lmSubsets: Exact Variable-Subset Selection in Linear Regression

Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.

Version:	0.5-2
Depends:	R (≥ 3.5.0)
Imports:	stats, graphics, utils
Published:	2021-02-07
Author:	Marc Hofmann [aut, cre], Cristian Gatu [aut], Erricos J. Kontoghiorghes [aut], Ana Colubi [aut], Achim Zeileis [aut], Martin Moene [cph] (for the GSL Lite library), Microsoft Corporation [cph] (for the GSL Lite library), Free Software Foundation, Inc. [cph] (for snippets from the GNU ISO C++ Library)
Maintainer:	Marc Hofmann <marc.hofmann at gmail.com>
License:	GPL (≥ 3)
URL:	https://github.com/marc-hofmann/lmSubsets.R
NeedsCompilation:	yes
SystemRequirements:	C++11
Citation:	lmSubsets citation info
CRAN checks:	lmSubsets results

Documentation:

Reference manual:	lmSubsets.pdf
Vignettes:	lmSubsets: Exact Variable-Subset Selection in Linear Regression for R

Downloads:

Package source:	lmSubsets_0.5-2.tar.gz
Windows binaries:	r-devel: lmSubsets_0.5-2.zip, r-release: lmSubsets_0.5-2.zip, r-oldrel: lmSubsets_0.5-2.zip
macOS binaries:	r-release (arm64): lmSubsets_0.5-2.tgz, r-oldrel (arm64): lmSubsets_0.5-2.tgz, r-release (x86_64): lmSubsets_0.5-2.tgz, r-oldrel (x86_64): lmSubsets_0.5-2.tgz
Old sources:	lmSubsets archive

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