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  "Package": "ClusROC",
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  "Title": "ROC Analysis in Three-Class Classification Problems for\nClustered Data",
  "Date": "2022-11-17",
  "Version": "1.0.2",
  "Author": "Duc-Khanh To [aut, cre]\n(<https://orcid.org/0000-0002-4641-0764>), with contributions\nfrom Gianfranco Adimari and Monica Chiogna",
  "Maintainer": "Duc-Khanh To <toduc@stat.unipd.it>",
  "Description": "Statistical methods for ROC surface analysis in\nthree-class classification problems for clustered data and in\npresence of covariates. In particular, the package allows to\nobtain covariate-specific point and interval estimation for:\n(i) true class fractions (TCFs) at fixed pairs of thresholds;\n(ii) the ROC surface; (iii) the volume under ROC surface (VUS);\n(iv) the optimal pairs of thresholds. Methods considered in\npoints (i), (ii) and (iv) are proposed and discussed in To et\nal. (2022) <doi:10.1177/09622802221089029>. Referring to point\n(iv), three different selection criteria are implemented:\nGeneralized Youden Index (GYI), Closest to Perfection (CtP) and\nMaximum Volume (MV). Methods considered in point (iii) are\nproposed and discussed in Xiong et al. (2018)\n<doi:10.1177/0962280217742539>. Visualization tools are also\nprovided. We refer readers to the articles cited above for all\ndetails.",
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  "Date/Publication": "2022-11-17 15:03:13 UTC",
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      "title": "ROC Analysis in Three-Class Classification Problems for Clustered Data",
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        "ClusROC"
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      "topics": [
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      "topics": [
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