armada: A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence

Two steps variable selection procedure in a context of high-dimensional dependent data but few observations. First step is dedicated to eliminate dependence between variables (clustering of variables, followed by factor analysis inside each cluster). Second step is a variable selection using by aggregation of adapted methods. Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment. 2018. <https://hal.archives-ouvertes.fr/hal-01939694>.

Version: 0.1.0
Imports: stats, mvtnorm, ClustOfVar, FAMT, graphics, VSURF, glmnet, anapuce, qvalue, parallel, doParallel, impute, ComplexHeatmap, circlize
Published: 2019-04-04
Author: Aurelie Gueudin [aut, cre], Anne Gegout-Petit [aut]
Maintainer: Aurelie Gueudin <aurelie.gueudin at univ-lorraine.fr>
License: GPL-3
NeedsCompilation: no
CRAN checks: armada results

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Reference manual: armada.pdf
Package source: armada_0.1.0.tar.gz
Windows binaries: r-devel: armada_0.1.0.zip, r-devel-gcc8: armada_0.1.0.zip, r-release: armada_0.1.0.zip, r-oldrel: armada_0.1.0.zip
OS X binaries: r-release: armada_0.1.0.tgz, r-oldrel: armada_0.1.0.tgz

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