pkg/DESCRIPTION

   1 Package: valse
   2 Title: Variable Selection with Mixture of Models
   3 Date: 2020-03-11
   4 Version: 0.1-0
   5 Description: Two methods are implemented to cluster data with finite mixture
   6     regression models. Those procedures deal with high-dimensional covariates and
   7     responses through a variable selection procedure based on the Lasso estimator.
   8     A low-rank constraint could be added, computed for the Lasso-Rank procedure.
   9     A collection of models is constructed, varying the level of sparsity and the
  10     number of clusters, and a model is selected using a model selection criterion
  11     (slope heuristic, BIC or AIC). Details of the procedure are provided in 'Model-
  12     based clustering for high-dimensional data. Application to functional data' by
  13     Emilie Devijver, published in Advances in Data Analysis and Clustering (2016).
  14 Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
  15     Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
  16     Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
  17 Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
  18 Depends:
  19     R (>= 3.5.0)
  20 Imports:
  21     MASS,
  22     parallel,
  23     ggplot2,
  24     cowplot,
  25     reshape2
  26 Suggests:
  27     capushe,
  28     roxygen2
  29 URL: http://git.auder.net/?p=valse.git
  30 License: MIT + file LICENSE
  31 RoxygenNote: 7.0.2
  32 Collate:
  33     'plot_valse.R'
  34     'main.R'
  35     'selectVariables.R'
  36     'constructionModelesLassoRank.R'
  37     'constructionModelesLassoMLE.R'
  38     'computeGridLambda.R'
  39     'initSmallEM.R'
  40     'EMGrank.R'
  41     'EMGLLF.R'
  42     'generateXY.R'
  43     'A_NAMESPACE.R'
  44     'util.R'