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",
    role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0873-4864"))
Description: Implements Raise Regression as an inference-preserving alternative to
    Ridge Regression for combating multicollinearity in linear models, including
    the classical single-variable Raise Regression, the Simultaneous Raise
    Regression (SRR) based on QR decomposition and the Sequential Variance
    Inflation Factor (SVIF) of Jacob and Varadharajan (2022)
    <doi:10.1007/s11135-022-01557-9>, and the original raise parameter selection
    strategy of Jacob and Varadharajan (2023) <doi:10.13189/ms.2023.110106>. Also
    implements Robust Raise Regression for data contaminated by outliers, with
    exact finite-sample inference (sandwich standard errors, Wald tests,
    Satterthwaite-corrected degrees of freedom) obtained by down-weighting
    observations using Stahel-Donoho projection outlyingness and Tukey's biweight
    function. Provides ordinary and robust Ridge Regression (Hoerl and Kennard,
    1970, <doi:10.1080/00401706.1970.10488634>), ordinary and robust Liu
    Regression (Liu, 1993, <doi:10.1080/03610929308831027>), with the robust
    variants of both based on the MM-estimates of Yohai (1987,
    <doi:10.1214/aos/1176350366>) and, for Liu Regression specifically, the
    biasing-parameter derivation of Filzmoser and Kurnaz (2018)
    <doi:10.1080/03610918.2016.1271889>. Also provides the classical Variance
    Inflation Factor (VIF) and Condition Number (Belsley, 1991) computed from
    the correlation matrix of the predictors, and the Robust Variance Inflation
    Factor (RVIF) and robust Condition Number of Jacob and Varadharajan (2024,
    Sankhya B, <doi:10.1007/s13571-024-00342-y>), which use the same projection
    outlyingness and biweight down-weighting scheme to obtain a weighted
    correlation matrix that resists the influence of outliers. A flexible
    scaleDat() function supports classical (mean and standard deviation), robust
    weighted (Stahel-Donoho and Tukey biweight), median and Median Absolute
    Deviation Normalized (MADN, the median absolute deviation scaled by 1.4826
    to estimate the standard deviation under normality), and min-max scaling.
    Diagnostic and goodness-of-fit plots, and the standard
    influence-diagnostic suite (Cook's distance, DFBETAS and COVRATIO regression
    diagnostics) and heteroskedasticity tests (via
    the 'lmtest' and 'car' packages) analogous to those for objects of class
    'lm', are provided for the exact, unbiased Raise Regression fit.
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.3.1
Depends: R (>= 3.5.0)
Imports: stats, graphics, grDevices, mrfDepth, MASS, withr
Suggests: testthat (>= 3.0.0), lmtest, car
URL: https://github.com/jinsejacob/raiseR
BugReports: https://github.com/jinsejacob/raiseR/issues
Config/testthat/edition: 3
Author: Jinse Jacob [aut, cre] (ORCID: <https://orcid.org/0000-0002-0873-4864>)
Maintainer: Jinse Jacob <jinsejacob@hotmail.com>
NeedsCompilation: no
Packaged: 2026-09-05 05:56:09 UTC; root
Repository: CRAN
Date/Publication: 2026-09-15 10:50:07 UTC
Built: R 4.5.2; ; 2026-09-15 11:13:24 UTC; unix
