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.


Reference manual

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2.0.0 by Frederic Bertrand, 8 months 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