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.
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[aut, cre],
Hakan Savas Sazak