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, see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>. See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.

Version: 1.6.3
Imports: stats, minqa, Matrix, Rcpp (≥ 0.12.12), methods
LinkingTo: Rcpp, RcppEigen
Published: 2023-08-23
DOI: 10.32614/CRAN.package.glmmLasso
Author: Andreas Groll
Maintainer: Andreas Groll <groll at>
License: GPL-2
NeedsCompilation: yes
In views: MixedModels
CRAN checks: glmmLasso results


Reference manual: glmmLasso.pdf


Package source: glmmLasso_1.6.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmmLasso_1.6.3.tgz, r-oldrel (arm64): glmmLasso_1.6.3.tgz, r-release (x86_64): glmmLasso_1.6.3.tgz, r-oldrel (x86_64): glmmLasso_1.6.3.tgz
Old sources: glmmLasso archive

Reverse dependencies:

Reverse imports: autoMrP


Please use the canonical form to link to this page.