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Procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data

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MLGL

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The code was originally on an R-forge repository.

This package implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data.

Installation

From github:

library(devtools)
install_github("modal-inria/MLGL")

From CRAN:

install.packages("MLGL", repos = "https://cran.rstudio.com")

Dependencies

install.packages(c("gglasso", "MASS", "Matrix", "fastcluster", "FactoMineR", "parallelDist"), repos = "https://cran.rstudio.com")

Credits

MLGL is developed by Quentin Grimonprez, Guillemette Marot, Alain Celisse and Samuel Blanck.

Copyright Inria - Université de Lille

Licence

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

References

  • Q. Grimonprez, S. Blanck, A. Celisse, G. Marot, MLGL: An R package implementing correlated variable selection by hierarchical clustering and group-Lasso. https://hal.inria.fr/hal-01857242

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Procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data

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