Title: Variable selection for supervised classification in high
Author: Emeline Perthame (Agrocampus Ouest, Rennes, France), Chloe Friguet
(Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus
Description: The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing
supervised classification models and variable selection on dependent
covariates. The classification procedures are combined with a factor
modeling of dependence among covariates. The available procedures 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)).

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 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.

Title: Variable Selection for Supervised Classification in High
Author: Emeline Perthame (INRIA, Grenoble, France), Chloe Friguet
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

Author: Emeline Perthame (Institut Pasteur, Paris, France), Chloe Friguet

Title: Functional Data Clustering Using Adaptive Density Peak Detection
Description: An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).
Imports: MFPCA, cluster, fda, fda.usc, funData, stats, graphics