From d2d8620fbe990a40e42029d977f79b208b916d29 Mon Sep 17 00:00:00 2001
From: "J. Derek Tucker" <jdtuck@sandia.gov>
Date: Mon, 21 Jun 2021 14:51:03 -0600
Subject: [PATCH] spelling fixes

---
 R/elastic_lpcr_regression.R       | 8 ++++----
 R/elastic_mlpcr_regression.R      | 8 ++++----
 R/elastic_pcr_regression.R        | 8 ++++----
 R/function_group_warp_bayes.R     | 2 +-
 R/joint_gauss_model.R             | 2 +-
 R/jointfPCA.R                     | 6 +++---
 R/pair_align_functions.R          | 2 +-
 R/pair_align_functions_bayes.R    | 2 +-
 R/predict.lpcr.R                  | 4 ++--
 R/predict.mlpcr.R                 | 4 ++--
 R/tolerance.R                     | 2 +-
 man/elastic.lpcr.regression.Rd    | 8 ++++----
 man/elastic.mlpcr.regression.Rd   | 8 ++++----
 man/elastic.pcr.regression.Rd     | 8 ++++----
 man/function_group_warp_bayes.Rd  | 2 +-
 man/jointFPCA.Rd                  | 6 +++---
 man/joint_gauss_model.Rd          | 2 +-
 man/pair_align_functions.Rd       | 2 +-
 man/pair_align_functions_bayes.Rd | 2 +-
 man/pcaTB.Rd                      | 2 +-
 man/predict.lpcr.Rd               | 4 ++--
 man/predict.mlpcr.Rd              | 4 ++--
 22 files changed, 48 insertions(+), 48 deletions(-)

