Variable Importance Measures for Multivariate Random Forests R package MulvariateRandomForestVarImp version 0.0.2 Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test. Software Depends: R (>= 2.10) Imports: MultivariateRandomForest (>= 1.1.5), MASS (>= 7.3.0) Suggests: testthat (>= 3.0.0) Dogonadze Nika <nika.dogonadze@toptal.com> Comprehensive R Archive Network (CRAN) Sikdar Sharmistha [aut], Hooker Giles [aut], Kadiyali Vrinda [ctb], Dogonadze Nika [cre] GPL (>= 3) 2021-12-15 application/tgz https://CRAN.R-project.org/package=MulvariateRandomForestVarImp doi:10.32614/CRAN.package.MulvariateRandomForestVarImp