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spatstat.random (version 3.3-3)

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

61,655

Version

3.3-3

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

March 19th, 2025

Functions in spatstat.random (3.3-3)

default.rmhcontrol

Set Default Control Parameters for Metropolis-Hastings Algorithm.

Compute or Extract Effective Range of Cluster Kernel

Pakes distribution

Default Expansion Rule for Simulation of Model

Extract Cluster Offspring Kernel

Window.rmhmodel

Extract Window of Spatial Object

Recognise Stationary and Poisson Point Process Models

as.owin.rmhmodel

Convert Data To Class owin

quadratresample

Resample a Point Pattern by Resampling Quadrats

Mixed Poisson Distribution

Perfect Simulation of the Diggle-Gratton Process

rPoissonCluster

Simulate Poisson Cluster Process

Apply Expansion Rule

Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution

Simulate Matern Model II

Perfect Simulation of the Hardcore Process

Simulate Simple Sequential Inhibition

Mosaic Random Field

Alternating Gibbs Sampler for Multitype Point Processes

Simulate Neyman-Scott Point Process with Cauchy cluster kernel

Alternating Gibbs Sampler for Area-Interaction Process

Simulate Gauss-Poisson Process

Simulate Log-Gaussian Cox Process

Perfect Simulation of the Diggle-Gates-Stibbard Process

Perfect Simulation of the Penttinen Process

Simulate Product Shot-noise Cox Process

Generate Random Numbers of Points for Cell Process

Simulate Matern Cluster Process

Simulate Matern Model I

Mosaic Random Set

Interaction Distance of a Point Process Model

Simulate Cluster Process using Brix-Kendall Algorithm or Modifications

First Reciprocal Moment of the Truncated Poisson Distribution

Simulate Thomas Process

Poisson Line Tessellation

Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

Alternating Gibbs Sampler for Multitype Hard Core Process

Generate Poisson Point Pattern

Generate Poisson Point Pattern in Three Dimensions

Generate Poisson Point Pattern on Line Segments

Determine Initial State for Metropolis-Hastings Simulation.

Simulate Baddeley-Silverman Cell Process

Generate N Uniform Random Points in a Disc

Random Perturbation of Line Segment Pattern

Simulate Neyman-Scott Process

Theoretical Distribution of Nearest Neighbour Distance

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Random Re-Labelling of Point Pattern

Perfect Simulation of the Strauss Process

Simulate point patterns using the Metropolis-Hastings algorithm.

Perfect Simulation of the Strauss-Hardcore Process

Generate N Random Multitype Points

Specify Simulation Window or Expansion Rule

Define Point Process Model for Metropolis-Hastings Simulation.

Generate N Uniform Random Points

Set Control Parameters for Metropolis-Hastings Algorithm.

Randomly Shift a Point Pattern

Generate Multitype Poisson Point Pattern

rmhmodel.default

Build Point Process Model for Metropolis-Hastings Simulation.

Generate N Random Points

Define Point Process Model for Metropolis-Hastings Simulation.

Random Pixel Noise

Generate Poisson Random Line Process

Generate N Uniform Random Points in Any Dimensions

Generate Poisson Point Pattern in Any Dimensions

spatstat.random-internal

Internal spatstat.random functions

Simulate Stratified Random Point Pattern

Simulated Annealing or Simulated Tempering for Gibbs Point Processes

Randomly Shift a Line Segment Pattern

Generate N Uniform Random Points in Three Dimensions

runifpointOnLines

Generate N Uniform Random Points On Line Segments

Random Values from the Truncated Poisson Distribution

rshift.splitppp

Randomly Shift a List of Point Patterns

Random Thinning

Random Thinning of Clumps

spatstat.random-package

The spatstat.random Package

update.rmhcontrol

Update Control Parameters of Metropolis-Hastings Algorithm

Test Expansion Rule

Field of clusters

domain.rmhmodel

Extract the Domain of any Spatial Object