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>.


Reference manual

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0.5-1 by Marc Hofmann, 10 days ago


Browse source code at https://github.com/cran/lmSubsets

Authors: 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)

Documentation:   PDF Manual  

GPL (>= 3) license

Imports stats, graphics, utils

System requirements: C++11

See at CRAN