Package: glmmLasso 1.6.4

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.

Authors:Andreas Groll [aut, cre, cph]

glmmLasso_1.6.4.tar.gz
glmmLasso_1.6.4.zip(r-4.7)glmmLasso_1.6.4.zip(r-4.6)glmmLasso_1.6.4.zip(r-4.5)
glmmLasso_1.6.4.tgz(r-4.6-x86_64)glmmLasso_1.6.4.tgz(r-4.6-arm64)glmmLasso_1.6.4.tgz(r-4.5-x86_64)glmmLasso_1.6.4.tgz(r-4.5-arm64)
glmmLasso_1.6.4.tar.gz(r-4.7-arm64)glmmLasso_1.6.4.tar.gz(r-4.7-x86_64)glmmLasso_1.6.4.tar.gz(r-4.6-arm64)glmmLasso_1.6.4.tar.gz(r-4.6-x86_64)
glmmLasso_1.6.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
glmmLasso/json (API)

# Install 'glmmLasso' in R:
install.packages('glmmLasso', repos = c('https://hoarzpassey.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • knee - Clinical pain study on knee data
  • soccer - German Bundesliga data for the seasons 2008-2010

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

4.50 score 8 stars 1 packages 132 scripts 669 downloads 11 mentions 4 exports 5 dependencies

Last updated from:8d5d623381. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK175
linux-devel-x86_64OK174
source / vignettesOK281
linux-release-arm64OK161
linux-release-x86_64OK172
macos-release-arm64OK195
macos-release-x86_64OK479
macos-oldrel-arm64OK176
macos-oldrel-x86_64OK546
windows-develOK171
windows-releaseOK166
windows-oldrelOK158
wasm-releaseOK110

Exports:acatcumulativeglmmLassoglmmLassoControl

Dependencies:latticeMatrixminqaRcppRcppEigen