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GNU R package to prepare and display ROC curves

ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.

ROCR features: ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands).

Performance measures that ROCR knows: Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost.

Package version:1.0-4-3


Getting started with ROCR:

* After installation (cf. file 'INSTALL'), and starting R,
  load the package with 'library(ROCR)'.

* For a short overview of ROCR:

* For an overview of ROCR's online help: 

* ROCR help pages:


You need the R package 'RUnit' to run the unit tests for ROCR.
You can start the test suite by typing:
R --vanilla < unittests/runit.ROCR.R
from _this_ directory.


This file documents changes and updates to the ROCR package.

Version 1.0-4 (Dec 08, 2009)
- fixes bug with 1.0-3 that prevented plot arguments getting passed through

Version 1.0-3
- adapted to more strict R CMD CHECK rules in R > 2.9

Version 1.0-2 (Jan 27, 2007)
- fixed minor bug in 'prediction' function concerning the optional
  parameter 'label.ordering' (thanks to Robert Perdisci for notify


r-cran-rocr (1.0-4-3) unstable; urgency=low

  * Support depending on R pre-releases (debian/rules, 



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