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glmmLasso: Variable Selection for Generalized Linear Mixed Models byL1-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 statistik.tu-dortmund.de>
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:

Please use the canonical formhttps://CRAN.R-project.org/package=glmmLassoto link to this page.


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