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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 |
DOI: | 10.32614/CRAN.package.lmSubsets |
Author: | Marc Hofmann [aut, cre],
Cristian Gatu [aut],
Erricos J. Kontoghiorghes [aut],
Ana Colubi [aut],
Achim Zeileis |
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 |
Reference manual: | lmSubsets.pdf |
Vignettes: |
lmSubsets: Exact Variable-Subset Selection in Linear Regression for R |
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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These binaries (installable software) and packages are in development.
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