rwa: Perform a Relative Weights Analysis
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described
in Tonidandel & LeBreton (2015) <doi:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <doi:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
the total variance predicted in a regression model into weights that accurately reflect the proportional
contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.
| Version: |
1.0.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr, magrittr, stats, tidyr, ggplot2, boot, purrr, utils |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0), rlang, spelling |
| Published: |
2026-09-28 |
| DOI: |
10.32614/CRAN.package.rwa |
| Author: |
Martin Chan [aut, cre] |
| Maintainer: |
Martin Chan <martinchan53 at gmail.com> |
| BugReports: |
https://github.com/martinctc/rwa/issues |
| License: |
GPL-3 |
| URL: |
https://martinctc.github.io/rwa/, https://github.com/martinctc/rwa |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
rwa results |
Documentation:
Downloads:
Reverse dependencies:
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