l0ara
Sparse Generalized Linear Model with L0 Approximation for Feature Selection
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.6 |
rolling linux/jammy R-4.5 | l0ara_0.1.6.tar.gz |
110.6 KiB |
0.1.6 |
rolling linux/noble R-4.5 | l0ara_0.1.6.tar.gz |
113.6 KiB |
0.1.6 |
rolling source/ R- | l0ara_0.1.6.tar.gz |
9.5 KiB |
0.1.6 |
latest linux/jammy R-4.5 | l0ara_0.1.6.tar.gz |
110.6 KiB |
0.1.6 |
latest linux/noble R-4.5 | l0ara_0.1.6.tar.gz |
113.6 KiB |
0.1.6 |
latest source/ R- | l0ara_0.1.6.tar.gz |
9.5 KiB |
0.1.6 |
2026-04-26 source/ R- | l0ara_0.1.6.tar.gz |
9.5 KiB |
0.1.6 |
2026-04-23 source/ R- | l0ara_0.1.6.tar.gz |
9.5 KiB |
0.1.6 |
2026-04-09 windows/windows R-4.5 | l0ara_0.1.6.zip |
434.3 KiB |
0.1.6 |
2025-04-20 source/ R- | l0ara_0.1.6.tar.gz |
9.5 KiB |
Dependencies (latest)
Imports
- Rcpp (>= 0.12.6)