easy.glmnet: Functions to Simplify the Use of 'glmnet' for Machine Learning

Provides several functions to simplify using the 'glmnet' package: converting data frames into matrices ready for 'glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models; f) selecting the most representative model across imputations and folds; and g) getting the relevance of the model regressors; as described in several publications: Solanes et al. (2022) <doi:10.1038/s41537-022-00309-w>, Palau et al. (2023) <doi:10.1016/j.rpsm.2023.01.001>, Sobregrau et al. (2024) <doi:10.1016/j.jpsychores.2024.111656>.

Version:	1.0
Imports:	doParallel, foreach, glmnet, parallel, survival
Suggests:	pROC
Published:	2024-09-11
DOI:	10.32614/CRAN.package.easy.glmnet
Author:	Joaquim Radua [aut, cre]
Maintainer:	Joaquim Radua <quimradua at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	easy.glmnet results

Documentation:

Reference manual:

easy.glmnet.pdf

Downloads:

Package source:	easy.glmnet_1.0.tar.gz
Windows binaries:	r-devel: easy.glmnet_1.0.zip, r-release: easy.glmnet_1.0.zip, r-oldrel: easy.glmnet_1.0.zip
macOS binaries:	r-release (arm64): easy.glmnet_1.0.tgz, r-oldrel (arm64): easy.glmnet_1.0.tgz, r-release (x86_64): easy.glmnet_1.0.tgz, r-oldrel (x86_64): easy.glmnet_1.0.tgz

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