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.