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  "Title": "Global, Parameterwise and Joint Shrinkage Factor Estimation",
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  "Authors@R": "c(person(\"Daniela\", \"Dunkler\", email = \"daniela.dunkler@meduniwien.ac.at\", role = c(\"aut\", \"cre\")),\nperson(\"Georg\", \"Heinze\", role = \"aut\"))",
  "Description": "The predictive value of a statistical model can often be\nimproved by applying shrinkage methods. This can be achieved,\ne.g., by regularized regression or empirical Bayes approaches.\nVarious types of shrinkage factors can also be estimated after\na maximum likelihood. While global shrinkage modifies all\nregression coefficients by the same factor, parameterwise\nshrinkage factors differ between regression coefficients. With\nvariables which are either highly correlated or associated with\nregard to contents, such as several columns of a design matrix\ndescribing a nonlinear effect, parameterwise shrinkage factors\nare not interpretable and a compromise between global and\nparameterwise shrinkage, termed 'joint shrinkage', is a useful\nextension. A computational shortcut to resampling-based\nshrinkage factor estimation based on DFBETA residuals can be\napplied. Global, parameterwise and joint shrinkage for models\nfitted by lm(), glm(), coxph(), or mfp() is available.",
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