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  "Title": "Dynamically Weighted Modified Maximum Likelihood (DWMML) Ridge\nRegression",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Filiz\", \"Karadag\", email = \"filiz.karadag@ege.edu.tr\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0116-7772\")),\nperson(\"Hakan Savas\", \"Sazak\", email = \"hakan.savas.sazak@ege.edu.tr\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6123-1214\")),\nperson(\"Olgun\", \"Aydin\", email = \"olgun.aydin@pg.edu.pl\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-7090-0931\")))",
  "Description": "Implements the dynamically weighted modified maximum\nlikelihood ridge (DWMMLR) regression estimator, a robust and\nmulticollinearity-aware linear regression estimator that\ncombines the DWMML3 weighting procedure of Sazak (2019)\n<doi:10.1080/00949655.2019.1571060> with ridge penalization to\naddress both outlier sensitivity and variance inflation due to\nmulticollinearity. The ridge parameter is selected\nautomatically using the approach implemented in the\n'ridgregextra' package (Karadag, Sazak, and Aydin, 2023)\n<https://CRAN.R-project.org/package=ridgregextra>, described\nfurther in Karadag, Sazak, and Aydin (2026)\n<doi:10.1080/02664763.2026.2655681>, which targets a variance\ninflation factor (VIF) close to but not below 1, removing the\nneed for manual tuning. Returns comprehensive outputs\n(coefficients, fitted values, residuals, mean squared error\n(MSE), standard errors, R-squared, and adjusted R-squared)\nthrough a simple x/y interface.",
  "License": "MIT + file LICENSE",
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