{ "_id": "6a10aa79acfb0bcc41cbf06d", "Package": "rwa", "Type": "Package", "Title": "Perform a Relative Weights Analysis", "Version": "0.1.2.9000", "Authors@R": "person(given = \"Martin\",\nfamily = \"Chan\",\nrole = c(\"aut\", \"cre\"),\nemail = \"martinchan53@gmail.com\")", "Description": "Perform a Relative Weights Analysis (RWA) (a.k.a. Key\nDrivers Analysis) as per the method described in Tonidandel &\nLeBreton (2015) , with its\noriginal roots in Johnson (2000)\n. In essence, RWA decomposes\nthe total variance predicted in a regression model into weights\nthat accurately reflect the proportional contribution of the\npredictor variables, which addresses the issue of\nmulti-collinearity. In typical scenarios, RWA returns similar\nresults to Shapley regression, but with a significant advantage\non computational performance.", "License": "GPL-3", "Encoding": "UTF-8", "URL": "https://martinctc.github.io/rwa/, https://github.com/martinctc/rwa", "BugReports": "https://github.com/martinctc/rwa/issues", "RoxygenNote": "7.3.2", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Language": "en-US", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://martinctc.r-universe.dev", "Date/Publication": "2026-01-22 12:16:46 UTC", "RemoteUrl": "https://github.com/martinctc/rwa", "RemoteRef": "HEAD", "RemoteSha": "e13908aa33dbcc83c78efed9986eb190d5a3127e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 18:50:04 UTC", "User": "root" }, "Author": "Martin Chan [aut, cre]", "Maintainer": "Martin Chan ", "MD5sum": "7df2eebeb227948cb4779d869da58a33", "_user": "martinctc", "_type": "src", "_file": "rwa_0.1.2.9000.tar.gz", 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