Efficient algorithms for fitting the regularization path of linear or logistic regression models with grouped penalties, such as group lasso, group MCP, and group SCAD. The algorithms are based on the idea of either locally approximated coordinate descent or group descent, depending on the penalty.
| Version: | 2.3-0 |
| Depends: | R (≥ 2.13.0) |
| Published: | 2013-02-10 |
| Author: | Patrick Breheny |
| Maintainer: | Patrick Breheny <patrick.breheny at uky.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Citation: | grpreg citation info |
| In views: | MachineLearning |
| CRAN checks: | grpreg results |
| Package source: | grpreg_2.3-0.tar.gz |
| MacOS X binary: | grpreg_2.3-0.tgz |
| Windows binary: | grpreg_2.3-0.zip |
| Reference manual: | grpreg.pdf |
| News/ChangeLog: | NEWS |
| Old sources: | grpreg archive |