Model-Independent Variable Selection via the Rule-Based Variable Priority

A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.


Reference manual

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install.packages("varPro")

3.3.0 by Udaya B. Kogalur, a month ago


https://www.varprotools.org/ https://www.luminwin.net/ https://ishwaran.org/


Report a bug at https://github.com/kogalur/varPro/issues/


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


Authors: Min Lu [aut] , Aster K. Shear [aut] , Udaya B. Kogalur [aut, cre] , Hemant Ishwaran [aut]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports randomForestSRC, glmnet, parallel, foreach, gbm, BART, survival

Suggests mlbench, doMC, caret, MASS, igraph

System requirements: OpenMP


Imported by ggRandomForests, randomForestRHF, randomForestSGT.


See at CRAN