Package: spatstat
Version: 3.6-3
Date: 2026-09-28
Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Authors@R: c(person("Adrian", "Baddeley", 
                    role = c("aut", "cre"),
       	            email = "Adrian.Baddeley@curtin.edu.au",
		    comment = c(ORCID="0000-0001-9499-8382")),
	     person("Rolf", "Turner", 
                    role = "aut",
 	            email="rolfturner@posteo.net",
		    comment=c(ORCID="0000-0001-5521-5218")),
	     person("Ege",   "Rubak", 
                    role = "aut",
		    email = "rubak@math.aau.dk",
		    comment=c(ORCID="0000-0002-6675-533X")))
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
Depends: R (>= 3.5.0), spatstat.data (>= 3.1-9), spatstat.univar (>=
        3.2-0), spatstat.geom (>= 3.8-3), spatstat.random (>= 3.5-2),
        spatstat.explore (>= 3.8-3), spatstat.model (>= 3.7-2),
        spatstat.linnet (>= 3.5-4), utils
Imports: spatstat.utils (>= 3.2-5)
Suggests:
Description: 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.
License: GPL (>= 2)
URL: http://spatstat.org/
NeedsCompilation: no
ByteCompile: true
BugReports: https://github.com/spatstat/spatstat/issues
Repository: https://spatstat.r-universe.dev
Date/Publication: 2026-09-28 02:26:03 UTC
RemoteUrl: https://github.com/spatstat/spatstat
RemoteRef: HEAD
RemoteSha: 5c3dbf60e1161ec0ce389d96877f59e9bc768805
Packaged: 2026-10-13 13:42:14 UTC; root
Author: Adrian Baddeley [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-9499-8382>),
  Rolf Turner [aut] (ORCID: <https://orcid.org/0000-0001-5521-5218>),
  Ege Rubak [aut] (ORCID: <https://orcid.org/0000-0002-6675-533X>)
