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  "Title": "Measures of Uncertainty for Model Selection",
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  "Authors@R": "c(person(\"Yuanyuan\", \"Li\", role = c(\"aut\", \"cre\"),\nemail = \"yynli9696@gmail.com\"),\nperson(\"Jiming\", \"Jiang\", role = \"ths\"))",
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  "Description": "Following the common types of measures of uncertainty for\nparameter estimation, two measures of uncertainty were proposed\nfor model selection, see Liu, Li and Jiang (2020)\n<doi:10.1007/s11749-020-00737-9>. The first measure is a kind\nof model confidence set that relates to the variation of model\nselection, called Mac. The second measure focuses on error of\nmodel selection, called LogP. They are all computed via\nbootstrapping. This package provides functions to compute these\ntwo measures. Furthermore, a similar model confidence set\nadapted from Bayesian Model Averaging can also be computed\nusing this package.",
  "License": "GPL (>= 3)",
  "URL": "https://github.com/YuanyuanLi96/maclogp",
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  "Date/Publication": "2022-11-25 03:27:45 UTC",
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      "title": "Diabetes data",
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