Package: glmmLasso 1.6.3

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

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glmmLasso/json (API)

# Install 'glmmLasso' in R:
install.packages('glmmLasso', repos = c('https://hoarzpassey.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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:

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

4.31 score 8 stars 1 packages 86 scripts 977 downloads 11 mentions 4 exports 5 dependencies

Last updated 1 years agofrom:6d3e5465af. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-win-x86_64OKNov 11 2024
R-4.5-linux-x86_64OKNov 11 2024
R-4.4-win-x86_64NOTENov 11 2024
R-4.4-mac-x86_64NOTENov 11 2024
R-4.4-mac-aarch64NOTENov 11 2024
R-4.3-win-x86_64NOTENov 11 2024
R-4.3-mac-x86_64NOTENov 11 2024
R-4.3-mac-aarch64NOTENov 11 2024

Exports:acatcumulativeglmmLassoglmmLassoControl

Dependencies:latticeMatrixminqaRcppRcppEigen