dwmmlRidge: Dynamically Weighted Modified Maximum Likelihood (DWMML) Ridge Regression

Implements the dynamically weighted modified maximum likelihood ridge (DWMMLR) regression estimator, a robust and multicollinearity-aware linear regression estimator that combines the DWMML3 weighting procedure of Sazak (2019) <doi:10.1080/00949655.2019.1571060> with ridge penalization to address both outlier sensitivity and variance inflation due to multicollinearity. The ridge parameter is selected automatically using the approach implemented in the 'ridgregextra' package (Karadag, Sazak, and Aydin, 2023) <https://CRAN.R-project.org/package=ridgregextra>, described further in Karadag, Sazak, and Aydin (2026) <doi:10.1080/02664763.2026.2655681>, which targets a variance inflation factor (VIF) close to but not below 1, removing the need for manual tuning. Returns comprehensive outputs (coefficients, fitted values, residuals, mean squared error (MSE), standard errors, R-squared, and adjusted R-squared) through a simple x/y interface.

Version:	0.1.1
Depends:	R (≥ 4.0.0)
Imports:	MASS, stats, Styperidge.reg
Suggests:	isdals, mctest
Enhances:	ridgregextra
Published:	2026-06-26
DOI:	10.32614/CRAN.package.dwmmlRidge
Author:	Filiz Karadag [aut, cre], Hakan Savas Sazak [aut], Olgun Aydin [aut]
Maintainer:	Filiz Karadag <filiz.karadag at ege.edu.tr>
BugReports:	https://github.com/filizkrdg/dwmmlRidge/issues
License:	MIT + file LICENSE
URL:	https://github.com/filizkrdg/dwmmlRidge
NeedsCompilation:	no
Materials:	README
CRAN checks:	dwmmlRidge results

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