Package: FisherEM
Type: Package
Title: The Fisher-EM algorithm
Version: 1.4
Date: 2013-06-21
Author: Charles Bouveyron and Camille Brunet
Maintainer: Camille Brunet <camille.brunet@gmail.com>
Depends: MASS, elasticnet
Description: The FisherEM package provides an efficient algorithm for
the unsupervised classification of high-dimensional data. This
FisherEM algorithm models and clusters the data in a
discriminative and low-dimensional latent subspace. It also
provides a low-dimensional representation of the clustered
data. A sparse version of Fisher-EM algorithm is also provided.
License: GPL-2
LazyLoad: yes
Packaged: 2013-06-28 08:20:50 UTC; charles
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-28 18:44:30
Package: funFEM
Type: Package
Title: Clustering in the Discriminative Functional Subspace
Version: 1.1
Date: 2015-03-05
Author: Charles Bouveyron
Depends: R (>= 2.10), MASS, fda, elasticnet
Maintainer: Charles Bouveyron <charles.bouveyron@parisdescartes.fr>
Description: The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
License: GPL-2
LazyLoad: yes
Packaged: 2015-03-13 10:22:39 UTC; charles
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-03-13 11:47:30
Package: FADA
Type: Package
Title: Variable Selection for Supervised Classification in High
Dimension
Version: 1.3.2
Date: 2016-05-12
Author: Emeline Perthame (INRIA, Grenoble, 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: 2016-05-20 13:46:22 UTC; Emeline
Repository: CRAN
Date/Publication: 2016-05-20 22:36:50
Package: regsel
Type: Package
Title: Variable Selection and Regression
Version: 0.2
Date: 2016-02-24
Author: Michal Knut
Maintainer: Michal Knut <1105406k@student.gla.ac.uk>
Description: Functions for fitting linear and generalized linear models with variable selection. The functions can automatically do Stepwise Regression, Lasso or Elastic Net as variable selection methods. Lasso and Elastic net are improved and handle factors better (they can either include or exclude all factor levels).
License: GPL-2
LazyData: TRUE
RoxygenNote: 5.0.1
Depends: glmnet, elasticnet
NeedsCompilation: no
Packaged: 2016-03-07 13:27:49 UTC; knutm
Repository: CRAN
Date/Publication: 2016-03-09 09:13:48