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  "Title": "Variable Selection for Supervised Classification in High\nDimension",
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  "Author": "Emeline Perthame (Institut Pasteur, Paris, France), Chloe\nFriguet (Universite de Bretagne Sud, Vannes, France) and David\nCauseur (Agrocampus Ouest, Rennes, France)",
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  "Description": "The functions provided in the FADA (Factor Adjusted\nDiscriminant Analysis) package aim at performing supervised\nclassification of high-dimensional and correlated profiles. The\nprocedure combines a decorrelation step based on a factor\nmodeling of the dependence among covariates and a\nclassification method. The available methods are Lasso\nregularized logistic model (see Friedman et al. (2010)), sparse\nlinear discriminant analysis (see Clemmensen et al. (2011)),\nshrinkage linear and diagonal discriminant analysis (see M.\nAhdesmaki et al. (2010)). More methods of classification can be\nused on the decorrelated data provided by the package FADA.",
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