lmSubsets v0.4

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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).

Functions in lmSubsets

Name Description
refit Refitting Models
image Variable Selection Heatmaps
lmSelect Best-Subset Regression
AirPollution Air Pollution and Mortality
IbkTemperature Temperature Observations and Numerical Weather Predictions for Innsbruck
lmSubsets All-Subsets Regression
methods Methods for 'lmSubsets' and 'lmSelect' Objects
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Vignettes of lmSubsets

Name
Makefile
README.txt
benchmark.R
bm-01.R
bm-01.RData
bm-02.R
bm-02.RData
bm-03.R
bm-03.RData
bm-04.R
bm-04.RData
bm-05.R
bm-05.RData
bm-ex.R
bm-lasso.R
bm-lasso.RData
lmSubsets.Rnw
lmSubsets.bib
results.txt
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Details

Date 2019-03-07
SystemRequirements C++11
License GPL (>= 3)
URL https://github.com/marc-hofmann/lmSubsets.R
NeedsCompilation yes
Packaged 2019-03-07 14:03:00 UTC; marc
Repository CRAN
Date/Publication 2019-03-07 14:50:03 UTC

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