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ClusROC  

ROC Analysis in Three-Class Classification Problems for Clustered Data
View on CRAN: Click here


Download and install ClusROC package within the R console
Install from CRAN:
install.packages("ClusROC")

Install from Github:
library("remotes")
install_github("cran/ClusROC")

Install by package version:
library("remotes")
install_version("ClusROC", "1.0.2")



Attach the package and use:
library("ClusROC")
Maintained by
Duc-Khanh To
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-25
Latest Update: 2022-11-17
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) . 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) . Visualization tools are also provided. We refer readers to the articles cited above for all details.
How to cite:
Duc-Khanh To (2022). ClusROC: ROC Analysis in Three-Class Classification Problems for Clustered Data. R package version 1.0.2, https://cran.r-project.org/web/packages/ClusROC. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.1 (2022-05-25 09:40)
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