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dwmmlRidge  

Dynamically Weighted Modified Maximum Likelihood (DWMML) Ridge Regression
View on CRAN: Click here


Download and install dwmmlRidge package within the R console
Install from CRAN:
install.packages("dwmmlRidge")

Install from Github:
library("remotes")
install_github("cran/dwmmlRidge")

Install by package version:
library("remotes")
install_version("dwmmlRidge", "0.1.1")



Attach the package and use:
library("dwmmlRidge")
Maintained by
Filiz Karadag
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-26
Latest Update: 2026-06-26
Description:
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.
How to cite:
Filiz Karadag (2026). dwmmlRidge: Dynamically Weighted Modified Maximum Likelihood (DWMML) Ridge Regression. R package version 0.1.1, https://cran.r-project.org/web/packages/dwmmlRidge. Accessed 25 Oct. 2026.
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