Package: spatstat 3.3-0
spatstat: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Authors:
spatstat_3.3-0.tar.gz
spatstat_3.3-0.zip(r-4.5)spatstat_3.3-0.zip(r-4.4)spatstat_3.3-0.zip(r-4.3)
spatstat_3.3-0.tgz(r-4.4-any)spatstat_3.3-0.tgz(r-4.3-any)
spatstat_3.3-0.tar.gz(r-4.5-noble)spatstat_3.3-0.tar.gz(r-4.4-noble)
spatstat_3.3-0.tgz(r-4.4-emscripten)spatstat_3.3-0.tgz(r-4.3-emscripten)
spatstat.pdf |spatstat.html✨
spatstat/json (API)
NEWS
# Install 'spatstat' in R: |
install.packages('spatstat', repos = c('https://spatstat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/spatstat/spatstat/issues
cluster-processcox-point-processgibbs-processkernel-densitynetwork-analysispoint-processpoisson-processspatial-analysisspatial-dataspatial-data-analysisspatial-statisticsspatstatstatistical-methodsstatistical-modelsstatistical-testsstatistics
Last updated 6 days agofrom:4b50cebe0d. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 27 2024 |
R-4.5-win | OK | Nov 27 2024 |
R-4.5-linux | OK | Nov 27 2024 |
R-4.4-win | NOTE | Nov 27 2024 |
R-4.4-mac | NOTE | Nov 27 2024 |
R-4.3-win | NOTE | Nov 27 2024 |
R-4.3-mac | NOTE | Nov 27 2024 |
Exports:beginnerbugfixesfoolatest.changeslatest.newsplot.fooprint.autoexecprint.bugtableprint.changetablespatstat.family
Dependencies:abinddeldirgoftestlatticeMatrixmgcvnlmepolycliprpartspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensor
Analysing Replicated Point Patterns in Spatstat
Rendered fromreplicated.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2021-12-12
Started: 2014-08-11
Bugs Fixed in Spatstat
Rendered frombugfixes.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2024-06-06
Started: 2018-07-27
Datasets Provided for the Spatstat Package
Rendered fromdatasets.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2023-02-01
Started: 2014-11-19
Getting Started with Spatstat
Rendered fromgetstart.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2021-12-12
Started: 2014-08-11
Guide to Function Objects in Spatstat
Rendered fromfv.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2024-07-25
Started: 2023-07-21
Handling shapefiles in the spatstat package
Rendered fromshapefiles.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2024-03-20
Started: 2014-08-11
Summary of Recent Updates to the Spatstat Family
Rendered fromupdates.Rnw
usingutils::Sweave
on Nov 27 2024.Last update: 2024-11-19
Started: 2014-08-11
Readme and manuals
Help Manual
Help page | Topics |
---|---|
The Spatstat Package | spatstat-package spatstat |
Print Introduction For Beginners | beginner |
List Recent Bug Fixes | bugfixes |
Foo is Not a Real Name | foo plot.foo |
List Recent Significant Changes to a Function | latest.changes |
Print News About Latest Version of Package | latest.news |
Names of All Packages in the Spatstat Family | spatstat.family |