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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.
| Version: | 0.1.6 |
| Imports: | Rcpp (≥ 0.12.6) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2020-02-06 |
| Author: | Wenchuan Guo, Shujie Ma, Zhenqiu Liu |
| Maintainer: | Wenchuan Guo <wguo007 at ucr.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | l0ara results |
| Reference manual: | l0ara.pdf |
| Package source: | l0ara_0.1.6.tar.gz |
| Windows binaries: | r-devel: l0ara_0.1.6.zip, r-release: l0ara_0.1.6.zip, r-oldrel: l0ara_0.1.6.zip |
| macOS binaries: | r-release (arm64): l0ara_0.1.6.tgz, r-oldrel (arm64): l0ara_0.1.6.tgz, r-release (x86_64): l0ara_0.1.6.tgz, r-oldrel (x86_64): l0ara_0.1.6.tgz |
| Old sources: | l0ara archive |
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