ROC Analysis in Three-Class Classification Problems for Clustered Data.

ClusROC

INTRODUCTION

The R package ClusROC This package implements the techniques for ROC surface analysis, in cases of clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:

• [x] true class fractions (TCFs) at fixed pairs of thresholds;
• [x] the ROC surface;
• [x] the optimal pairs of thresholds;
• [x] the volume under ROC surface (VUS).

Methods considered for the first three quantities are proposed and discussed in To et al. (2022). Referring to the third, three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered for the last 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.

INSTALLATION

The latest release of package can be installed directly from CRAN by using the code below:

# Install latest release from CRAN
install.packages("ClusROC")

To install the latest development version from Github, you’ll need to do a source install. Those aren’t easy! Try

# Install development version from Github
library(devtools)
install_github("toduckhanh/ClusROC")