ggplot2, shiny (>= 0.11), reshape2, dplyr, gridExtra, lme4,
refund

ggplot2, tidyr, shiny (>= 0.11), reshape2, dplyr, gridExtra,
lme4, plotly, refund

c(
person("Julia", "Wrobel", role = c("aut", "cre"), email = "julia.wrobel@cuanschutz.edu"),
person("Jeff", "Goldsmith", role = "aut", email = "jeff.goldsmith@columbia.edu")
)
Shiny applications are produced for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.
ggplot2, tidyr, shiny (>= 0.11), reshape2, dplyr, gridExtra,
lme4, plotly, refund, registr

Produces Shiny applications for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.

Wavelet-Domain Regression with Functional Data
Methods for regressing scalar responses on functional or image predictors, via transformation to the wavelet domain and back.
'decomp.R' 'decomp2d.R' 'decomp3d.R' 'reconstr.R'
'reconstr2d.R' 'reconstr3d.R' 'wcr.R' 'wcr.perm.R' 'wnet.R'
'wnet.perm.R' 'wUtil.R' 'plot.wnet-and-plot.wcr.R' 'Data2wd.R'
'wd2fhat.R'

Performs regularization of differential item functioning (DIF) parameters
in item response theory (IRT) models (Belzak & Bauer, 2020)
<https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization
algorithm.
knitr, testthat (>= 2.1.0), covr, rmarkdown

Random Subspace Method (RSM) for linear regression.
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.

Random Subspace Method (RSM) for Linear Regression

Tools for an Introductory Class in Regression and Modeling
R (>= 3.2.1), bestglm, leaps, VGAM, rpart, randomForest
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers.