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# Copyright 2013-2023 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.package import *
class RSpatstat(RPackage):
"""Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests.
Comprehensive open-source toolbox for analysing Spatial Point Patterns.
Focused mainly on two-dimensional point patterns, including
multitype/marked points, in any spatial region. Also supports
three-dimensional point patterns, space-time point patterns in any number
of dimensions, point patterns on a linear network, and patterns of other
geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data
analysis, model-fitting, simulation, spatial sampling, model diagnostics,
and formal inference. Data types include point patterns, line segment
patterns, spatial windows, pixel images, tessellations, and linear
networks. 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"
license("GPL-2.0-or-later")
version("3.0-5", sha256="b926ed55dfeb95b09fb441f44d85204277eee00e42ac258c0a08baa1ce263bb1")
version("2.3-4", sha256="4ea0f8d70b926b92bf4a06521f985a0bb6d573619f5d526957c87860ccb999da")
version("2.3-0", sha256="da02443722f2c7ef9d59a2799b7b8002c94cecf73f2b0d2b29280d39f49c4c06")
version("1.64-1", sha256="ca3fc7d0d6b7a83fd045a7502bf03c6871fa1ab2cf411647c438fd99b4eb551a")
version("1.63-3", sha256="07b4a1a1b37c91944f31779dd789598f4a5ad047a3de3e9ec2ca99b9e9565528")
depends_on("r@3.3:", type=("build", "run"))
depends_on("r@3.5.0:", type=("build", "run"), when="@2.3-0:")
depends_on("r-spatstat-model@3.2-3:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-explore@3.1-0:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-data@1.4-2:", type=("build", "run"))
depends_on("r-spatstat-data@2.1-0:", type=("build", "run"), when="@2.3-0:")
depends_on("r-spatstat-data@2.1-2:", type=("build", "run"), when="@2.3-4:")
depends_on("r-spatstat-data@3.0-1:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-geom@2.3-0:", type=("build", "run"), when="@2.3-0:")
depends_on("r-spatstat-geom@2.4-0:", type=("build", "run"), when="@2.3-4:")
depends_on("r-spatstat-geom@3.1-0:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-random@2.2-0:", type=("build", "run"), when="@2.3-4:")
depends_on("r-spatstat-random@3.1-4:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-linnet@2.3-0:", type=("build", "run"), when="@2.3-0:")
depends_on("r-spatstat-linnet@2.3-2:", type=("build", "run"), when="@2.3-4:")
depends_on("r-spatstat-linnet@3.1-0:", type=("build", "run"), when="@3.0-5:")
depends_on("r-spatstat-utils@1.17:", type=("build", "run"))
depends_on("r-spatstat-utils@2.2-0:", type=("build", "run"), when="@2.3-0:")
depends_on("r-spatstat-utils@2.3-0:", type=("build", "run"), when="@2.3-4:")
depends_on("r-spatstat-utils@3.0-2:", type=("build", "run"), when="@3.0-5:")
depends_on("r-rpart", type=("build", "run"), when="@:1.64-1")
depends_on("r-nlme", type=("build", "run"), when="@:1.64-1")
depends_on("r-mgcv", type=("build", "run"), when="@:1.64-1")
depends_on("r-matrix", type=("build", "run"), when="@:1.64-1")
depends_on("r-deldir@0.0-21:", type=("build", "run"), when="@:1.64-1")
depends_on("r-abind", type=("build", "run"), when="@:1.64-1")
depends_on("r-tensor", type=("build", "run"), when="@:1.64-1")
depends_on("r-polyclip@1.10:", type=("build", "run"), when="@:1.64-1")
depends_on("r-goftest@1.2-2:", type=("build", "run"), when="@:1.64-1")
depends_on("r-spatstat-core@2.3-0:", type=("build", "run"), when="@2.3-0:2.3-4")
depends_on("r-spatstat-core@2.4-1:", type=("build", "run"), when="@2.3-4:2.3-4")
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