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: |
1.0.3 |
| Imports: |
dplyr, ggforce, ggplot2, ggpubr, magrittr, parallel, patchwork, ranger, rlang, stats, stringr, tidyr |
| Suggests: |
knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2026-09-10 |
| DOI: |
10.32614/CRAN.package.shadowVIMP |
| Author: |
Tim Mueller [aut, cre],
Oktawia Miluch [aut],
Staburo GmbH [cph, fnd] |
| Maintainer: |
Tim Mueller <mueller at staburo.de> |
| BugReports: |
https://github.com/Staburo/shadowVIMP/issues |
| License: |
Apache License (≥ 2) |
| URL: |
https://github.com/Staburo/shadowVIMP |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README, NEWS |
| CRAN checks: |
shadowVIMP results |