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spatstat.random (version 3.5-2)

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

78,002

Version

3.5-2

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

September 22nd, 2026

Functions in spatstat.random (3.5-2)

Perfect Simulation of the Diggle-Gratton Process

methods.clusterprocess

Methods for Cluster Models

Simulate Neyman-Scott Point Process with Cauchy cluster kernel

Pakes distribution

Perfect Simulation of the Diggle-Gates-Stibbard Process

quadratresample

Resample a Point Pattern by Resampling Quadrats

Apply Expansion Rule

Simulate a Gaussian Random Field

Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution

Recognise Stationary and Poisson Point Process Models

Simulate Gauss-Poisson Process

Simulate Product Shot-noise Cox Process

Simulate Matern Model II

Simulate Matern Model I

Simulate Neyman-Scott Process

Simulate Matern Cluster Process

Mosaic Random Field

Perfect Simulation of the Hardcore Process

Mosaic Random Set

Simulate Log-Gaussian Cox Process

Alternating Gibbs Sampler for Multitype Point Processes

Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

rPoissonCluster

Simulate Poisson Cluster Process

Perfect Simulation of the Strauss Process

Simulate Thomas Process

Perfect Simulation of the Strauss-Hardcore Process

Simulate Simple Sequential Inhibition

Perfect Simulation of the Penttinen Process

Random Un-Rounding of Spatial Location

Alternating Gibbs Sampler for Area-Interaction Process

Random Perturbation of Line Segment Pattern

Generate Random Numbers of Points for Cell Process

Perturb the Points in a Point Pattern According to a Diffusion Process

Random Re-Labelling of Point Pattern

Alternating Gibbs Sampler for Multitype Hard Core Process

Simulate Baddeley-Silverman Cell Process

Theoretical Distribution of Nearest Neighbour Distance

Simulate Cluster Process using Brix-Kendall Algorithm or Modifications

First Reciprocal Moment of the Truncated Poisson Distribution

Interaction Distance of a Point Process Model

Define Point Process Model for Metropolis-Hastings Simulation.

Specify Simulation Window or Expansion Rule

Generate Multitype Poisson Point Pattern

Determine Initial State for Metropolis-Hastings Simulation.

Simulate point patterns using the Metropolis-Hastings algorithm.

Set Control Parameters for Metropolis-Hastings Algorithm.

rmhmodel.default

Build Point Process Model for Metropolis-Hastings Simulation.

Generate N Random Multitype Points

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Define Point Process Model for Metropolis-Hastings Simulation.

Generate Poisson Point Pattern

Random Pixel Noise

Generate N Random Points in 3 Dimensions

Generate N Random Points

Generate Poisson Random Line Process

rpoisDirichletTess

Poisson Dirichlet Tessellation

Generate Poisson Point Pattern in Three Dimensions

Simulated Annealing or Simulated Tempering for Gibbs Point Processes

Random Thinning

Generate Poisson Point Pattern on Line Segments

Simulate Stratified Random Point Pattern

Random Thinning of Clumps

rshift.splitppp

Randomly Shift a List of Point Patterns

Poisson Line Tessellation

update.rmhcontrol

Update Control Parameters of Metropolis-Hastings Algorithm

Generate N Uniform Random Points in a Disc

Truncated Poisson Distribution

Generate Poisson Point Pattern in Any Dimensions

Randomly Shift a Line Segment Pattern

Generate N Uniform Random Points in Three Dimensions

Test Expansion Rule

Randomly Shift a Point Pattern

Generate N Uniform Random Points

runifpointOnLines

Generate N Uniform Random Points On Line Segments

Generate N Uniform Random Points in Any Dimensions

spatstat.random-internal

Internal spatstat.random functions

spatstat.random-package

The spatstat.random Package

Window.rmhmodel

Extract Window of Spatial Object

Extract Cluster Offspring Kernel

Field of clusters

Compute or Extract Effective Range of Cluster Kernel

Mixed Poisson Distribution

Default Expansion Rule for Simulation of Model

as.owin.rmhmodel

Convert Data To Class owin

default.rmhcontrol

Set Default Control Parameters for Metropolis-Hastings Algorithm.

Cluster Point Process Model

domain.rmhmodel

Extract the Domain of any Spatial Object