The R Journal: article published in 2018, volume 10:2

nsROC: An R package for Non-Standard ROC Curve Analysis PDF download
Sonia Pérez-Fernández, Pablo Martínez-Camblor, Peter Filzmoser and Norberto Corral , The R Journal (2018) 10:2, pages 55-77.

Abstract 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.

Received: 2017-06-27; online 2018-08-17, supplementary material, (1.4 Kb)
CRAN packages: pROC, ROCR, plotROC, fbroc, OptimalCutpoints, timeROC, survivalROC, HSROC, nsROC, sde, tdROC, survival
CRAN Task Views implied by cited CRAN packages: Survival, ClinicalTrials, DifferentialEquations, Econometrics, Finance, MachineLearning, Multivariate, SocialSciences, TimeSeries


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@article{RJ-2018-043,
  author = {Sonia Pérez-Fernández and Pablo Martínez-Camblor and Peter
          Filzmoser and Norberto Corral},
  title = {{nsROC: An R package for Non-Standard ROC Curve Analysis}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-043},
  url = {https://doi.org/10.32614/RJ-2018-043},
  pages = {55--77},
  volume = {10},
  number = {2}
}