**Data Frame Operations on Sparse and Dense Matrix Objects** (**Matrix.utils**)

Implements cast, aggregate, and merge/join for Matrix and matrix-like objects.

**Various Methods to Estimate the AUC** (**auRoc**)

Estimate the AUC using a variety of methods as follows: (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. (2) frequentist parametric methods using the likelihood ratio test based on higher-order asymptotic results, the signed log-likelihood ratio test, the Wald test, or the approximate ”t” solution to the Behrens-Fisher problem. (3) Bayesian parametric MCMC methods.

**Variable Importance Testing Approaches** (**vita**)

Implements the novel testing approach by Janitza et al.(2015) <http://…ver.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://…ver.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.

### Like this:

Like Loading...