ROC Analysis in Three-Class Classification Problems for Clustered Data R package ClusROC version 1.0.3 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. Software Depends: R (>= 3.5.0), stats, utils, graphics, nlme, Rcpp (>= 0.12.3) Imports: rgl, ellipse, numDeriv, ggplot2, ggpubr, foreach, iterators, parallel, doParallel LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0) Duc-Khanh To <tdkhanh@hcmus.edu.vn> Comprehensive R Archive Network (CRAN) Duc-Khanh To [aut, cre] (ORCID: <https://orcid.org/0000-0002-4641-0764>), Gianfranco Adimari [ctb], Monica Chiogna [ctb] GPL-3 2025-10-01 application/tgz https://CRAN.R-project.org/package=ClusROC doi:10.32614/CRAN.package.ClusROC