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.

Package details

AuthorFrederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Ismail Aouadi [ctb], Nicolas Jung [ctb]
MaintainerFrederic Bertrand <[email protected]>
LicenseGPL-3
Version1.4.0
URL https://github.com/fbertran/SelectBoost http://www-irma.u-strasbg.fr/~fbertran/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SelectBoost")

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SelectBoost documentation built on May 27, 2019, 5:01 p.m.