./geography/R-spatstat.linnet, Linear Networks Functionality of the spatstat Family

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Branch: pkgsrc-2022Q3, Version: 2.3.0, Package name: R-spatstat.linnet-2.3.0, Maintainer: pkgsrc-users

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.

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