FADA: Variable selection for supervised classification in high dimension

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 adecorrelation 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.

Version: 1.2
Depends: MASS, elasticnet
Imports: sparseLDA, sda, glmnet, mnormt, crossval
Published: 2014-10-14
Author: Emeline Perthame (Agrocampus Ouest, Rennes, France), Chloe Friguet (Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus Ouest, Rennes, France)
Maintainer: David Causeur <david.causeur at agrocampus-ouest.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: FADA results


Reference manual: FADA.pdf
Package source: FADA_1.2.tar.gz
Windows binaries: r-devel: FADA_1.2.zip, r-release: FADA_1.2.zip, r-oldrel: FADA_1.2.zip
OS X Mavericks binaries: r-release: FADA_1.2.tgz, r-oldrel: FADA_1.2.tgz
Old sources: FADA archive