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: 0.1.0
Imports: dplyr, magrittr, stats, tidyr, ggplot2, boot, purrr, utils
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), rlang, spelling
Published: 2025-07-16
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:

Reference manual: rwa.html , rwa.pdf
Vignettes: Bootstrap Confidence Intervals for Relative Weights Analysis (source, R code)
Evaluating the Tonidandel & LeBreton Relative Weights Analysis Method (source, R code)
Introduction to Relative Weights Analysis with the rwa Package (source, R code)

Downloads:

Package source: rwa_0.1.0.tar.gz
Windows binaries: r-devel: rwa_0.0.3.zip, r-release: rwa_0.0.3.zip, r-oldrel: rwa_0.0.3.zip
macOS binaries: r-release (arm64): rwa_0.0.3.tgz, r-oldrel (arm64): rwa_0.0.3.tgz, r-release (x86_64): rwa_0.1.0.tgz, r-oldrel (x86_64): rwa_0.1.0.tgz
Old sources: rwa archive

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