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) <10.18637/jss.v093.i03>.

Version: 0.5-1
Depends: R (≥ 3.4.0)
Imports: stats, graphics, utils
Published: 2020-05-23
Author: Marc Hofmann [aut, cre], Cristian Gatu [aut], Erricos J. Kontoghiorghes [aut], Ana Colubi [aut], Achim Zeileis ORCID iD [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

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Reference manual: lmSubsets.pdf
Vignettes: lmSubsets: Exact Variable-Subset Selection in Linear Regression for R
Package source: lmSubsets_0.5-1.tar.gz
Windows binaries: r-devel: lmSubsets_0.5.zip, r-release: lmSubsets_0.5.zip, r-oldrel: lmSubsets_0.5-1.zip
macOS binaries: r-release: lmSubsets_0.5.tgz, r-oldrel: lmSubsets_0.5-1.tgz
Old sources: lmSubsets archive

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