relaxnet: Relaxation of glmnet models (as in relaxed lasso, Meinshausen 2007)

Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.

Version: 0.3-2
Depends: glmnet
Suggests: parallel
Published: 2013-08-16
Author: Stephan Ritter, Alan Hubbard
Maintainer: Stephan Ritter <stephanritterRpacks at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: relaxnet results


Reference manual: relaxnet.pdf
Package source: relaxnet_0.3-2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: relaxnet_0.3-2.tgz, r-oldrel: relaxnet_0.3-2.tgz
Old sources: relaxnet archive

Reverse dependencies:

Reverse depends: widenet


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