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:
glmmLasso_1.6.4.tar.gz
glmmLasso_1.6.4.zip(r-4.7-x86_64)glmmLasso_1.6.4.zip(r-4.7-arm64)glmmLasso_1.6.4.zip(r-4.6-x86_64)glmmLasso_1.6.4.zip(r-4.6-arm64)glmmLasso_1.6.4.zip(r-4.5-x86_64)
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✨
DESCRIPTION
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:8d5d623381. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 183 | ||
| linux-devel-x86_64 | OK | 164 | ||
| source / vignettes | OK | 363 | ||
| linux-release-arm64 | OK | 167 | ||
| linux-release-x86_64 | OK | 229 | ||
| macos-release-arm64 | OK | 202 | ||
| macos-release-x86_64 | OK | 275 | ||
| macos-oldrel-arm64 | OK | 228 | ||
| macos-oldrel-x86_64 | OK | 319 | ||
| windows-devel-arm64 | OK | 373 | ||
| windows-devel-x86_64 | OK | 189 | ||
| windows-release-arm64 | OK | 382 | ||
| windows-release-x86_64 | OK | 140 | ||
| windows-oldrel-x86_64 | OK | 131 | ||
| wasm-release | OK | 106 |
