91 lines
5.8 KiB
Python
91 lines
5.8 KiB
Python
# Copyright 2013-2024 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 RSpatstat(RPackage):
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"""Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests.
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Comprehensive open-source toolbox for analysing Spatial Point Patterns.
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Focused mainly on two-dimensional point patterns, including
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multitype/marked points, in any spatial region. Also supports
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three-dimensional point patterns, space-time point patterns in any number
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of dimensions, point patterns on a linear network, and patterns of other
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geometrical objects. Supports spatial covariate data such as pixel images.
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Contains over 2000 functions for plotting spatial data, exploratory data
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analysis, model-fitting, simulation, spatial sampling, model diagnostics,
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and formal inference. Data types include point patterns, line segment
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patterns, spatial windows, pixel images, tessellations, and linear
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networks. Exploratory methods include quadrat counts, K-functions and their
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simulation envelopes, nearest neighbour distance and empty space
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statistics, Fry plots, pair correlation function, kernel smoothed
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intensity, relative risk estimation with cross-validated bandwidth
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selection, mark correlation functions, segregation indices, mark dependence
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diagnostics, and kernel estimates of covariate effects. Formal hypothesis
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tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo,
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Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests
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for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA)
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are also supported. Parametric models can be fitted to point pattern data
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using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types
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of models include Poisson, Gibbs and Cox point processes, Neyman-Scott
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cluster processes, and determinantal point processes. Models may involve
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dependence on covariates, inter-point interaction, cluster formation and
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dependence on marks. Models are fitted by maximum likelihood, logistic
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regression, minimum contrast, and composite likelihood methods. A model can
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be fitted to a list of point patterns (replicated point pattern data) using
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the function mppm(). The model can include random effects and fixed effects
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depending on the experimental design, in addition to all the features
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listed above. Fitted point process models can be simulated, automatically.
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Formal hypothesis tests of a fitted model are supported (likelihood ratio
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test, analysis of deviance, Monte Carlo tests) along with basic tools for
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model selection (stepwise(), AIC()) and variable selection (sdr). Tools for
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validating the fitted model include simulation envelopes, residuals,
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residual plots and Q-Q plots, leverage and influence diagnostics, partial
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residuals, and added variable plots."""
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cran = "spatstat"
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license("GPL-2.0-or-later")
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version("3.0-5", sha256="b926ed55dfeb95b09fb441f44d85204277eee00e42ac258c0a08baa1ce263bb1")
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version("2.3-4", sha256="4ea0f8d70b926b92bf4a06521f985a0bb6d573619f5d526957c87860ccb999da")
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version("2.3-0", sha256="da02443722f2c7ef9d59a2799b7b8002c94cecf73f2b0d2b29280d39f49c4c06")
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version("1.64-1", sha256="ca3fc7d0d6b7a83fd045a7502bf03c6871fa1ab2cf411647c438fd99b4eb551a")
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version("1.63-3", sha256="07b4a1a1b37c91944f31779dd789598f4a5ad047a3de3e9ec2ca99b9e9565528")
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depends_on("r@3.3:", type=("build", "run"))
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depends_on("r@3.5.0:", type=("build", "run"), when="@2.3-0:")
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depends_on("r-spatstat-model@3.2-3:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-explore@3.1-0:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-data@1.4-2:", type=("build", "run"))
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depends_on("r-spatstat-data@2.1-0:", type=("build", "run"), when="@2.3-0:")
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depends_on("r-spatstat-data@2.1-2:", type=("build", "run"), when="@2.3-4:")
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depends_on("r-spatstat-data@3.0-1:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-geom@2.3-0:", type=("build", "run"), when="@2.3-0:")
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depends_on("r-spatstat-geom@2.4-0:", type=("build", "run"), when="@2.3-4:")
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depends_on("r-spatstat-geom@3.1-0:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-random@2.2-0:", type=("build", "run"), when="@2.3-4:")
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depends_on("r-spatstat-random@3.1-4:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-linnet@2.3-0:", type=("build", "run"), when="@2.3-0:")
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depends_on("r-spatstat-linnet@2.3-2:", type=("build", "run"), when="@2.3-4:")
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depends_on("r-spatstat-linnet@3.1-0:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-spatstat-utils@1.17:", type=("build", "run"))
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depends_on("r-spatstat-utils@2.2-0:", type=("build", "run"), when="@2.3-0:")
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depends_on("r-spatstat-utils@2.3-0:", type=("build", "run"), when="@2.3-4:")
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depends_on("r-spatstat-utils@3.0-2:", type=("build", "run"), when="@3.0-5:")
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depends_on("r-rpart", type=("build", "run"), when="@:1.64-1")
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depends_on("r-nlme", type=("build", "run"), when="@:1.64-1")
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depends_on("r-mgcv", type=("build", "run"), when="@:1.64-1")
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depends_on("r-matrix", type=("build", "run"), when="@:1.64-1")
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depends_on("r-deldir@0.0-21:", type=("build", "run"), when="@:1.64-1")
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depends_on("r-abind", type=("build", "run"), when="@:1.64-1")
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depends_on("r-tensor", type=("build", "run"), when="@:1.64-1")
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depends_on("r-polyclip@1.10:", type=("build", "run"), when="@:1.64-1")
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depends_on("r-goftest@1.2-2:", type=("build", "run"), when="@:1.64-1")
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depends_on("r-spatstat-core@2.3-0:", type=("build", "run"), when="@2.3-0:2.3-4")
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depends_on("r-spatstat-core@2.4-1:", type=("build", "run"), when="@2.3-4:2.3-4")
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