A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

An implementation of the selectboost algorithm (Bertrand et al. 2020, ), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.


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install.packages("SelectBoost")

2.0.0 by Frederic Bertrand, 2 days ago


https://github.com/fbertran/SelectBoost, http://www-irma.u-strasbg.fr/~fbertran/


Report a bug at https://github.com/fbertran/SelectBoost/issues


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


Authors: Frederic Bertrand [cre, aut] , Myriam Maumy-Bertrand [aut] , Ismail Aouadi [ctb] , Nicolas Jung [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports lars, glmnet, igraph, parallel, msgps, Rfast, methods, Cascade, graphics, grDevices, varbvs, spls, abind

Suggests knitr, rmarkdown, mixOmics, CascadeData


Imported by Patterns.


See at CRAN