Authors@R: 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"),
(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.
(>= 1.7.3), tuneR, knitr, rmarkdown, Rmixmod(>= 2.0.1)

Title: Adaptive Shrinkage of Correlation and Covariance Matrices
Description: 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
Imports: ashr, reshape2, Matrix, gridExtra, corrplot, corpcor

matrices using a mixture model prior over the Fisher z-transformation of the
correlations. It allows separate shrinkage intensity for each cell of the
correlation matrix. Can also we flexibly  extended to shrinking correlation like
quantities such as cosine similarities in word2vec models and to partial
correlations in conditional graph estimation. For details on methods, refer
to Stephens (2017) "False discovery rates: a new deal",
Imports: ashr, glmnet, SQUAREM, reshape2, methods, Matrix, gridExtra,
Suggests: knitr, glasso, roxygen2, devtools

Title: Adaptive Shrinkage of Correlation Vectors and Matrices
Author: person("Kushal", "Dey", email = "kkdey@uchicago.edu", role =
"Matthew", "Stephens", email = "mstephens@uchicago.edu", role = "aut")
Description: Performs adaptive shrinkage of correlation and covariance matrices
using a mixture model prior over the Fisher z-transformation of the correlations,
Stephens (2016) <doi:10.1093/biostatistics/kxw041> with the method flexible in choosing
a separate shrinkage intensity for each cell of the correlation or covariance
matrices: it is particularly efficient in handling missing data in the data matrix.
Imports: ashr, SQUAREM, reshape2, Matrix
Suggests: knitr, BiocStyle, corpcor, glasso, Biobase, roxygen2,