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: 1.9.1
Depends: R (≥ 2.14)
Imports: plyr, utils, methods, Rcpp (≥ 0.11.1)
LinkingTo: Rcpp
Suggests: microbenchmark, tcltk, MASS, logcondens, doParallel, testthat
Published: 2017-02-05
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 lindinglab.org>
License: GPL (≥ 3)
URL: http://expasy.org/tools/pROC/
NeedsCompilation: yes
Citation: pROC citation info
Materials: README NEWS
CRAN checks: pROC results

Downloads:

Reference manual: pROC.pdf
Package source: pROC_1.9.1.tar.gz
Windows binaries: r-devel: pROC_1.9.1.zip, r-release: pROC_1.9.1.zip, r-oldrel: pROC_1.9.1.zip
OS X El Capitan binaries: r-release: pROC_1.9.1.tgz
OS X Mavericks binaries: r-oldrel: pROC_1.9.1.tgz
Old sources: pROC archive

Reverse dependencies:

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

Linking:

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