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.
clere(>= 1.1.2), spikeslab(>= 1.1.5), parcor(>= 0.2), missMDA
(>= 1.7.3), tuneR, knitr, rmarkdown

c(person("Clement", "Thery", role = c("aut", "cre"), email = "corregeous@correg.org"),
person("Christophe", "Biernacki", role = "ths"),
person("Gaetan", "Loridant", role = "ths"),
person("Florian", "Watrin", role = "ctb"),
person("Quentin", "Grimonprez", role = "ctb"),
person("Vincent", "Kubicki", role = "ctb"),
person("Samuel", "Blanck", role = "ctb"),
person("Jeremie","Kellner", role = "ctb")
)
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.
clere(>= 1.1.2), spikeslab(>= 1.1.5), parcor(>= 0.2), missMDA
(>= 1.7.3), tuneR, knitr, rmarkdown, Rmixmod(>= 2.0.1)
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]

Adaptive Shrinkage of Correlation and Covariance Matrices
Performs adaptive shrinkage of correlation and covariance
matrices using a mixture model prior over the Fisher
z-transformation of the correlations. A separate shrinkage
intensity may be specified for each cell of the correlation or the
covariance table.
ashr, reshape2, Matrix, gridExtra, corrplot, corpcor
Kushal Dey [aut, cre],
Matthew Stephens [aut],
Peter Carbonetto [ctb]