CorReg: Linear Regression Based on Linear Structure Between Variables

Linear regression based on a recursive structural equation model (explicit multiples correlations) found by a M.C.M.C.(Markov Chain Monte Carlo) algorithm. It permits to face highly correlated variables. Variable selection is included (by lasso, elastic net, etc.). It also provides some graphical tools for basic statistics. For more information about the method, read the PhD thesis of Clement Thery (2015) in the link below.

Version: 1.2.17
Imports: Rcpp (≥ 0.11.0), lars (≥ 1.2), elasticnet (≥ 1.1), corrplot (≥ 0.73), Matrix (≥ 1.1), rpart (≥ 4.1-5), glmnet (≥ 2.0-2), MASS (≥ 7.3-30), mvtnorm (≥ 0.9), mclust (≥ 4.2), methods
LinkingTo: Rcpp, RcppEigen
Suggests: clere (≥ 1.1.2), spikeslab (≥ 1.1.5), parcor (≥ 0.2), missMDA (≥ 1.7.3), tuneR, knitr, rmarkdown, Rmixmod (≥ 2.0.1)
Published: 2020-02-20
Author: Clement Thery [aut, cre], Christophe Biernacki [ths], Gaetan Loridant [ths], Florian Watrin [ctb], Quentin Grimonprez [ctb], Vincent Kubicki [ctb], Samuel Blanck [ctb], Jeremie Kellner [ctb]
Maintainer: Clement Thery <corregeous at>
License: CeCILL
Copyright: ArcelorMittal
NeedsCompilation: yes
Citation: CorReg citation info
Materials: NEWS
CRAN checks: CorReg results


Reference manual: CorReg.pdf
Vignettes: How to take advantage of CorReg ?
Package source: CorReg_1.2.17.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: CorReg_1.2.17.tgz, r-oldrel: CorReg_1.2.17.tgz
Old sources: CorReg archive


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