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+##############################################################################
+# Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC.
+# Produced at the Lawrence Livermore National Laboratory.
+#
+# This file is part of Spack.
+# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
+# LLNL-CODE-647188
+#
+# For details, see https://github.com/llnl/spack
+# Please also see the NOTICE and LICENSE files for our notice and the LGPL.
+#
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License (as
+# published by the Free Software Foundation) version 2.1, February 1999.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and
+# conditions of the GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this program; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+##############################################################################
+from spack import *
+
+
+class RRocr(RPackage):
+    """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."""
+    homepage = "https://cran.r-project.org/package=ROCR"
+    url      = "https://cran.rstudio.com/src/contrib/ROCR_1.0-7.tar.gz"
+
+    version('1.0-7', '46cbd43ae87fc4e1eff2109529a4820e')
+    depends_on('r-gplots', type=('build', 'run'))