diff --git a/R/elastic_lpcr_regression.R b/R/elastic_lpcr_regression.R
index ac08080..4b27988 100644
--- a/R/elastic_lpcr_regression.R
+++ b/R/elastic_lpcr_regression.R
@@ -1,4 +1,4 @@
-#' Elastic Logisitc Prinipcal Component Regression
+#' Elastic logistic Principal Component Regression
 #'
 #' This function identifies a logistic regression model with phase-variability
 #' using elastic pca
@@ -6,9 +6,9 @@
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} lables
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @return Returns a lpcr object containing
@@ -18,7 +18,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{Loss}{logistic loss}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data
diff --git a/R/elastic_mlpcr_regression.R b/R/elastic_mlpcr_regression.R
index 61528e3..02efc1a 100644
--- a/R/elastic_mlpcr_regression.R
+++ b/R/elastic_mlpcr_regression.R
@@ -1,4 +1,4 @@
-#' Elastic Multinomial Logisitc Prinipcal Component Regression
+#' Elastic Multinomial logistic Principal Component Regression
 #'
 #' This function identifies a multinomial logistic regression model with phase-variability
 #' using elastic pca
@@ -6,9 +6,9 @@
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} labels
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @return Returns a mlpcr object containing
@@ -19,7 +19,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{Loss}{logistic loss}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data
diff --git a/R/elastic_pcr_regression.R b/R/elastic_pcr_regression.R
index 8255b5a..b208630 100644
--- a/R/elastic_pcr_regression.R
+++ b/R/elastic_pcr_regression.R
@@ -1,4 +1,4 @@
-#' Elastic Linear Prinipcal Component Regression
+#' Elastic Linear Principal Component Regression
 #'
 #' This function identifies a regression model with phase-variability
 #' using elastic pca
@@ -6,9 +6,9 @@
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} responses
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @param parallel run in parallel (default = F)
@@ -20,7 +20,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{SSE}{sum of squared errors}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data
diff --git a/R/function_group_warp_bayes.R b/R/function_group_warp_bayes.R
index 0b9dca9..f1a7e80 100644
--- a/R/function_group_warp_bayes.R
+++ b/R/function_group_warp_bayes.R
@@ -5,7 +5,7 @@
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param time sample points of functions
 #' @param iter number of iterations (default = 150000)
-#' @param powera Dirchelet prior parameter (default 1)
+#' @param powera Dirichlet prior parameter (default 1)
 #' @param times factor of length of subsample points to look at (default = 5)
 #' @param tau standard deviation of Normal prior for increment (default ceil(times*.4))
 #' @param gp number of colors in plots (default seq(dim(f)[2]))
diff --git a/R/joint_gauss_model.R b/R/joint_gauss_model.R
index aed2ea9..e47bea3 100644
--- a/R/joint_gauss_model.R
+++ b/R/joint_gauss_model.R
@@ -3,7 +3,7 @@
 #' This function models the functional data using a Gaussian model extracted from
 #' the principal components of the srvfs using the joint model
 #'
-#' @param warp_data fdawarp objecet from \link{time_warping} of aligned data
+#' @param warp_data fdawarp object from \link{time_warping} of aligned data
 #' @param n number of random samples (n = 1)
 #' @param no number of principal components (n=4)
 #' @return Returns a fdawarp object containing \item{fs}{random aligned samples}
diff --git a/R/jointfPCA.R b/R/jointfPCA.R
index 0ffcf29..45e16e7 100644
--- a/R/jointfPCA.R
+++ b/R/jointfPCA.R
@@ -3,12 +3,12 @@
 #' This function calculates amplitude and phase joint  functional principal component
 #' analysis on aligned data
 #'
-#' @param warp_data fdawarp objecet from \link{time_warping} of aligned data
-#' @param no number of prinicpal components to extract
+#' @param warp_data fdawarp object from \link{time_warping} of aligned data
+#' @param no number of principal components to extract
 #' @param id integration point for f0 (default = midpoint)
 #' @param C balance value (default = NULL)
 #' @param ci geodesic standard deviations (default = c(-1,0,1))
-#' @param showplot show plots of prinipal directions (default = T)
+#' @param showplot show plots of principal directions (default = T)
 #' @return Returns a list containing \item{q_pca}{srvf principal directions}
 #' \item{f_pca}{f principal directions}
 #' \item{latent}{latent values}
diff --git a/R/pair_align_functions.R b/R/pair_align_functions.R
index 15187ec..2ef9561 100644
--- a/R/pair_align_functions.R
+++ b/R/pair_align_functions.R
@@ -9,7 +9,7 @@
 #' @param lambda controls amount of warping (default = 0)
 #' @param method controls which optimization method (default="DP") options are
 #' Dynamic Programming ("DP"), Coordinate Descent ("DP2"), Riemannian BFGS
-#' ("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirchelet Bayesian ("dBayes"),
+#' ("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirichlet Bayesian ("dBayes"),
 #' and Expo-Map Bayesian ("expBayes")
 #' @param w controls LRBFGS (default = 0.01)
 #' @param iter number of mcmc iterations for mcmc method (default 2000)
diff --git a/R/pair_align_functions_bayes.R b/R/pair_align_functions_bayes.R
index 6ada120..b39ff19 100644
--- a/R/pair_align_functions_bayes.R
+++ b/R/pair_align_functions_bayes.R
@@ -9,7 +9,7 @@
 #' @param iter number of iterations (default = 15000)
 #' @param times factor of length of subsample points to look at (default = 5)
 #' @param tau standard deviation of Normal prior for increment (default ceil(times*.4))
-#' @param powera Dirchelet prior parameter (default 1)
+#' @param powera Dirichlet prior parameter (default 1)
 #' @param showplot shows plots of functions (default = T)
 #' @param extrainfo T/F whether additional information is returned
 #' @return Returns a list containing \item{f1}{function 1}
diff --git a/R/predict.lpcr.R b/R/predict.lpcr.R
index b93a26a..d65f576 100644
--- a/R/predict.lpcr.R
+++ b/R/predict.lpcr.R
@@ -1,4 +1,4 @@
-#' Elastic Prediction for functional logisitc PCR Model
+#' Elastic Prediction for functional logistic PCR Model
 #'
 #' This function performs prediction from an elastic logistic fPCR regression model
 #'  with phase-variability
@@ -8,7 +8,7 @@
 #' @param y An optional vector of labels to calculate PC. If omitted, PC is NULL
 #' @param ... additional arguments affecting the predictions produced
 #' @return Returns a list containing
-#' \item{y_pred}{predicted probabilites of the class of newdata}
+#' \item{y_pred}{predicted probabilities of the class of newdata}
 #' \item{y_labels}{class labels of newdata}
 #' \item{PC}{probability of classification}
 #' @keywords srvf alignment regression
diff --git a/R/predict.mlpcr.R b/R/predict.mlpcr.R
index b522fe3..017d61f 100644
--- a/R/predict.mlpcr.R
+++ b/R/predict.mlpcr.R
@@ -1,4 +1,4 @@
-#' Elastic Prediction for functional multinomial logisitc PCR Model
+#' Elastic Prediction for functional multinomial logistic PCR Model
 #'
 #' This function performs prediction from an elastic multinomial logistic fPCR regression model
 #'  with phase-variability
@@ -8,7 +8,7 @@
 #' @param y An optional vector of labels to calculate PC. If omitted, PC is NULL
 #' @param ... additional arguments affecting the predictions produced
 #' @return Returns a list containing
-#' \item{y_pred}{predicted probabilites of the class of newdata}
+#' \item{y_pred}{predicted probabilities of the class of newdata}
 #' \item{y_labels}{class labels of newdata}
 #' \item{PC}{probability of classification per class}
 #' \item{PC.comb}{total probability of classification}
diff --git a/R/tolerance.R b/R/tolerance.R
index 1ca7317..b464a8d 100644
--- a/R/tolerance.R
+++ b/R/tolerance.R
@@ -89,7 +89,7 @@ bootTB <- function(f, time, a=.05, p=.99, B=500, no = 5, parallel=T){
 #' Tolerance Bound Calculation using Elastic Functional PCA
 #'
 #' This function computes tolerance bounds for functional data containing
-#' phase and amplitude variation using principal component anlaysis
+#' phase and amplitude variation using principal component analysis
 #'
 #' @param f matrix of functions
 #' @param time vector describing time sampling
diff --git a/man/elastic.lpcr.regression.Rd b/man/elastic.lpcr.regression.Rd
index 88cbc3e..dc9fb6d 100644
--- a/man/elastic.lpcr.regression.Rd
+++ b/man/elastic.lpcr.regression.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/elastic_lpcr_regression.R
 \name{elastic.lpcr.regression}
 \alias{elastic.lpcr.regression}
-\title{Elastic Logisitc Prinipcal Component Regression}
+\title{Elastic logistic Principal Component Regression}
 \usage{
 elastic.lpcr.regression(
   f,
@@ -21,10 +21,10 @@ elastic.lpcr.regression(
 
