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.

Documentation

Manual: pROC.pdf
Vignette: None available.

Maintainer: Xavier Robin <robin at lindinglab.org>

Author(s): Xavier Robin*, Natacha Turck*, Alexandre Hainard*, Natalia Tiberti*, Frédérique Lisacek*, Jean-Charles Sanchez*, Markus Müller*, Stefan Siegert* (Fast DeLong code)

Install package and any missing dependencies by running this line in your R console:

install.packages("pROC")

Depends R (>= 2.14)
Imports plyr, utils, methods, Rcpp(>=0.11.1)
Suggests microbenchmark, tcltk, MASS, logcondens, doParallel, testthat
Enhances
Linking to Rcpp

Package pROC
Materials
URL http://expasy.org/tools/pROC/
Task Views
Version 1.9.1
Published 2017-02-05
License GPL (>= 3)
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks pROC check results
Package source pROC_1.9.1.tar.gz