Package: ROCR
Title: Visualizing the Performance of Scoring Classifiers
Version: 1.0-7
Date: 2015-03-26
Depends: gplots, methods
Author: Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer
Description: 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-parameterized 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 parameterization 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.
Maintainer: Tobias Sing <tobias.sing@gmail.com>
License: GPL (>= 2)
URL: http://rocr.bioinf.mpi-sb.mpg.de/
Packaged: 2015-03-26 10:34:10 UTC; singto1
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-03-26 17:12:17
Built: R 3.4.1; ; 2017-08-21 01:38:05 UTC; windows
