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FADA  

Variable Selection for Supervised Classification in High Dimension
View on CRAN: Click here


Download and install FADA package within the R console
Install from CRAN:
install.packages("FADA")

Install from Github:
library("remotes")
install_github("cran/FADA")

Install by package version:
library("remotes")
install_version("FADA", "1.3.5")



Attach the package and use:
library("FADA")
Maintained by
David Causeur
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-07-10
Latest Update: 2019-12-10
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.
How to cite:
David Causeur (2014). FADA: Variable Selection for Supervised Classification in High Dimension. R package version 1.3.5, https://cran.r-project.org/web/packages/FADA. Accessed 07 Oct. 2026.
Previous versions and publish date:
1.0 (2014-07-10 16:42), 1.1 (2014-09-01 12:59), 1.2 (2014-10-14 09:26), 1.3.1 (2016-05-07 00:48), 1.3.2 (2016-05-20 22:36), 1.3.3 (2018-02-07 12:27), 1.3.4 (2019-04-19 16:50), (2026-07-09 08:03)
Other packages that cited FADA R package
View FADA citation profile
Other R packages that FADA depends, imports, suggests or enhances
Complete documentation for FADA
Functions, R codes and Examples using the FADA R package
Some associated functions: FADA-package . FADA . data.test . data.train . decorrelate.test . decorrelate.train . 
Some associated R codes: FADA.R . decorrelate.test.R . decorrelate.train.R . func.R .  Full FADA package functions and examples
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