A statistical method for reducing the number of covariates in an analysis by evaluating Variable Importance Measures (VIMPs) derived from the Random Forest algorithm. It performs statistical tests on the VIMPs and outputs whether the covariate is significant along with the p-values.
| Version: | 1.0.2 |
| Imports: | dplyr,ggforce,ggplot2,ggpubr,magrittr, parallel,patchwork,ranger,rlang, stats,stringr,tidyr |
| Suggests: | knitr,rmarkdown,spelling,testthat (≥ 3.0.0) |
| Published: | 2025-06-19 |
| DOI: | 10.32614/CRAN.package.shadowVIMP |
| Author: | Tim Mueller [aut], Oktawia Miluch [aut, cre], Staburo GmbH [cph, fnd] |
| Maintainer: | Oktawia Miluch <oktawia.miluch at staburo.de> |
| BugReports: | https://github.com/OktawiaStaburo/shadowVIMP/issues |
| License: | Apache License (≥ 2) |
| URL: | https://github.com/OktawiaStaburo/shadowVIMP |
| NeedsCompilation: | no |
| Language: | en-GB |
| Materials: | README,NEWS |
| CRAN checks: | shadowVIMP results |