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  "Date": "2026-06-07",
  "Title": "Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests",
  "Authors@R": "c(person(\"Adrian\", \"Baddeley\", \nrole = c(\"aut\", \"cre\"),\nemail = \"Adrian.Baddeley@curtin.edu.au\",\ncomment = c(ORCID=\"0000-0001-9499-8382\")),\nperson(\"Rolf\", \"Turner\",\nrole = \"aut\",\nemail=\"rolfturner@posteo.net\",\ncomment=c(ORCID=\"0000-0001-5521-5218\")),\nperson(\"Ege\",   \"Rubak\",\nrole = \"aut\",\nemail = \"rubak@math.aau.dk\",\ncomment=c(ORCID=\"0000-0002-6675-533X\")))",
  "Maintainer": "Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>",
  "Description": "Comprehensive open-source toolbox for analysing Spatial\nPoint Patterns. Focused mainly on two-dimensional point\npatterns, including multitype/marked points, in any spatial\nregion. Also supports three-dimensional point patterns,\nspace-time point patterns in any number of dimensions, point\npatterns on a linear network, and patterns of other geometrical\nobjects. Supports spatial covariate data such as pixel images.\nContains over 3000 functions for plotting spatial data,\nexploratory data analysis, model-fitting, simulation, spatial\nsampling, model diagnostics, and formal inference. Data types\ninclude point patterns, line segment patterns, spatial windows,\npixel images, tessellations, and linear networks. Exploratory\nmethods include quadrat counts, K-functions and their\nsimulation envelopes, nearest neighbour distance and empty\nspace statistics, Fry plots, pair correlation function, kernel\nsmoothed intensity, relative risk estimation with\ncross-validated bandwidth selection, mark correlation\nfunctions, segregation indices, mark dependence diagnostics,\nand kernel estimates of covariate effects. Formal hypothesis\ntests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte\nCarlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage\nMonte Carlo) and tests for covariate effects\n(Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also\nsupported. Parametric models can be fitted to point pattern\ndata using the functions ppm(), kppm(), slrm(), dppm() similar\nto glm(). Types of models include Poisson, Gibbs and Cox point\nprocesses, Neyman-Scott cluster processes, and determinantal\npoint processes. Models may involve dependence on covariates,\ninter-point interaction, cluster formation and dependence on\nmarks. Models are fitted by maximum likelihood, logistic\nregression, minimum contrast, and composite likelihood methods.\nA model can be fitted to a list of point patterns (replicated\npoint pattern data) using the function mppm(). The model can\ninclude random effects and fixed effects depending on the\nexperimental design, in addition to all the features listed\nabove. Fitted point process models can be simulated,\nautomatically. Formal hypothesis tests of a fitted model are\nsupported (likelihood ratio test, analysis of deviance, Monte\nCarlo tests) along with basic tools for model selection\n(stepwise(), AIC()) and variable selection (sdr). Tools for\nvalidating the fitted model include simulation envelopes,\nresiduals, residual plots and Q-Q plots, leverage and influence\ndiagnostics, partial residuals, and added variable plots.",
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  "URL": "http://spatstat.org/",
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  "Repository": "https://spatstat.r-universe.dev",
  "Date/Publication": "2026-06-30 02:24:58 UTC",
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      "title": "The Spatstat Package",
      "topics": [
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        "spatstat"
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      "page": "beginner",
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      "topics": [
        "beginner"
      ]
    },
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      "page": "bugfixes",
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      "page": "foo",
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