You are viewing the GitHub repository which holds the latest development version of spatstat.linnet. For the latest public release on CRAN, click the green badge above.

• Overview of spatstat.linnet
• Where to find data
• Detailed contents of package
• Installing the package
• Bug reports
• Questions
• Proposing changes to code
• Future development

The original spatstat package has been split into several sub-packages (See spatstat/spatstat).

This package spatstat.linnet is one of the sub-packages. It contains the subset of the functionality of spatstat that deals with data on linear networks. It supports

• network geometry
• point patterns on a network
• spatial covariates on a network
• simulation
• exploratory data analysis
• parametric modelling and formal inference
• informal model diagnostics

There is also an extension package spatstat.Knet which contains additional algorithms for linear networks.

Examples of datasets on linear networks are the point patterns chicago, dendrite and spiders provided in the spatstat.data package (available when spatstat.linnet is loaded) and the point pattern wacrashes provided in the extension package spatstat.Knet (which must be loaded separately).

spatstat.linnet supports

• creation of linear networks from coordinate data
• extraction of networks from tessellations
• modification of networks
• interactive editing of networks
• geometrical operations and measurement on networks
• construction of the disc in the shortest-path metric
• trees, tree branch labels, tree pruning

• creation of point patterns on a network from coordinate data
• extraction of sub-patterns
• shortest-path distance measurement

• create pixel images and functions on a network
• arithmetic operators for pixel images on a network
• plot pixel images on a network (colour/thickness/perspective)
• tessellation on a network

• completely random (uniform Poisson) point patterns on a network
• nonuniform random (Poisson) point patterns on a network
• Switzer-type point process
• log-Gaussian Cox process

• kernel density estimation on a network
• bandwidth selection
• kernel smoothing on a network
• estimation of intensity as a function of a covariate
• ROC curves
• Berman-Waller-Lawson test
• CDF test
• variable selection by Sufficient Dimension Reduction
• K function on a network (shortest path or Euclidean distance)
• pair correlation function on a network (shortest path or Euclidean distance)
• inhomogeneous K function and pair correlation function
• inhomogeneous F, G and J functions
• simulation envelopes of summary functions

• fit point process model on a network
• fitted/predicted intensity
• analysis of deviance for point process model
• simulate fitted model

• lurking variable plot
• residuals
• leverage and influence
• four-panel diagnostic plot
• residual Q-Q plot

This repository contains the development version of spatstat.linnet. The easiest way to install the development version is to start R and type

repo <- c('https://spatstat.r-universe.dev', 'https://cloud.r-project.org')
install.packages("spatstat.linnet", dependencies=TRUE, repos=repo)

To install the latest public release of spatstat.linnet, type

install.packages("spatstat.linnet")

Users are encouraged to report bugs. If you find a bug in a spatstat function, please identify the sub-package containing that function. Visit the GitHub repository for the sub-package, click the Issues tab at the top of the page, and press new issue to start a new bug report, documentation correction or feature request.

Please do not post questions on the Issues pages, because they are too clunky for correspondence.

For questions about the spatstat package family, first check the question-and-answer website stackoverflow to see whether your question has already been asked and answered. If not, you can either post your question at stackoverflow, or email the authors.

Feel free to fork spatstat.linnet, make changes to the code, and ask us to include them in the package by making a github pull request.

The spatstat package family is the result of 30 years of software development and contains over 200,000 lines of code. It is still under development, motivated by the needs of researchers in many fields, and driven by innovations in statistical science. We welcome contributions of code, and suggestions for improvements.