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spatstat.random (version 3.4-3)

Random Generation Functionality for the 'spatstat' Family

Description

Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.

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Version

Install

install.packages('spatstat.random')

Monthly Downloads

90,060

Version

3.4-3

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

November 21st, 2025

Functions in spatstat.random (3.4-3)

domain.rmhmodel

Extract the Domain of any Spatial Object

Field of clusters

Default Expansion Rule for Simulation of Model

Extract Cluster Offspring Kernel

Compute or Extract Effective Range of Cluster Kernel

Mixed Poisson Distribution

Pakes distribution

as.owin.rmhmodel

Convert Data To Class owin

default.rmhcontrol

Set Default Control Parameters for Metropolis-Hastings Algorithm.

Window.rmhmodel

Extract Window of Spatial Object

Simulate Gauss-Poisson Process

Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution

Simulate a Gaussian Random Field

Apply Expansion Rule

quadratresample

Resample a Point Pattern by Resampling Quadrats

Simulate Neyman-Scott Point Process with Cauchy cluster kernel

Recognise Stationary and Poisson Point Process Models

Perfect Simulation of the Diggle-Gates-Stibbard Process

Perfect Simulation of the Diggle-Gratton Process

Perfect Simulation of the Hardcore Process

Simulate Matern Model I

Mosaic Random Field

Perfect Simulation of the Penttinen Process

Simulate Neyman-Scott Process

Simulate Matern Model II

Simulate Product Shot-noise Cox Process

rPoissonCluster

Simulate Poisson Cluster Process

Simulate Matern Cluster Process

Simulate Log-Gaussian Cox Process

Mosaic Random Set

Alternating Gibbs Sampler for Multitype Point Processes

Simulate Thomas Process

Generate Random Numbers of Points for Cell Process

Perfect Simulation of the Strauss Process

Alternating Gibbs Sampler for Area-Interaction Process

Alternating Gibbs Sampler for Multitype Hard Core Process

Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

Perfect Simulation of the Strauss-Hardcore Process

Simulate Simple Sequential Inhibition

Simulate Baddeley-Silverman Cell Process

Simulate Cluster Process using Brix-Kendall Algorithm or Modifications

Set Control Parameters for Metropolis-Hastings Algorithm.

Simulate point patterns using the Metropolis-Hastings algorithm.

Theoretical Distribution of Nearest Neighbour Distance

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Interaction Distance of a Point Process Model

First Reciprocal Moment of the Truncated Poisson Distribution

Specify Simulation Window or Expansion Rule

Random Perturbation of Line Segment Pattern

Random Re-Labelling of Point Pattern

Generate Multitype Poisson Point Pattern

Define Point Process Model for Metropolis-Hastings Simulation.

rmhmodel.default

Build Point Process Model for Metropolis-Hastings Simulation.

Generate Poisson Random Line Process

Generate N Random Multitype Points

Generate N Random Points

Determine Initial State for Metropolis-Hastings Simulation.

Define Point Process Model for Metropolis-Hastings Simulation.

Poisson Line Tessellation

Random Pixel Noise

Generate Poisson Point Pattern on Line Segments

rshift.splitppp

Randomly Shift a List of Point Patterns

Generate Poisson Point Pattern in Three Dimensions

Random Values from the Truncated Poisson Distribution

Randomly Shift a Point Pattern

Simulate Stratified Random Point Pattern

Generate Poisson Point Pattern in Any Dimensions

Randomly Shift a Line Segment Pattern

Generate Poisson Point Pattern

runifpointOnLines

Generate N Uniform Random Points On Line Segments

Random Thinning of Clumps

Generate N Uniform Random Points

Random Thinning

spatstat.random-internal

Internal spatstat.random functions

Simulated Annealing or Simulated Tempering for Gibbs Point Processes

Generate N Uniform Random Points in Three Dimensions

Generate N Uniform Random Points in a Disc

spatstat.random-package

The spatstat.random Package

Generate N Uniform Random Points in Any Dimensions

update.rmhcontrol

Update Control Parameters of Metropolis-Hastings Algorithm

Test Expansion Rule