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  "Title": "Slab and Shrinkage Linear Regression Estimation",
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  "Authors@R": "c(person(given = \"Ziwei\", family = \"Chen\", role = c(\"aut\", \"cre\"),\nemail = \"Ziwei.Chen.3@citystgeorges.ac.uk\",\ncomment = c(ORCID = \"0009-0009-6376-3850\")),\nperson(given = \"Vali\", family = \"Asimit\", role = \"aut\",\nemail = \"asimit@citystgeorges.ac.uk\",\ncomment = c(ORCID = \"0000-0002-7706-0066\")),\nperson(given = \"Marina Anca\", family = \"Cidota\", role = \"aut\",\nemail = \"cidota@fmi.unibuc.ro\",\ncomment = c(ORCID = \"0009-0004-9505-7233\")),\nperson(given = \"Jennifer\", family = \"Asimit\", role = \"aut\",\nemail = \"jennifer.asimit@mrc-bsu.cam.ac.uk\",\ncomment = c(ORCID = \"0000-0002-4857-2249\")))",
  "Description": "Implements a suite of shrinkage estimators for\nmultivariate linear regression to improve estimation stability\nand predictive accuracy. Provides methods including the Stein\nestimator, Diagonal Shrinkage, the general Shrinkage estimator\n(solving a Sylvester equation), and Slab Regression (Simple and\nGeneralized). These methods address Stein's paradox by\nintroducing structured bias to reduce variance without\nrequiring cross-validation, except for 'ShrinkageRR' where the\nintensity is chosen by minimizing an explicit Mean Squared\nError (MSE) criterion. Methods are based on Asimit, V., Cidota,\nM. A., Chen, Z., and Asimit, J. (2025)\n<https://openaccess.city.ac.uk/id/eprint/35005/>.",
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  "Date/Publication": "2026-03-18 16:50:39 UTC",
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      "filename": "savvySh.html",
      "title": "savvySh: Shrinkage Methods for Linear Regression Estimation",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Theoretical Overview",
        "Multiplicative Shrinkage",
        "Slab Regression",
        "Linear Shrinkage",
        "Shrinkage Ridge Regression",
        "Summary of Shrinkage Estimators",
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