glmnet: Lasso and elastic-net regularized generalized linear models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the Cox model. Two recent additions are the multiresponse gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper listed below.

Version: 1.9-8
Depends: Matrix (≥ 1.0-6), utils
Suggests: survival, foreach
Published: 2014-05-24
Author: Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani
Maintainer: Trevor Hastie <hastie at>
License: GPL-2
NeedsCompilation: yes
Citation: glmnet citation info
Materials: ChangeLog
In views: MachineLearning
CRAN checks: glmnet results


Reference manual: glmnet.pdf
Vignettes: Fitting the Penalized Cox Model
Package source: glmnet_1.9-8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: glmnet_1.9-8.tgz, r-oldrel: glmnet_1.9-8.tgz
OS X Mavericks binaries: r-release: glmnet_1.9-8.tgz
Old sources: glmnet archive

Reverse dependencies:

Reverse depends: AdapEnetClass, bigdata, BigTSP, BioMark, cosso, covTest, DivMelt, fcd, FindIt, glmnetcr, glmvsd, hdi, hdlm, KsPlot, lassoscore, MESS, mht, MMMS, MMS, msr, oblique.tree, parcor, PAS, refund.wave, relaxnet, RVtests, SIS, SML, sparsenet, SubLasso, widenet
Reverse imports: c060, Causata, FADA, FindIt, hybridEnsemble, imputeR, IsingFit, MGSDA, MPAgenomics, mpath, parboost, polywog, refund, RSDA, rsig, RTextTools, stm
Reverse suggests: catdata, emil, fbRanks, FRESA.CAD, fscaret, mlr, ModelGood, nscancor, pmml, randomForestSRC, subsemble, SuperLearner
Reverse enhances: stabs