lars, glmnet, igraph, parallel, msgps, Rfast, methods,
Cascade, graphics, grDevices, varbvs, spls, abind
An implementation of the selectboost algorithm (Bertrand et al. 2020, <arXiv:1810.01670>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

knitr, markdown, rmarkdown, mixOmics, CascadeData

c(
person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@utt.fr", comment = c(ORCID = "0000-0002-0837-8281")),
person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@utt.fr", comment = c(ORCID = "0000-0002-4615-1512")),
person(given = "Ismail", family= "Aouadi", role = c("ctb"), email = "i.aouadi@unistra.fr"),
person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))

Regularization for variable selection in model-based clustering
and discriminant analysis.
Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau
Regularization for variable selection in model-based clustering and discriminant analysis
Rcpp (>= 0.11.1), glasso, parallel, Rmixmod

Regularization for Variable Selection in Model-Based Clustering
and Discriminant Analysis
Performs a regularization approach to variable selection in the
model-based clustering and classification frameworks.
First, the variables are arranged in order with a lasso-like procedure.
Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011)
is adapted to define the role of variables in the two frameworks.
Rcpp (>= 0.11.1), glasso, parallel, Rmixmod, methods