update CRAN R packages (#28786)

1 files changed, 57 insertions, 0 deletions

diff --git a/var/spack/repos/builtin/packages/r-spatstat-core/package.py b/var/spack/repos/builtin/packages/r-spatstat-core/package.py
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+# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other
+# Spack Project Developers. See the top-level COPYRIGHT file for details.
+#
+# SPDX-License-Identifier: (Apache-2.0 OR MIT)
+
+from spack import *
+
+
+class RSpatstatCore(RPackage):
+    """Core Functionality of the 'spatstat' Family.
+
+    Functionality for data analysis and modelling of spatial data, mainly
+    spatial point patterns, in the 'spatstat' family of packages. (Excludes
+    analysis of spatial data on a linear network, which is covered by the
+    separate package 'spatstat.linnet'.) Exploratory methods include quadrat
+    counts, K-functions and their simulation envelopes, nearest neighbour
+    distance and empty space statistics, Fry plots, pair correlation function,
+    kernel smoothed intensity, relative risk estimation with cross-validated
+    bandwidth selection, mark correlation functions, segregation indices, mark
+    dependence diagnostics, and kernel estimates of covariate effects. Formal
+    hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte
+    Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and
+    tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov,
+    ANOVA) are also supported. Parametric models can be fitted to point pattern
+    data using the functions ppm(), kppm(), slrm(), dppm() similar to glm().
+    Types of models include Poisson, Gibbs and Cox point processes,
+    Neyman-Scott cluster processes, and determinantal point processes. Models
+    may involve dependence on covariates, inter-point interaction, cluster
+    formation and dependence on marks. Models are fitted by maximum likelihood,
+    logistic regression, minimum contrast, and composite likelihood methods. A
+    model can be fitted to a list of point patterns (replicated point pattern
+    data) using the function mppm(). The model can include random effects and
+    fixed effects depending on the experimental design, in addition to all the
+    features listed above. Fitted point process models can be simulated,
+    automatically. Formal hypothesis tests of a fitted model are supported
+    (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with
+    basic tools for model selection (stepwise(), AIC()) and variable selection
+    (sdr). Tools for validating the fitted model include simulation envelopes,
+    residuals, residual plots and Q-Q plots, leverage and influence
+    diagnostics, partial residuals, and added variable plots."""
+
+    cran = "spatstat.core"
+
+    version('2.3-2', sha256='7f4d6d997f9187eda71097a53917e7cbe03f8dcfb4e758d86a90fbe42c92f63c')
+
+    depends_on('r@3.5.0:', type=('build', 'run'))
+    depends_on('r-spatstat-data@2.1-0:', type=('build', 'run'))
+    depends_on('r-spatstat-geom@2.3-0:', type=('build', 'run'))
+    depends_on('r-nlme', type=('build', 'run'))
+    depends_on('r-rpart', type=('build', 'run'))
+    depends_on('r-spatstat-utils@2.2-0:', type=('build', 'run'))
+    depends_on('r-spatstat-sparse@2.0-0:', type=('build', 'run'))
+    depends_on('r-mgcv', type=('build', 'run'))
+    depends_on('r-matrix', type=('build', 'run'))
+    depends_on('r-abind', type=('build', 'run'))
+    depends_on('r-tensor', type=('build', 'run'))
+    depends_on('r-goftest@1.2-2:', type=('build', 'run'))