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.
Contents:
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
- binomial random patterns (
runifpoint,rpoint,rmpoint,runifdisc) - completely random patterns (
rpoispp,rmpoispp) - systematic random patterns (
rstrat,rsyst)
- simple sequential inhibition (
rSSI) - Matern inhibition models (
rMaternI,rMaternII) - cell process (
rcell)
- random shift (
rshift) - random thinning (
rthin) - random (re)labelling (
rlabel) - block resampling (
quadratresample)
- log-Gaussian Cox process (
rLGCP) - Neyman-Scott cluster processes
(
rThomas,rMatClust,rCauchy,rVarGamma) - general Neyman-Scott cluster process (
rNeymanScott) - general Poisson cluster process (
rPoissonCluster) - Gauss-Poisson process (
rGaussPoisson)
- perfect simulation algorithms for specific Gibbs models
(
rHardcore,rStrauss,rStraussHard,rDiggleGratton,rDGS,rPenttinen, - Metropolis-Hastings simulation algorithm for Gibbs models
(
rmh) - alternating Gibbs sampler for multitype Gibbs processes (
rags,ragsMultiHard) - alternating Gibbs sampler for area-interaction process (
ragsAreaInter)
- random points along specified line segments
(
runifpointOnLines,rpoisppOnLines)
- random pixel noise (
rnoise) - random mosaic (
rMosaicField,rMosaicSet)
- Poisson line process (
rpoisline)
- tessellation using Poisson line process (
rpoislinetess)
- uniform random points in 3D (
runifpoint3) - Poisson point process in 3D (
rpoispp3)
- uniform random points in space or space-time (
runifpointx) - Poisson point process in space or space-time (
rpoisppx)
- theoretical distribution of nearest neighbour distance (
rknn) - mixed Poisson distribution (
dmixpois)
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")