StepReg: Stepwise Regression Analysis

Stepwise regression analysis for variable selection can be used to get the best candidate final regression model in univariate or multivariate regression analysis with the 'forward' and 'stepwise' steps. Procedure can use Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, corrected Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria. Multicollinearity detection in regression model are performed by checking tolerance value. Continuous variables nested within class effect and weighted stepwise regression are also considered in this package.

Version: 1.2.0
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.13)
LinkingTo: Rcpp, RcppEigen
Published: 2019-05-31
Author: Junhui Li,Kun Cheng,Wenxin Liu
Maintainer: Junhui Li <junhuili at cau.edu.cn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: StepReg results

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Reference manual: StepReg.pdf
Package source: StepReg_1.2.0.tar.gz
Windows binaries: r-devel: StepReg_1.2.0.zip, r-release: StepReg_1.2.0.zip, r-oldrel: StepReg_1.2.0.zip
OS X binaries: r-release: StepReg_1.2.0.tgz, r-oldrel: StepReg_1.2.0.tgz
Old sources: StepReg archive

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