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

An implementation of the selectboost algorithm (Aouadi et al. 2018, <arXiv:1810.01670>), 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.

Version: 1.4.0
Depends: R (≥ 2.10)
Imports: lars, glmnet, igraph, parallel, msgps, Rfast, methods, Cascade, graphics, grDevices
Suggests: knitr, rmarkdown, mixOmics, CascadeData
Published: 2019-05-27
Author: Frederic Bertrand ORCID iD [cre, aut], Myriam Maumy-Bertrand ORCID iD [aut], Ismail Aouadi [ctb], Nicolas Jung [ctb]
Maintainer: Frederic Bertrand <frederic.bertrand at>
License: GPL-3
NeedsCompilation: no
Classification/MSC: 62H11, 62J12, 62J99
Citation: SelectBoost citation info
Materials: NEWS
CRAN checks: SelectBoost results


Reference manual: SelectBoost.pdf
Vignettes: Benchmarking the SelectBoost Package for Network Reverse Engineering
Towards confidence estimates in Cascade Networks using the SelectBoost package
Simulation Tools Provided With the Selectboost Package
Package source: SelectBoost_1.4.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: SelectBoost_1.4.0.tgz, r-oldrel: SelectBoost_1.4.0.tgz

Reverse dependencies:

Reverse imports: Patterns


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