54 lines
3.1 KiB
Python
54 lines
3.1 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 RSpatstatLinnet(RPackage):
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"""Linear Networks Functionality of the 'spatstat' Family.
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Defines types of spatial data on a linear network and provides
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functionality for geometrical operations, data analysis and modelling of
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data on a linear network, in the 'spatstat' family of packages. Contains
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definitions and support for linear networks, including creation of
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networks, geometrical measurements, topological connectivity, geometrical
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operations such as inserting and deleting vertices, intersecting a network
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with another object, and interactive editing of networks. Data types
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defined on a network include point patterns, pixel images, functions, and
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tessellations. Exploratory methods include kernel estimation of intensity
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on a network, K-functions and pair correlation functions on a network,
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simulation envelopes, nearest neighbour distance and empty space distance,
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relative risk estimation with cross-validated bandwidth selection. Formal
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hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte
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Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and
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tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov,
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ANOVA) are also supported. Parametric models can be fitted to point pattern
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data using the function lppm() similar to glm(). Only Poisson models are
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implemented so far. Models may involve dependence on covariates and
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dependence on marks. Models are fitted by maximum likelihood. Fitted point
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process models can be simulated, automatically. Formal hypothesis tests of
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a fitted model are supported (likelihood ratio test, analysis of deviance,
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Monte Carlo tests) along with basic tools for model selection (stepwise(),
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AIC()) and variable selection (sdr). Tools for validating the fitted model
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include simulation envelopes, residuals, residual plots and Q-Q plots,
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leverage and influence diagnostics, partial residuals, and added variable
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plots. Random point patterns on a network can be generated using a variety
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of models."""
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cran = "spatstat.linnet"
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version("2.3-2", sha256="9c78a4b680debfff0f3ae934575c30d03ded49bc9a7179475384af0ebaf13778")
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version("2.3-1", sha256="119ba6e3da651aa9594f70a7a35349209534215aa640c2653aeddc6aa25038c3")
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depends_on("r@3.5.0:", type=("build", "run"))
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depends_on("r-spatstat-data@2.1-0:", type=("build", "run"))
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depends_on("r-spatstat-geom@2.3-0:", type=("build", "run"))
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depends_on("r-spatstat-random@2.2-0:", type=("build", "run"), when="@2.3-2:")
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depends_on("r-spatstat-core@2.3-0:", type=("build", "run"))
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depends_on("r-spatstat-core@2.3-2:", type=("build", "run"), when="@2.3-2:")
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depends_on("r-spatstat-utils@2.2-0:", type=("build", "run"))
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depends_on("r-matrix", type=("build", "run"))
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depends_on("r-spatstat-sparse@2.0:", type=("build", "run"))
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