r-powerlaw: new package (#7222)
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@ -35,6 +35,12 @@ class RMgcv(RPackage):
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url = "https://cran.r-project.org/src/contrib/mgcv_1.8-16.tar.gz"
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list_url = "https://cran.r-project.org/src/contrib/Archive/mgcv"
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version('1.8-22', 'b42079b33b46de784f293a74c824b877')
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version('1.8-21', 'aae8262a07c8698ca8d6213065c4983f')
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version('1.8-20', '58eb94404aad7ff8a0cf11a2f098f8bf')
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version('1.8-19', 'f9a4e29464f4d10b7b2cb9d0bec3fa9e')
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version('1.8-18', 'c134fc2db253530233b95f2e36b56a2f')
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version('1.8-17', '398582d0f999ac34749f4f5f1d103f75')
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version('1.8-16', '4c1d85e0f80b017bccb4b63395842911')
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version('1.8-13', '30607be3aaf44b13bd8c81fc32e8c984')
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40
var/spack/repos/builtin/packages/r-powerlaw/package.py
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var/spack/repos/builtin/packages/r-powerlaw/package.py
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##############################################################################
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# Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC.
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# Produced at the Lawrence Livermore National Laboratory.
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#
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# This file is part of Spack.
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# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
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# LLNL-CODE-647188
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#
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# For details, see https://github.com/spack/spack
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# Please also see the NOTICE and LICENSE files for our notice and the LGPL.
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License (as
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# published by the Free Software Foundation) version 2.1, February 1999.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and
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# conditions of the GNU Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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##############################################################################
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from spack import *
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class RPowerlaw(RPackage):
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"""An implementation of maximum likelihood estimators for a variety of
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heavy tailed distributions, including both the discrete and continuous
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power law distributions. Additionally, a goodness-of-fit based approach
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is used to estimate the lower cut-off for the scaling region."""
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homepage = "https://github.com/csgillespie/poweRlaw"
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url = "https://cran.rstudio.com/src/contrib/poweRlaw_0.70.1.tar.gz"
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list_url = "https://cran.rstudio.com/src/contrib/Archive/poweRlaw"
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version('0.70.1', '4117cb95c37f72441f320ea12f553065')
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depends_on('r-vgam', type=('build', 'run'))
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