 \item{time}{vector of size \eqn{N} describing the sample points}
 
-\item{pca.method}{string specifing pca method (options = "combined",
+\item{pca.method}{string specifying pca method (options = "combined",
 "vert", or "horiz", default = "combined")}
 
-\item{no}{scalar specifify number of principal components (default=5)}
+\item{no}{scalar specify number of principal components (default=5)}
 
 \item{smooth_data}{smooth data using box filter (default = F)}
 
@@ -38,7 +38,7 @@ Returns a lpcr object containing
 \item{warp_data}{fdawarp object of aligned data}
 \item{pca}{pca object of principal components}
 \item{Loss}{logistic loss}
-\item{pca.method}{string specifing pca method used}
+\item{pca.method}{string specifying pca method used}
 }
 \description{
 This function identifies a logistic regression model with phase-variability
diff --git a/man/elastic.mlpcr.regression.Rd b/man/elastic.mlpcr.regression.Rd
index eda9301..62e098f 100644
--- a/man/elastic.mlpcr.regression.Rd
+++ b/man/elastic.mlpcr.regression.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/elastic_mlpcr_regression.R
 \name{elastic.mlpcr.regression}
 \alias{elastic.mlpcr.regression}
-\title{Elastic Multinomial Logisitc Prinipcal Component Regression}
+\title{Elastic Multinomial logistic Principal Component Regression}
 \usage{
 elastic.mlpcr.regression(
   f,
@@ -21,10 +21,10 @@ elastic.mlpcr.regression(
 
