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.">

glmmLasso: Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation (original) (raw)

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
License: GPL-2
NeedsCompilation: yes
In views: MixedModels
CRAN checks: glmmLasso results

Documentation:

Reference manual: glmmLasso.html , <glmmLasso.pdf>

Downloads:

Package source: glmmLasso_1.6.3.tar.gz
Windows binaries: r-devel: glmmLasso_1.6.3.zip, r-release: glmmLasso_1.6.3.zip, r-oldrel: glmmLasso_1.6.3.zip
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

Linking:

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