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. 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.


1.2.7 : Purge values to clean the dataset (including blank separators for thousands)

1.2.0 : some CPP corrections to fit RcppEigen + add a first vignette

1.1.6 : Kruskal-Wallis test to manage non-normal distributions in the BoxPlot function

1.1.0 : improved imports for basic functions

1.0.7 :

remove ridge dependency by using MASS function lm.ridge Add a new BoxPlot function with confidence intervals and anova integrated

1.0.5 enhanced documentation


Changing random generator in C to use runif() instead.


bug removal when X was a data.frame in density_estimatione

Reference manual

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1.2.8 by Clement THERY, a year ago

Browse source code at

Authors: Clement THERY [aut, cre] , Christophe BIERNACKI [ctb] , Gaetan LORIDANT [ctb] , Florian WATRIN [ctb] , Quentin GRIMONPREZ [ctb] , Vincent KUBICKI [ctb] , Samuel BLANCK [ctb] , Jeremie KELLNER [ctb]

Documentation:   PDF Manual  

CeCILL license

Imports Rcpp, lars, Rmixmod, elasticnet, corrplot, Matrix, rpart, glmnet, MASS, mvtnorm, mclust, methods, graphics, grDevices, utils, stats

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

Linking to Rcpp, RcppEigen

See at CRAN