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:	0.9-52
Depends:	R (≥ 2.10.0), survival, methods
Imports:	Rcpp
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	globaltest
Published:	2022-04-23
DOI:	10.32614/CRAN.package.penalized
Author:	Jelle Goeman, Rosa Meijer, Nimisha Chaturvedi, Matthew Lueder
Maintainer:	Jelle Goeman <j.j.goeman at lumc.nl>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	penalized citation info
Materials:	README ChangeLog
In views:	MachineLearning, Survival
CRAN checks:	penalized results

Documentation:

Reference manual:	penalized.pdf
Vignettes:	Penalized user guide

Downloads:

Package source:	penalized_0.9-52.tar.gz
Windows binaries:	r-devel: penalized_0.9-52.zip, r-release: penalized_0.9-52.zip, r-oldrel: penalized_0.9-52.zip
macOS binaries:	r-release (arm64): penalized_0.9-52.tgz, r-oldrel (arm64): penalized_0.9-52.tgz, r-release (x86_64): penalized_0.9-52.tgz, r-oldrel (x86_64): penalized_0.9-52.tgz
Old sources:	penalized archive

Reverse dependencies:

Reverse depends:	DIFtree, PACLasso, structree
Reverse imports:	c060, DIFboost, DIFlasso, GSelection, hdnom, mispr, mvdalab, netZooR, penalizedclr, pensim, scRecover, splmm
Reverse suggests:	catdata, confSAM, flowml, fscaret, globaltest, lda, mlr, ordinalNet, peperr, riskRegression, tramnet

Linking:

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