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: | 1.6.2 |
| Imports: | stats, minqa, Matrix, Rcpp (≥ 0.12.12), methods, GMMBoost |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2022-08-23 |
| Author: | Andreas Groll |
| Maintainer: | Andreas Groll <groll at statistik.tu-dortmund.de> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | glmmLasso results |
| Reference manual: | glmmLasso.pdf |
| Package source: | glmmLasso_1.6.2.tar.gz |
| Windows binaries: | r-devel: glmmLasso_1.6.2.zip, r-release: glmmLasso_1.6.2.zip, r-oldrel: glmmLasso_1.6.2.zip |
| macOS binaries: | r-release (arm64): glmmLasso_1.6.2.tgz, r-oldrel (arm64): glmmLasso_1.6.2.tgz, r-release (x86_64): glmmLasso_1.6.2.tgz, r-oldrel (x86_64): glmmLasso_1.6.2.tgz |
| Old sources: | glmmLasso archive |
| Reverse imports: | autoMrP |
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