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.


Reference manual

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install.packages("CorReg")

1.2.17 by Clement Thery, 7 years ago


http://www.correg.org, http://www.theses.fr/2015LIL10060


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


Authors: 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]


Documentation:   PDF Manual  


CeCILL license


Imports Rcpp, lars, elasticnet, corrplot, Matrix, rpart, glmnet, MASS, mvtnorm, mclust, methods

Suggests clere, spikeslab, parcor, missMDA, tuneR, knitr, rmarkdown, Rmixmod

Linking to Rcpp, RcppEigen


See at CRAN