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rwa  

Perform a Relative Weights Analysis
View on CRAN: Click here


Download and install rwa package within the R console
Install from CRAN:
install.packages("rwa")

Install from Github:
library("remotes")
install_github("cran/rwa")

Install by package version:
library("remotes")
install_version("rwa", "0.1.1")



Attach the package and use:
library("rwa")
Maintained by
Martin Chan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-24
Latest Update: 2025-07-16
Description:
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described in Tonidandel & LeBreton (2015) , with its original roots in Johnson (2000) . 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.
How to cite:
Martin Chan (2020). rwa: Perform a Relative Weights Analysis. R package version 0.1.1, https://cran.r-project.org/web/packages/rwa. Accessed 07 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:54), 0.0.3 (2020-11-24 13:10), 0.1.0 (2025-07-16 17:20)
Other packages that cited rwa R package
View rwa citation profile
Other R packages that rwa depends, imports, suggests or enhances
Complete documentation for rwa
Functions, R codes and Examples using the rwa R package
Some associated functions: pipe . plot_rwa . remove_all_na_cols . rwa . 
Some associated R codes: globals.R . plot_rwa.R . remove_all_na_cols.R . rwa.R . utils-pipe.R .  Full rwa package functions and examples
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