{ "_id": "6abe2ff199f7aa8c6aa536c9", "Package": "ClusROC", "Type": "Package", "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(), with contributions\nfrom Gianfranco Adimari and Monica Chiogna", "Maintainer": "Duc-Khanh To ", "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) . 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. Visualization tools are also\nprovided. 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