{ "_id": "6aaa686cea78b3e62eacc1be", "Package": "raiseR", "Type": "Package", "Title": "Raise Regression and Robust Methods for Multicollinearity", "Version": "0.1.0", "Authors@R": "person(given = \"Jinse\", family = \"Jacob\", email = \"jinsejacob@hotmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-0873-4864\"))", "Description": "Implements Raise Regression as an inference-preserving\nalternative to Ridge Regression for combating multicollinearity\nin linear models, including the classical single-variable Raise\nRegression, the Simultaneous Raise Regression (SRR) based on QR\ndecomposition and the Sequential Variance Inflation Factor\n(SVIF) of Jacob and Varadharajan (2022)\n, and the original raise\nparameter selection strategy of Jacob and Varadharajan (2023)\n. Also implements Robust Raise\nRegression for data contaminated by outliers, with exact\nfinite-sample inference (sandwich standard errors, Wald tests,\nSatterthwaite-corrected degrees of freedom) obtained by\ndown-weighting observations using Stahel-Donoho projection\noutlyingness and Tukey's biweight function. Provides ordinary\nand robust Ridge Regression (Hoerl and Kennard, 1970,\n), ordinary and robust Liu\nRegression (Liu, 1993, ), with\nthe robust variants of both based on the MM-estimates of Yohai\n(1987, ) and, for Liu Regression\nspecifically, the biasing-parameter derivation of Filzmoser and\nKurnaz (2018) . Also\nprovides the classical Variance Inflation Factor (VIF) and\nCondition Number (Belsley, 1991) computed from the correlation\nmatrix of the predictors, and the Robust Variance Inflation\nFactor (RVIF) and robust Condition Number of Jacob and\nVaradharajan (2024, Sankhya B,\n), which use the same\nprojection outlyingness and biweight down-weighting scheme to\nobtain a weighted correlation matrix that resists the influence\nof outliers. A flexible scaleDat() function supports classical\n(mean and standard deviation), robust weighted (Stahel-Donoho\nand Tukey biweight), median and Median Absolute Deviation\nNormalized (MADN, the median absolute deviation scaled by\n1.4826 to estimate the standard deviation under normality), and\nmin-max scaling. Diagnostic and goodness-of-fit plots, and the\nstandard influence-diagnostic suite (Cook's distance, DFBETAS\nand COVRATIO regression diagnostics) and heteroskedasticity\ntests (via the 'lmtest' and 'car' packages) analogous to those\nfor objects of class 'lm', are provided for the exact, unbiased\nRaise Regression fit.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "URL": "https://github.com/jinsejacob/raiseR", "BugReports": "https://github.com/jinsejacob/raiseR/issues", "Config/testthat/edition": "3", "Author": "Jinse Jacob [aut, cre]\n()", "Maintainer": "Jinse Jacob ", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://jinsejacob.r-universe.dev", "Date/Publication": "2026-09-05 07:26:36 UTC", "RemoteUrl": "https://github.com/jinsejacob/raiser", "RemoteRef": "HEAD", "RemoteSha": "f7a37f243ffad744cf58813702960e948d872443", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-16 09:55:38 UTC", "User": "root" }, "_user": "jinsejacob", "_type": "src", 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