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, Gatu, Kontoghiorghes, Colubi, Zeileis (2018, submitted).

Version: 0.3
Depends: R (≥ 3.4.0)
Imports: stats, graphics, utils
Published: 2018-05-01
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
NeedsCompilation: yes
SystemRequirements: C++11
Citation: lmSubsets citation info
CRAN checks: lmSubsets results

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Reference manual: lmSubsets.pdf
Package source: lmSubsets_0.3.tar.gz
Windows binaries: r-devel: lmSubsets_0.3.zip, r-release: lmSubsets_0.3.zip, r-oldrel: lmSubsets_0.3.zip
OS X binaries: r-release: lmSubsets_0.3.tgz, r-oldrel: lmSubsets_0.3.tgz
Old sources: lmSubsets archive

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