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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 RSpatstatLinnet(RPackage):
"""Linear Networks Functionality of the 'spatstat' Family.
Defines types of spatial data on a linear network and provides
functionality for geometrical operations, data analysis and modelling of
data on a linear network, in the 'spatstat' family of packages. Contains
definitions and support for linear networks, including creation of
networks, geometrical measurements, topological connectivity, geometrical
operations such as inserting and deleting vertices, intersecting a network
with another object, and interactive editing of networks. Data types
defined on a network include point patterns, pixel images, functions, and
tessellations. Exploratory methods include kernel estimation of intensity
on a network, K-functions and pair correlation functions on a network,
simulation envelopes, nearest neighbour distance and empty space distance,
relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are
implemented so far. Models may involve dependence on covariates and
dependence on marks. Models are fitted by maximum likelihood. 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. Random point patterns on a network can be generated using a variety
of models."""
cran = "spatstat.linnet"
version("3.1-0", sha256="b9b0ad66af169ca1ef3da852578d7b65521cf55f4a72c43ae5b1f2d4f47bf00c")
version("2.3-2", sha256="9c78a4b680debfff0f3ae934575c30d03ded49bc9a7179475384af0ebaf13778")
version("2.3-1", sha256="119ba6e3da651aa9594f70a7a35349209534215aa640c2653aeddc6aa25038c3")
depends_on("r@3.5.0:", type=("build", "run"))
depends_on("r-spatstat-model@3.2-1:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-explore@3.0-6:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-data@2.1-0:", type=("build", "run"))
depends_on("r-spatstat-data@3.0:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-geom@2.3-0:", type=("build", "run"))
depends_on("r-spatstat-geom@3.0-6:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-random@2.2-0:", type=("build", "run"), when="@2.3-2:")
depends_on("r-spatstat-random@3.1-3:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-utils@2.2-0:", type=("build", "run"))
depends_on("r-spatstat-utils@3.0-2:", type=("build", "run"), when="@3.1-0:")
depends_on("r-matrix", type=("build", "run"))
depends_on("r-spatstat-sparse@2.0:", type=("build", "run"))
depends_on("r-spatstat-sparse@3.0:", type=("build", "run"), when="@3.1-0:")
depends_on("r-spatstat-core@2.3-2:", type=("build", "run"), when="@2.3-2:2.3-2")
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