Package: r-cran-spatstat.linnet
Version: 3.5-0-1
Architecture: arm64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 1966
Depends: r-api-4.0, r-cran-spatstat.data (>= 3.1-9), r-cran-spatstat.univar (>= 3.1-7), r-cran-spatstat.geom (>= 3.7-3), r-cran-spatstat.random (>= 3.4-5), r-cran-spatstat.explore (>= 3.8), r-cran-spatstat.model (>= 3.7), r-cran-matrix, r-cran-spatstat.utils (>= 3.2-2), r-cran-spatstat.sparse (>= 3.1), libc6 (>= 2.17)
Recommends: r-cran-goftest, r-cran-locfit, r-cran-spatstat (>= 3.5), r-cran-testthat
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=spatstat.linnet
Description: 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.
