vita: Variable Importance Testing Approaches

Implements the novel testing approach by Janitza et al.(2015) <> for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>. Janitza et al.(2015) <> do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental.

Version: 1.0.0
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.11.6), parallel, randomForest, stats
LinkingTo: Rcpp
Suggests: mnormt
Published: 2015-12-14
DOI: 10.32614/CRAN.package.vita
Author: Ender Celik [aut, cre]
Maintainer: Ender Celik <celik.p.ender at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: vita results


Reference manual: vita.pdf


Package source: vita_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): vita_1.0.0.tgz, r-oldrel (arm64): vita_1.0.0.tgz, r-release (x86_64): vita_1.0.0.tgz, r-oldrel (x86_64): vita_1.0.0.tgz

Reverse dependencies:

Reverse imports: MSclassifR, RFlocalfdr


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