l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection

An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.

Version:	0.1.6
Imports:	Rcpp (≥ 0.12.6)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2020-02-06
Author:	Wenchuan Guo, Shujie Ma, Zhenqiu Liu
Maintainer:	Wenchuan Guo <wguo007 at ucr.edu>
License:	GPL-2
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	l0ara results

Documentation:

Reference manual:

l0ara.pdf

Downloads:

Package source:	l0ara_0.1.6.tar.gz
Windows binaries:	r-devel: l0ara_0.1.6.zip, r-release: l0ara_0.1.6.zip, r-oldrel: l0ara_0.1.6.zip
macOS binaries:	r-release (arm64): l0ara_0.1.6.tgz, r-oldrel (arm64): l0ara_0.1.6.tgz, r-release (x86_64): l0ara_0.1.6.tgz, r-oldrel (x86_64): l0ara_0.1.6.tgz
Old sources:	l0ara archive

Linking:

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