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>.


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install.packages("armada")

0.1.0 by Aurelie Gueudin, 8 years ago


Browse source code at https://github.com/cran/armada


Authors: Aurelie Gueudin [aut, cre] , Anne Gegout-Petit [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports stats, mvtnorm, ClustOfVar, FAMT, graphics, VSURF, glmnet, anapuce, qvalue, parallel, doParallel, impute, ComplexHeatmap, circlize


See at CRAN