glmmLasso: Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation

A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.

Version: 1.6.1
Imports: stats, minqa, Matrix, Rcpp (≥ 0.12.12), methods, GMMBoost
LinkingTo: Rcpp, RcppEigen
Published: 2022-05-11
Author: Andreas Groll
Maintainer: Andreas Groll <groll at statistik.tu-dortmund.de>
License: GPL-2
NeedsCompilation: yes
CRAN checks: glmmLasso results

Documentation:

Reference manual: glmmLasso.pdf

Downloads:

Package source: glmmLasso_1.6.1.tar.gz
Windows binaries: r-devel: glmmLasso_1.6.1.zip, r-release: glmmLasso_1.6.1.zip, r-oldrel: glmmLasso_1.6.1.zip
macOS binaries: r-release (arm64): glmmLasso_1.6.1.tgz, r-oldrel (arm64): glmmLasso_1.6.1.tgz, r-release (x86_64): glmmLasso_1.6.1.tgz, r-oldrel (x86_64): glmmLasso_1.6.1.tgz
Old sources: glmmLasso archive

Reverse dependencies:

Reverse imports: autoMrP

Linking:

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