pROC: Display and Analyze ROC Curves

Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

Version: 1.10.0
Depends: R (≥ 2.14)
Imports: plyr, utils, methods, Rcpp (≥ 0.11.1), ggplot2
LinkingTo: Rcpp
Suggests: microbenchmark, tcltk, MASS, logcondens, doParallel, testthat
Published: 2017-06-10
Author: Xavier Robin [cre, aut], Natacha Turck [aut], Alexandre Hainard [aut], Natalia Tiberti [aut], Frédérique Lisacek [aut], Jean-Charles Sanchez [aut], Markus Müller [aut], Stefan Siegert [ctb] (Fast DeLong code)
Maintainer: Xavier Robin <robin at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: pROC citation info
Materials: README NEWS
CRAN checks: pROC results


Reference manual: pROC.pdf
Package source: pROC_1.10.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: pROC_1.10.0.tgz
OS X Mavericks binaries: r-oldrel: pROC_1.10.0.tgz
Old sources: pROC archive

Reverse dependencies:

Reverse depends: bimixt, FRESA.CAD, RatingScaleReduction, RcmdrPlugin.ROC, roccv
Reverse imports: Biocomb, biomod2, BioPET, biospear, blkbox, BootValidation, chemmodlab, ebmc, EFS, elo, FAMILY, healthcareai, LANDD, LEGIT, LogisticDx, LOGIT, mcca, mlDNA, quantable, randomUniformForest, reportROC, SCGLR, stepPenal, ThresholdROC, tpAUC, TrendInTrend, yardstick
Reverse suggests: aplore3, arsenal, bst, caret, caretEnsemble, Causata, dtree, eclust, ensemblepp, fscaret, kernDeepStackNet, mldr, mlr, OSTSC, prioritylasso, RcmdrPlugin.EZR, riskRegression, sjstats, waffect


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