Package: vita
Type: Package
Title: Variable Importance Testing Approaches
Version: 1.0.0
Date: 2015-12-12
Authors@R: person("Ender", "Celik", email = "celik.p.ender@gmail.com",
                  role = c("aut", "cre"))
Author: Ender Celik [aut, cre]
Maintainer: Ender Celik <celik.p.ender@gmail.com>
Description: Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    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) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    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.
Depends: R (>= 3.1.0)
License: GPL (>= 2)
LazyData: TRUE
Imports: Rcpp (>= 0.11.6),parallel,randomForest,stats
LinkingTo: Rcpp
Suggests: mnormt
NeedsCompilation: yes
Packaged: 2015-12-14 17:52:38 UTC; ender
Repository: CRAN
Date/Publication: 2015-12-14 19:05:44
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 05:13:58 UTC; unix
Archs: vita.so.dSYM
