Package: FADA
Type: Package
Title: Variable Selection for Supervised Classification in High
        Dimension
Version: 1.3.5
Date: 2019-12-10
Author: Emeline Perthame (Institut Pasteur, Paris, France), Chloe
        Friguet (Universite de Bretagne Sud, Vannes, France) and David
        Causeur (Agrocampus Ouest, Rennes, France)
Maintainer: David Causeur <david.causeur@agrocampus-ouest.fr>
Description: The functions provided in the FADA (Factor Adjusted
        Discriminant Analysis) package aim at performing supervised
        classification of high-dimensional and correlated profiles. The
        procedure combines a decorrelation step based on a factor
        modeling of the dependence among covariates and a
        classification method. The available methods are Lasso
        regularized logistic model (see Friedman et al. (2010)), sparse
        linear discriminant analysis (see Clemmensen et al. (2011)),
        shrinkage linear and diagonal discriminant analysis (see M.
        Ahdesmaki et al. (2010)). More methods of classification can be
        used on the decorrelated data provided by the package FADA.
License: GPL (>= 2)
Depends: MASS, elasticnet
Imports: sparseLDA,sda,glmnet,mnormt,crossval,corpcor,
        matrixStats,methods
NeedsCompilation: no
Packaged: 2026-07-04 02:09:09 UTC; root
Repository: https://dcauseur.r-universe.dev
Date/Publication: 2019-12-10 14:30:05 UTC
RemoteUrl: https://github.com/cran/FADA
RemoteRef: HEAD
RemoteSha: 784d171073ca5bbfe1d4c4511d736740c47e142e