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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