The receiver operating characteristic (ROC) curve is a graphical method which has become standard in the analysis of diagnostic markers, that is, in the study of the classification ability of a numerical variable. Most of the commercial statistical software provide routines for the standard ROC curve analysis. Of course, there are also many R packages dealing with the ROC estimation as well as other related problems. In this work we introduce the nsROC package which incorporates some new ROC curve procedures. Particularly: ROC curve comparison based on general distances among functions for both paired and unpaired designs; efficient confidence bands construction; a generalization of the curve considering different classification subsets than the one involved in the classical defini tion of the ROC curve; a procedure to deal with censored data in cumulative-dynamic ROC curve estimation for time-to-event outcomes; and a non-parametric ROC curve method for meta-analysis. This is the only R package which implements these particular procedures.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-043.zip
pROC, ROCR, plotROC, fbroc, OptimalCutpoints, timeROC, survivalROC, HSROC, nsROC, sde, tdROC, survival
Survival, ClinicalTrials, DifferentialEquations, Econometrics, Finance, MachineLearning, Multivariate, SocialSciences, TimeSeries
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For attribution, please cite this work as
Pérez-Fernández, et al., "nsROC: An R package for Non-Standard ROC Curve Analysis", The R Journal, 2018
BibTeX citation
@article{RJ-2018-043,
author = {Pérez-Fernández, Sonia and Martínez-Camblor, Pablo and Filzmoser, Peter and Corral, Norberto},
title = {nsROC: An R package for Non-Standard ROC Curve Analysis},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-043},
doi = {10.32614/RJ-2018-043},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {55-77}
}