sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization

Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.

Version: 1.3
Depends: glmnet, Matrix (≥ 1.0-6), shape
Imports: methods
Published: 2019-02-11
Author: Rahul Mazumder [aut, cre], Trevor Hastie [aut, cre], Jerome Friedman [aut, cre]
Maintainer: Trevor Hastie <hastie at stanford.edu>
License: GPL-2
URL: http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf
NeedsCompilation: yes
Citation: sparsenet citation info
CRAN checks: sparsenet results

Downloads:

Reference manual: sparsenet.pdf
Package source: sparsenet_1.3.tar.gz
Windows binaries: r-devel: sparsenet_1.3.zip, r-release: sparsenet_1.3.zip, r-oldrel: sparsenet_1.3.zip
OS X binaries: r-release: sparsenet_1.3.tgz, r-oldrel: sparsenet_1.3.tgz
Old sources: sparsenet archive

Reverse dependencies:

Reverse imports: cmenet

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