The R Journal: accepted article

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ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariates PDF download
María Xosé Rodríguez-Álvarez and Vanda Inácio

Abstract This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive fraction. For the Bayesian methods, we provide tools for assessing model fit via posterior predictive checks, while model choice can be carried out via several information criteria. Numerical and graphical outputs are provided for all methods. This is the only package implementing Bayesian procedures for ROC curves.

Received: 2020-10-30; online 2021-07-15
CRAN packages: sROC, pROC, nsROC, npROCRegression, OptimalCutpoints, ThresholdROC, ROCnReg, ggplot2, coda, moments, nor1mix, Matrix, spatstat, np, lattice, MASS, pbivnorm
CRAN Task Views implied by cited CRAN packages: Distributions, Econometrics, Multivariate, Graphics, NumericalMathematics, SocialSciences, TeachingStatistics, Bayesian, Cluster, Environmetrics, gR, Phylogenetics, Psychometrics, Robust, Spatial, SpatioTemporal, Survival


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-066,
  author = {María Xosé Rodríguez-Álvarez and Vanda Inácio},
  title = {{ROCnReg: An R Package for Receiver Operating Characteristic
          Curve Inference with and without Covariates}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-066},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-066/index.html}
}