Title: Linear regression based on linear structure between covariates
Description: Sequential linear regression based on a recursive structural
equation model (explicit correlations). It permits to face highly

Authors@R: c(person("Clement", "THERY", role = c("aut", "cre"),
person("Florian", "WATRIN", role = "ctb"))

Imports: Rcpp(>= 0.11.0), lars(>= 1.2), elasticnet(>= 1.1), Matrix(>=

person("Christophe", "BIERNACKI", role = "ctb"),
person("Gaetan", "LORIDANT", role = "ctb"),
person("Florian", "WATRIN", role = "ctb"),
person("Quentin", "GRIMONPREZ", role = "ctb")

person("Quentin", "GRIMONPREZ", role = "ctb"),
person("Vincent", "KUBICKI", role = "ctb")
Description: Linear regression based on a recursive structural equation model
(explicit correlations) found by a MCMC algorithm. It permits to face
highly correlated covariates. Variable selection is included (by lasso,
elasticnet, etc.). It also provides some graphical tools for basic

person("Vincent", "KUBICKI", role = "ctb"),
person("Samuel", "BLANCK", role = "ctb"),

Imports: Rcpp(>= 0.11.0), lars(>= 1.2), elasticnet(>= 1.1), corrplot(>=
Suggests: clere(>= 1.1), spikeslab(>= 1.1.5), parcor(>= 0.2),

Title: Linear Regression Based on Linear Structure Between Covariates
elastic net, etc.). It also provides some graphical tools for basic
elasticnet(>= 1.1), corrplot(>= 0.73), Matrix(>= 1.1), ridge(>=
Suggests: clere(>= 1.1), spikeslab(>= 1.1.5), parcor(>= 0.2), missMDA

Suggests: clere(>= 1.1), spikeslab(>= 1.1.5), parcor(>= 0.2), rtkpp (>=

(explicit multiples correlations) found by a MCMC algorithm. It permits to face
statistics (Boxplot with confidence intervals, etc) and regression trees.
elasticnet(>= 1.1), corrplot(>= 0.73), Matrix(>= 1.1), rpart(>=
mclust(>= 4.2), methods, graphics, grDevices, utils, stats
Suggests: clere(>= 1.1.2), spikeslab(>= 1.1.5), parcor(>= 0.2), missMDA

Title: Linear Regression Based on Linear Structure Between Variables
(explicit multiples correlations) found by a M.C.M.C. algorithm. It permits to face
highly correlated variables. Variable selection is included (by lasso,