spatstat.random

Random Generation and Simulation for the spatstat family

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

This package spatstat.random is one of the sub-packages. It contains the functions for random generation of data and simulation of models.

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

Overview

spatstat.random supports

• generating random spatial patterns of points according to many simple rules (complete spatial randomness, binomial process, random grid, systematic random, stratified random, simple sequential inhibition, cell process),

• randomised alteration of patterns (thinning, random shift, jittering),

• generating simulated realisations of spatial point processes (Poisson processes, Matern inhibition models, Matern cluster processes, Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes, Gibbs point processes)

• generating simulated realisations of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm; perfect simulation/ dominated coupling from the past; alternating Gibbs sampler)

• generating random spatial patterns of line segments

• generating random tessellations

• generating random images (random noise, random mosaics).

Exceptions:

• generation of determinantal point processes is provided in spatstat.model

• generation of quasi-random patterns is provided in spatstat.geom

Installing the package

This repository contains the development version of spatstat.random. 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.random", dependencies=TRUE, repos=repo)

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

install.packages("spatstat.random")