Package: sparsenet
Type: Package
Title: Fit Sparse Linear Regression Models via Nonconvex Optimization
Version: 1.4
Date: 2019-11-06
Author: Rahul Mazumder [aut, cre],
	Trevor Hastie [aut, cre],
	Jerome Friedman [aut, cre]
Maintainer: Trevor Hastie <hastie@stanford.edu>
Description: 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.
Depends: Matrix (>= 1.0-6), shape
Imports: methods
License: GPL-2
NeedsCompilation: yes
URL:
        http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf
Packaged: 2019-11-09 21:33:47 UTC; hastie
Repository: CRAN
Date/Publication: 2019-11-10 06:40:03 UTC
