penalized: L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model

Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.

Version: 0.9-47
Depends: R (≥ 2.10.0), survival, methods
Suggests: globaltest
Published: 2016-05-27
Author: Jelle Goeman, Rosa Meijer, Nimisha Chaturvedi
Maintainer: Jelle Goeman <j.j.goeman at lumc.nl>
BugReports: NA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: NA
NeedsCompilation: no
Citation: penalized citation info
Materials: ChangeLog
In views: MachineLearning, Survival
CRAN checks: penalized results

Downloads:

Reference manual: penalized.pdf
Vignettes: Penalized user guide
Package source: penalized_0.9-47.tar.gz
Windows binaries: r-devel: penalized_0.9-47.zip, r-release: penalized_0.9-47.zip, r-oldrel: penalized_0.9-47.zip
OS X Mavericks binaries: r-release: penalized_0.9-47.tgz, r-oldrel: penalized_0.9-47.tgz
Old sources: penalized archive

Reverse dependencies:

Reverse depends: DIFtree, lmmlasso, multiPIM, ROC632, subtype, uplift
Reverse imports: apricom, DIFboost, DIFlasso, gpDDE, hdnom, mvdalab, pensim
Reverse suggests: catdata, fscaret, Grace, lda, mlr, MWLasso, peperr