" class="section level2">
-<h2>Overlay appropriate <span class="math inline">\(t\)</span> distribution on top of permuted t-statistics</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">visualize</span>(t_null_perm, <span class="dt">method =</span> <span class="st">&quot;both&quot;</span>) <span class="op">+</span>
-<span class="st">  </span><span class="kw">shade_p_value</span>(<span class="dt">obs_stat =</span> obs_t, <span class="dt">direction =</span> <span class="st">&quot;two_sided&quot;</span>)</code></pre></div>
-<pre><code>## Warning: Check to make sure the conditions have been met for the
-## theoretical method. {infer} currently does not check these for you.</code></pre>
-<p><img 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" /><!-- --></p>
-</div>
-<div id="compute-theoretical-p-value" class="section level2">
-<h2>Compute theoretical p-value</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fli_small <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">t_test</span>(<span class="dt">formula =</span> arr_delay <span class="op">~</span><span class="st"> </span>half_year,
-         <span class="dt">alternative =</span> <span class="st">&quot;two_sided&quot;</span>,
-         <span class="dt">order =</span> <span class="kw">c</span>(<span class="st">&quot;h1&quot;</span>, <span class="st">&quot;h2&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>
-<span class="st">  </span>dplyr<span class="op">::</span><span class="kw">pull</span>(p_value)</code></pre></div>
-<pre><code>## [1] 0.3855</code></pre>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>

From f326f9d12885f1364a7ac967fb0badf4a2cb37e8 Mon Sep 17 00:00:00 2001
From: andrewpbray <andrew.bray@gmail.com>
Date: Wed, 14 Nov 2018 17:06:48 -0800
Subject: [PATCH 77/78] add tolerance for expect_equal so that the test doesn't
 fail when run in environments without long doubles.

---
 tests/testthat/test-wrappers.R | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/tests/testthat/test-wrappers.R b/tests/testthat/test-wrappers.R
index 6f676e75..21d3cc78 100644
--- a/tests/testthat/test-wrappers.R
+++ b/tests/testthat/test-wrappers.R
@@ -29,7 +29,7 @@ test_that("chisq_test works", {
     broom::glance() %>%
     dplyr::select(statistic, chisq_df = parameter, p_value = p.value)
 
-  expect_equal(new_way, old_way)
+  expect_equal(new_way, old_way, tolerance = 1e-5)
   ## Not implemented
   # expect_silent(
   #   iris3 %>% chisq_test(response = Sepal.Length.Group, explanatory = Species)
@@ -93,7 +93,8 @@ test_that("conf_int argument works", {
           conf_int = FALSE
         )
     ),
-    c("statistic", "t_df", "p_value", "alternative")
+    c("statistic", "t_df", "p_value", "alternative"), 
+    tolerance = 1e-5
   )
   expect_equal(
     names(
@@ -103,7 +104,8 @@ test_that("conf_int argument works", {
           conf_int = TRUE
         )
     ),
-    c("statistic", "t_df", "p_value", "alternative", "lower_ci", "upper_ci")
+    c("statistic", "t_df", "p_value", "alternative", "lower_ci", "upper_ci"),
+    tolerance = 1e-5
   )
 
   ci_test <- iris2 %>%
@@ -114,8 +116,8 @@ test_that("conf_int argument works", {
   old_way <- t.test(
     formula = Sepal.Width ~ Species, data = iris2, conf.level = 0.9
   )[["conf.int"]]
-  expect_equal(ci_test$lower_ci[1], old_way[1])
-  expect_equal(ci_test$upper_ci[1], old_way[2])
+  expect_equal(ci_test$lower_ci[1], old_way[1], tolerance = 1e-5)
+  expect_equal(ci_test$upper_ci[1], old_way[2], tolerance = 1e-5)
 
   expect_error(
     iris2 %>%

From 64aad2dd4a1d48e65ada211a57655934dc72c164 Mon Sep 17 00:00:00 2001
From: andrewpbray <andrew.bray@gmail.com>
Date: Thu, 15 Nov 2018 11:03:21 -0800
Subject: [PATCH 78/78] add cran release

---
 .Rbuildignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.Rbuildignore b/.Rbuildignore
index df4589d6..2193547a 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,3 +1,4 @@
+^CRAN-RELEASE$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^README\.Rmd$