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ClusROC

ROC Analysis in Three-Class Classification Problems for Clustered Data

v1.0.3 · Oct 1, 2025 · GPL-3

Description

Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.

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Dependency Network

Dependencies Reverse dependencies nlme Rcpp rgl ellipse numDeriv ggplot2 ggpubr foreach iterators doParallel ClusROC

Version History

4 tracked
new 1.0.3 Mar 10, 2026
updated 1.0.3 ← 1.0.2 diff Sep 30, 2025
updated 1.0.2 ← 1.0.1 diff Nov 16, 2022
new 1.0.1 May 24, 2022