36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
# Copyright 2013-2023 Lawrence Livermore National Security, LLC and other
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# Spack Project Developers. See the top-level COPYRIGHT file for details.
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#
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# SPDX-License-Identifier: (Apache-2.0 OR MIT)
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from spack.package import *
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class ROrdinal(RPackage):
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"""Regression Models for Ordinal Data.
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Implementation of cumulative link (mixed) models also known as ordered
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regression models, proportional odds models, proportional hazards models
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for grouped survival times and ordered logit/probit/... models. Estimation
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is via maximum likelihood and mixed models are fitted with the Laplace
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approximation and adaptive Gauss-Hermite quadrature. Multiple random effect
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terms are allowed and they may be nested, crossed or partially
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nested/crossed. Restrictions of symmetry and equidistance can be imposed on
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the thresholds (cut-points/intercepts). Standard model methods are
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available (summary, anova, drop-methods, step, confint, predict etc.) in
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addition to profile methods and slice methods for visualizing the
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likelihood function and checking convergence."""
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cran = "ordinal"
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version(
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"2019.12-10", sha256="7a41e7b7e852a8fa3e911f8859d36e5709ccec5ca42ee3de14a813b7aaac7725"
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)
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version("2019.4-25", sha256="2812ad7a123cae5dbe053d1fe5f2d9935afc799314077eac185c844e3c9d79df")
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depends_on("r@2.13.0:", type=("build", "run"))
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depends_on("r-ucminf", type=("build", "run"))
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depends_on("r-mass", type=("build", "run"))
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depends_on("r-matrix", type=("build", "run"))
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depends_on("r-numderiv", type=("build", "run"))
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