 \item{time}{vector of size \eqn{N} describing the sample points}
 
-\item{pca.method}{string specifing pca method (options = "combined",
+\item{pca.method}{string specifying pca method (options = "combined",
 "vert", or "horiz", default = "combined")}
 
-\item{no}{scalar specifify number of principal components (default=5)}
+\item{no}{scalar specify number of principal components (default=5)}
 
 \item{smooth_data}{smooth data using box filter (default = F)}
 
@@ -39,7 +39,7 @@ Returns a mlpcr object containing
 \item{warp_data}{fdawarp object of aligned data}
 \item{pca}{pca object of principal components}
 \item{Loss}{logistic loss}
-\item{pca.method}{string specifing pca method used}
+\item{pca.method}{string specifying pca method used}
 }
 \description{
 This function identifies a multinomial logistic regression model with phase-variability
diff --git a/man/elastic.pcr.regression.Rd b/man/elastic.pcr.regression.Rd
index 74ca02b..ceebc87 100644
--- a/man/elastic.pcr.regression.Rd
+++ b/man/elastic.pcr.regression.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/elastic_pcr_regression.R
 \name{elastic.pcr.regression}
 \alias{elastic.pcr.regression}
-\title{Elastic Linear Prinipcal Component Regression}
+\title{Elastic Linear Principal Component Regression}
 \usage{
 elastic.pcr.regression(
   f,
@@ -23,10 +23,10 @@ elastic.pcr.regression(
 
 \item{time}{vector of size \eqn{N} describing the sample points}
 
-\item{pca.method}{string specifing pca method (options = "combined",
+\item{pca.method}{string specifying pca method (options = "combined",
 "vert", or "horiz", default = "combined")}
 
-\item{no}{scalar specifify number of principal components (default=5)}
+\item{no}{scalar specify number of principal components (default=5)}
 
 \item{smooth_data}{smooth data using box filter (default = F)}
 
@@ -44,7 +44,7 @@ Returns a pcr object containing
 \item{warp_data}{fdawarp object of aligned data}
 \item{pca}{pca object of principal components}
 \item{SSE}{sum of squared errors}
-\item{pca.method}{string specifing pca method used}
+\item{pca.method}{string specifying pca method used}
 }
 \description{
 This function identifies a regression model with phase-variability
diff --git a/man/function_group_warp_bayes.Rd b/man/function_group_warp_bayes.Rd
index 7105d26..ad42afd 100644
--- a/man/function_group_warp_bayes.Rd
+++ b/man/function_group_warp_bayes.Rd
@@ -22,7 +22,7 @@ function_group_warp_bayes(
 
 \item{iter}{number of iterations (default = 150000)}
 
-\item{powera}{Dirchelet prior parameter (default 1)}
+\item{powera}{Dirichlet prior parameter (default 1)}
 
 \item{times}{factor of length of subsample points to look at (default = 5)}
 
diff --git a/man/jointFPCA.Rd b/man/jointFPCA.Rd
index 01892e2..56fec33 100644
--- a/man/jointFPCA.Rd
+++ b/man/jointFPCA.Rd
@@ -14,9 +14,9 @@ jointFPCA(
 )
 }
 \arguments{
-\item{warp_data}{fdawarp objecet from \link{time_warping} of aligned data}
+\item{warp_data}{fdawarp object from \link{time_warping} of aligned data}
 
-\item{no}{number of prinicpal components to extract}
+\item{no}{number of principal components to extract}
 
 \item{id}{integration point for f0 (default = midpoint)}
 
@@ -24,7 +24,7 @@ jointFPCA(
 
 \item{ci}{geodesic standard deviations (default = c(-1,0,1))}
 
-\item{showplot}{show plots of prinipal directions (default = T)}
+\item{showplot}{show plots of principal directions (default = T)}
 }
 \value{
 Returns a list containing \item{q_pca}{srvf principal directions}
diff --git a/man/joint_gauss_model.Rd b/man/joint_gauss_model.Rd
index 0bfcea0..02a6931 100644
--- a/man/joint_gauss_model.Rd
+++ b/man/joint_gauss_model.Rd
@@ -7,7 +7,7 @@
 joint_gauss_model(warp_data, n = 1, no = 5)
 }
 \arguments{
-\item{warp_data}{fdawarp objecet from \link{time_warping} of aligned data}
+\item{warp_data}{fdawarp object from \link{time_warping} of aligned data}
 
