Extended Version of Layout Functionality for 'Base' and 'Grid'
Graphics Systems
c(person("Zygmunt", "Zawadzki", role = c("aut", "cre"), email = "zygmunt@zstat.pl"))
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.
methods, gridExtra, utils, graphics, RColorBrewer
covr, testthat, ggplot2, knitr, rmarkdown, vdiffr, gdtools

Arrange Elements on the R's Drawing Area or Inside the
PowerPoint's Slide
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.
methods, gridExtra, utils, graphics, RColorBrewer, officer,
flextable
covr, testthat, ggplot2, knitr, rmarkdown, vdiffr, gdtools,
magrittr, dplyr

gridExtra, utils, graphics, RColorBrewer, officer, flextable,
assertthat, rvg
covr, testthat, ggplot2, knitr, rmarkdown, vdiffr, gdtools,
magrittr, dplyr, FSelectorRcpp, klaR, stringr, cowplot, png

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

Customized Training for Lasso and Elastic-Net Regularized
Generalized Linear Models
Scott Powers, Trevor Hastie, Robert Tibshirani
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.

Customizable Higher-Order Recipe Step Functions
person("Lars", "Kjeldgaard", email = "lars_kjeldgaard@hotmail.com", role = c("aut", "cre"))
Customizable higher-order recipe step functions for
the 'recipes' package. These step functions take 'prep' and 'bake' helper
functions as inputs and create specifications of customized recipe steps
as output.
recipes, dplyr, tibble, magrittr, purrr, tidyselect, methods,
generics, rlang