resolve CRAN check issue

DESCRIPTION

@@ -42,6 +42,7 @@ Imports:
     yardstick
 Suggests: 
     covr,
+    finetune,
     knitr,
     markdown,
     modeldata,

NEWS.md

@@ -1,5 +1,7 @@
 # shinymodels (development version)
 
+* Resolved a number of CRAN NOTEs related to documentation formatting.
+
 # shinymodels 0.1.0
 
 * First CRAN release

R/multi_class_diag_plots.R

@@ -2,7 +2,7 @@
 #' model
 #'
 #' This function plots the predicted probabilities against the observed class based on
-#' {tidymodels} results for a multi-class classification model.
+#' tidymodels results for a multi-class classification model.
 #' @param dat The predictions data frame in the [organize_data()] result. Following
 #'  variables are required: `.outcome`, `.pred`, `.color`, and `.hover`.
 #' @param y_name The y/response variable for the model.
@@ -57,7 +57,7 @@ plot_multiclass_conf_mat <- function(dat) {
 #' classification model
 #'
 #' This function plots the predicted probabilities against a numeric column based
-#' on {tidymodels} results for a multi-class classification model.
+#' on tidymodels results for a multi-class classification model.
 #' @inheritParams plot_multiclass_obs_pred
 #' @param numcol The numerical column to plot against the predicted probabilities.
 #' @param alpha The opacity for the geom points.
@@ -138,7 +138,7 @@ plot_multiclass_pred_numcol <-
 #' model
 #'
 #' This function plots the predicted probabilities against a factor column based on
-#' {tidymodels} results for a multi-class classification model.
+#' tidymodels results for a multi-class classification model.
 #' @inheritParams plot_multiclass_obs_pred
 #' @inheritParams plot_multiclass_pred_numcol
 #' @param factorcol The factor column to plot against the predicted probabilities.

R/regression_diag_plots.R

@@ -1,7 +1,7 @@
 #' Visualizing observed vs. predicted values for a regression model
 #'
 #' This function plots the predicted values against the observed values based on
-#'  {tidymodels} results for a regression model.
+#'  tidymodels results for a regression model.
 #' @param dat The predictions data frame in the [organize_data()] result. Following
 #' variables are required: `.outcome`, `.pred`, `.color`, and `.hover`.
 #' @param y_name The y/response variable for the model.
@@ -38,7 +38,7 @@ plot_numeric_obs_pred <- function(dat, y_name, alpha = 1, size = 1, source = NUL
 #' Visualizing residuals vs. predicted values for a regression model
 #'
 #' This function plots the predicted values against the residuals based on
-#' {tidymodels} results for a regression model.
+#' tidymodels results for a regression model.
 #' @inheritParams plot_numeric_obs_pred
 #' @keywords internal
 #' @export
@@ -68,7 +68,7 @@ plot_numeric_res_pred <- function(dat, y_name, size = 1, source = NULL) {
 #' Visualizing residuals vs. a numeric column for a regression model
 #'
 #' This function plots the residuals against a numeric column based on
-#'  {tidymodels} results for a regression model.
+#'  tidymodels results for a regression model.
 #' @inheritParams plot_numeric_obs_pred
 #' @param numcol The numerical column to plot against the residuals.
 #' @keywords internal
@@ -99,7 +99,7 @@ plot_numeric_res_numcol <-
 #' Visualizing residuals vs. a factor column for a regression model
 #'
 #' This function plots the residuals against a factor column based on
-#'  {tidymodels} results for a regression model.
+#'  tidymodels results for a regression model.
 #' @inheritParams plot_numeric_obs_pred
 #' @param factorcol The factor column to plot against the residuals.
 #' @keywords internal

R/shiny_models.R

@@ -12,7 +12,6 @@
 #' @param hover_only A logical to determine if interactive highlighting of
 #' points is enabled (the default) or not. This can be helpful for very large
 #' data sets.
-#' @param original_data Original dataset.
 #' @param ... Other parameters not currently used.
 #' @return A shiny application.
 #' @export

R/two_class_diag_plots.R

@@ -2,7 +2,7 @@
 #' model
 #'
 #' This function plots the predicted probabilities against the observed class based on
-#' {tidymodels} results for a two-class classification model.
+#' tidymodels results for a two-class classification model.
 #' @param dat The predictions data frame in the [organize_data()] result. Following
 #'  variables are required: `.outcome`, `.pred`, `.color`, and `.hover`.
 #' @param y_name The y/response variable for the model.
@@ -53,7 +53,7 @@ plot_twoclass_conf_mat <- function(dat) {
 #' classification model
 #'
 #' This function plots the predicted probabilities against a numeric column based
-#' on {tidymodels} results for a two-class classification model.
+#' on tidymodels results for a two-class classification model.
 #' @inheritParams plot_twoclass_obs_pred
 #' @param numcol The numerical column to plot against the predicted probabilities.
 #' @param alpha The opacity for the geom points.
@@ -125,7 +125,7 @@ plot_twoclass_pred_numcol <-
 #' model
 #'
 #' This function plots the predicted probabilities against a factor column based on
-#' {tidymodels} results for a two-class classification model.
+#' tidymodels results for a two-class classification model.
 #' @inheritParams plot_twoclass_obs_pred
 #' @inheritParams plot_twoclass_pred_numcol
 #' @param factorcol The factor column to plot against the predicted probabilities.

inst/welcome/welcome_tab.Rmd

@@ -5,9 +5,9 @@ output: html_document
 
 ###  Welcome to Shinymodels
 
-{shiny} + {tidymodels} = {shinymodels} 
+shiny + tidymodels = shinymodels
 
-The {shinymodels} app is designed to explore a {tidymodels} object. Our aim is to visualize the modeled data and help detect any problematic observations while modeling. 
+The shinymodels app is designed to explore a tidymodels object. Our aim is to visualize the modeled data and help detect any problematic observations while modeling. 
 
 ### General purpose
 
@@ -25,7 +25,7 @@ There are mainly two categories of tabs in the app:
 
 This package was built by Shisham Adhikari (UC Davis) under the supervision of Max Kuhn (RStudio) and Julia Silge (RStudio) as a part of the RStudio summer internship 2021. Your suggestions, feedback, complaints, or compliments are highly valued and will guide us to improve the package continuously. To give feedback, file a bug report, or ask a question, go to the [GitHub issues page](https://github.com/tidymodels/shinymodels/issues) or go to [RStudio Community](https://community.rstudio.com/) and create a post with the tag `tidymodels`. 
 
-For more information on {tidymodels}: 
+For more information on tidymodels: 
 
  * [`tidymodels.org`](https://www.tidymodels.org/)