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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.6.4 |
rolling source/ R- | glmmLasso_1.6.4.tar.gz |
69.7 KiB |
1.6.4 |
rolling linux/jammy R-4.5 | glmmLasso_1.6.4.tar.gz |
537.2 KiB |
1.6.4 |
rolling linux/noble R-4.5 | glmmLasso_1.6.4.tar.gz |
542.5 KiB |
1.6.4 |
latest source/ R- | glmmLasso_1.6.4.tar.gz |
69.7 KiB |
1.6.4 |
latest linux/jammy R-4.5 | glmmLasso_1.6.4.tar.gz |
537.2 KiB |
1.6.4 |
latest linux/noble R-4.5 | glmmLasso_1.6.4.tar.gz |
542.5 KiB |
1.6.4 |
2026-04-23 source/ R- | glmmLasso_1.6.4.tar.gz |
69.7 KiB |
1.6.4 |
2026-04-09 windows/windows R-4.5 | glmmLasso_1.6.4.zip |
860.5 KiB |
1.6.3 |
2025-04-20 source/ R- | glmmLasso_1.6.3.tar.gz |
68.3 KiB |