Package: ROCR
Title: Visualizing the performance of scoring classifiers.
Version: 1.0-5
Date: 2013-05-12
Depends: R (>= 2.10), 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: ORPHANED
License: GPL (>= 2)
URL: http://rocr.bioinf.mpi-sb.mpg.de/
Packaged: 2014-12-07 16:02:12 UTC; hornik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-12-07 17:23:11
X-CRAN-Comment: Orphaned on 2014-12-07 as maintainer address
<tobias.sing@mpi-sb.mpg.de> bounced.
X-CRAN-Original-Maintainer: Tobias Sing <tobias.sing@mpi-sb.mpg.de>

Package: ROCR
Title: Visualizing the Performance of Scoring Classifiers
Version: 1.0-6
Date: 2015-03-22
Depends: R (>= 2.10), 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: Billy Aung Myint <r@use-r.com>
License: GPL (>= 2)
URL: http://rocr.bioinf.mpi-sb.mpg.de/
Packaged: 2015-03-22 07:15:36 UTC; billy
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-03-22 09:33:34