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spatstat.random (version 3.4-4)

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

78,361

Version

3.4-4

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

January 21st, 2026

Functions in spatstat.random (3.4-4)

Simulate Neyman-Scott Point Process with Cauchy cluster kernel

Recognise Stationary and Poisson Point Process Models

Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution

Simulate a Gaussian Random Field

quadratresample

Resample a Point Pattern by Resampling Quadrats

Apply Expansion Rule

Perfect Simulation of the Diggle-Gates-Stibbard Process

Simulate Gauss-Poisson Process

Perfect Simulation of the Diggle-Gratton Process

Perfect Simulation of the Hardcore Process

Perfect Simulation of the Penttinen Process

Mosaic Random Field

Simulate Neyman-Scott Process

Simulate Product Shot-noise Cox Process

Mosaic Random Set

Simulate Matern Model II

Simulate Matern Cluster Process

Simulate Matern Model I

rPoissonCluster

Simulate Poisson Cluster Process

Simulate Log-Gaussian Cox Process

Alternating Gibbs Sampler for Area-Interaction Process

Generate Random Numbers of Points for Cell Process

Simulate Thomas Process

Alternating Gibbs Sampler for Multitype Point Processes

Perfect Simulation of the Strauss-Hardcore Process

Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

Alternating Gibbs Sampler for Multitype Hard Core Process

Simulate Simple Sequential Inhibition

Simulate Baddeley-Silverman Cell Process

Perfect Simulation of the Strauss Process

Simulate Cluster Process using Brix-Kendall Algorithm or Modifications

First Reciprocal Moment of the Truncated Poisson Distribution

Simulate point patterns using the Metropolis-Hastings algorithm.

Theoretical Distribution of Nearest Neighbour Distance

Set Control Parameters for Metropolis-Hastings Algorithm.

Random Re-Labelling of Point Pattern

Random Perturbation of Line Segment Pattern

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Specify Simulation Window or Expansion Rule

Interaction Distance of a Point Process Model

Generate N Random Multitype Points

Poisson Line Tessellation

Generate Multitype Poisson Point Pattern

Define Point Process Model for Metropolis-Hastings Simulation.

Generate Poisson Random Line Process

rmhmodel.default

Build Point Process Model for Metropolis-Hastings Simulation.

Random Pixel Noise

Determine Initial State for Metropolis-Hastings Simulation.

Generate N Random Points

Define Point Process Model for Metropolis-Hastings Simulation.

Generate Poisson Point Pattern

Randomly Shift a Point Pattern

Generate Poisson Point Pattern in Three Dimensions

rshift.splitppp

Randomly Shift a List of Point Patterns

Randomly Shift a Line Segment Pattern

Simulate Stratified Random Point Pattern

Generate Poisson Point Pattern in Any Dimensions

Random Values from the Truncated Poisson Distribution

Generate Poisson Point Pattern on Line Segments

spatstat.random-internal

Internal spatstat.random functions

Generate N Uniform Random Points in Three Dimensions

Generate N Uniform Random Points

Simulated Annealing or Simulated Tempering for Gibbs Point Processes

runifpointOnLines

Generate N Uniform Random Points On Line Segments

Random Thinning

Generate N Uniform Random Points in Any Dimensions

spatstat.random-package

The spatstat.random Package

Generate N Uniform Random Points in a Disc

update.rmhcontrol

Update Control Parameters of Metropolis-Hastings Algorithm

Random Thinning of Clumps

Test Expansion Rule

Pakes distribution

domain.rmhmodel

Extract the Domain of any Spatial Object

Field of clusters

Extract Cluster Offspring Kernel

Compute or Extract Effective Range of Cluster Kernel

as.owin.rmhmodel

Convert Data To Class owin

Mixed Poisson Distribution

Default Expansion Rule for Simulation of Model

Window.rmhmodel

Extract Window of Spatial Object

default.rmhcontrol

Set Default Control Parameters for Metropolis-Hastings Algorithm.