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| -rw-r--r-- | var/spack/repos/builtin/packages/r-rocr/package.py | 49 |
1 files changed, 49 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/r-rocr/package.py b/var/spack/repos/builtin/packages/r-rocr/package.py new file mode 100644 index 0000000000..0cdeb55c17 --- /dev/null +++ b/var/spack/repos/builtin/packages/r-rocr/package.py @@ -0,0 +1,49 @@ +############################################################################## +# 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')) |