 \item{n}{number of random samples (n = 1)}
 
diff --git a/man/pair_align_functions.Rd b/man/pair_align_functions.Rd
index 0b0fc4e..6a3c893 100644
--- a/man/pair_align_functions.Rd
+++ b/man/pair_align_functions.Rd
@@ -25,7 +25,7 @@ pair_align_functions(
 
 \item{method}{controls which optimization method (default="DP") options are
 Dynamic Programming ("DP"), Coordinate Descent ("DP2"), Riemannian BFGS
-("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirchelet Bayesian ("dBayes"),
+("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirichlet Bayesian ("dBayes"),
 and Expo-Map Bayesian ("expBayes")}
 
 \item{w}{controls LRBFGS (default = 0.01)}
diff --git a/man/pair_align_functions_bayes.Rd b/man/pair_align_functions_bayes.Rd
index 6a851dd..15a450d 100644
--- a/man/pair_align_functions_bayes.Rd
+++ b/man/pair_align_functions_bayes.Rd
@@ -29,7 +29,7 @@ pair_align_functions_bayes(
 
 \item{tau}{standard deviation of Normal prior for increment (default ceil(times*.4))}
 
-\item{powera}{Dirchelet prior parameter (default 1)}
+\item{powera}{Dirichlet prior parameter (default 1)}
 
 \item{showplot}{shows plots of functions (default = T)}
 
diff --git a/man/pcaTB.Rd b/man/pcaTB.Rd
index f86ee39..e8f2652 100644
--- a/man/pcaTB.Rd
+++ b/man/pcaTB.Rd
@@ -25,7 +25,7 @@ Returns a list containing \item{pca}{pca output}
 }
 \description{
 This function computes tolerance bounds for functional data containing
-phase and amplitude variation using principal component anlaysis
+phase and amplitude variation using principal component analysis
 }
 \examples{
 \dontrun{
diff --git a/man/predict.lpcr.Rd b/man/predict.lpcr.Rd
index 498ec39..7e84084 100644
--- a/man/predict.lpcr.Rd
+++ b/man/predict.lpcr.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/predict.lpcr.R
 \name{predict.lpcr}
 \alias{predict.lpcr}
-\title{Elastic Prediction for functional logisitc PCR Model}
+\title{Elastic Prediction for functional logistic PCR Model}
 \usage{
 \method{predict}{lpcr}(object, newdata = NULL, y = NULL, ...)
 }
@@ -17,7 +17,7 @@
 }
 \value{
 Returns a list containing
-\item{y_pred}{predicted probabilites of the class of newdata}
+\item{y_pred}{predicted probabilities of the class of newdata}
 \item{y_labels}{class labels of newdata}
 \item{PC}{probability of classification}
 }
diff --git a/man/predict.mlpcr.Rd b/man/predict.mlpcr.Rd
index 6df4eb1..00275de 100644
--- a/man/predict.mlpcr.Rd
+++ b/man/predict.mlpcr.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/predict.mlpcr.R
 \name{predict.mlpcr}
 \alias{predict.mlpcr}
-\title{Elastic Prediction for functional multinomial logisitc PCR Model}
+\title{Elastic Prediction for functional multinomial logistic PCR Model}
 \usage{
 \method{predict}{mlpcr}(object, newdata = NULL, y = NULL, ...)
 }
@@ -17,7 +17,7 @@
 }
 \value{
 Returns a list containing
-\item{y_pred}{predicted probabilites of the class of newdata}
+\item{y_pred}{predicted probabilities of the class of newdata}
 \item{y_labels}{class labels of newdata}
 \item{PC}{probability of classification per class}
 \item{PC.comb}{total probability of classification}