# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "rwa" in publications use:' type: software license: GPL-3.0-only title: 'rwa: Perform a Relative Weights Analysis' version: 0.0.3 doi: 10.32614/CRAN.package.rwa abstract: 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. authors: - family-names: Chan given-names: Martin email: martinchan53@gmail.com repository: https://martinctc.r-universe.dev repository-code: https://github.com/martinctc/rwa commit: 7980b82aef19e276d3f38ccb45f38b5bcd8dd0d3 url: https://github.com/martinctc/rwa contact: - family-names: Chan given-names: Martin email: martinchan53@gmail.com