Title: Custom Made Gauss Quadrature Nodes and Weights
Use the high-precision arithmetic provided by the R package 'Rmpfr'
to compute a custom-made Gauss quadrature nodes and weights,
with up to 33 nodes, using a moment-based method via moment
determinants. Paul Kabaila (2022) <arXiv:2211.04729>.
Author: Paul Kabaila [aut, cre] (<https://orcid.org/0000-0002-9205-668X>)

Title: Extended Version of Layout Functionality for 'Base' and 'Grid'
Authors@R: c(person("Zygmunt", "Zawadzki", role = c("aut", "cre"), email = "zygmunt@zstat.pl"))
Description: Create complicated drawing areas for multiple plots by combining much simpler layouts. It is an extended version of layout function from the 'graphics' package, but it also works with 'grid' graphics.
Imports: methods, gridExtra, utils, graphics, RColorBrewer
Suggests: covr, testthat, ggplot2, knitr, rmarkdown, vdiffr, gdtools

Title: Arrange Elements on the R's Drawing Area or Inside the
Description: Create complicated drawing areas for multiple elements by combining much simpler layouts. It is an extended version of layout function from the 'graphics' package, but it also works with 'grid' graphics. It also supports arranging elements inside 'PowerPoint' slides created using the 'officer' package.
Imports: methods, gridExtra, utils, graphics, RColorBrewer, officer,
Suggests: covr, testthat, ggplot2, knitr, rmarkdown, vdiffr, gdtools,

Imports: gridExtra, utils, graphics, RColorBrewer, officer, flextable,
magrittr, dplyr, FSelectorRcpp, klaR, stringr, cowplot, png

Imports: gridExtra, utils, graphics, RColorBrewer, officer (>= 0.3.6),
flextable (>= 0.5.6), assertthat, rvg (>= 0.2.2)

Title: Customized Training for Lasso and Elastic-Net Regularized
Author: Scott Powers, Trevor Hastie, Robert Tibshirani
Description: Customized training is a simple technique for transductive
learning, when the test covariates are known at the time of training. The
method identifies a subset of the training set to serve as the training set
for each of a few identified subsets in the training set. This package
implements customized training for the glmnet() and cv.glmnet() functions.