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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "spatstat" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'spatstat: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests'
version: 3.3-0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.spatstat
abstract: 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:
- family-names: Baddeley
  given-names: Adrian
  email: Adrian.Baddeley@curtin.edu.au
  orcid: https://orcid.org/0000-0001-9499-8382
- family-names: Turner
  given-names: Rolf
  email: rolfturner@posteo.net
  orcid: https://orcid.org/0000-0001-5521-5218
- family-names: Rubak
  given-names: Ege
  email: rubak@math.aau.dk
  orcid: https://orcid.org/0000-0002-6675-533X
preferred-citation:
  type: book
  title: 'Spatial Point Patterns: Methodology and Applications with R'
  authors:
  - family-names: Baddeley
    given-names: Adrian
    email: Adrian.Baddeley@curtin.edu.au
    orcid: https://orcid.org/0000-0001-9499-8382
  - family-names: Rubak
    given-names: Ege
    email: rubak@math.aau.dk
    orcid: https://orcid.org/0000-0002-6675-533X
  - family-names: Turner
    given-names: Rolf
    email: rolfturner@posteo.net
    orcid: https://orcid.org/0000-0001-5521-5218
  year: '2015'
  publisher:
    name: Chapman and Hall/CRC Press
    address: London
  isbn: '9781482210200'
  url: https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200/
repository: https://ar-puuk.r-universe.dev
repository-code: https://github.com/spatstat/spatstat
commit: 4b50cebe0df3013222dff9a4f237cf2f0932e9c1
url: http://spatstat.org/
date-released: '2024-11-20'
contact:
- family-names: Baddeley
  given-names: Adrian
  email: Adrian.Baddeley@curtin.edu.au
  orcid: https://orcid.org/0000-0001-9499-8382
references:
- type: article
  title: Hybrids of Gibbs Point Process Models and Their Implementation
  authors:
  - family-names: Baddeley
    given-names: Adrian
  - family-names: Turner
    given-names: Rolf
  - family-names: Mateu
    given-names: Jorge
  - family-names: Bevan
    given-names: Andrew
  journal: Journal of Statistical Software
  year: '2013'
  volume: '55'
  issue: '11'
  doi: 10.18637/jss.v055.i11
  start: '1'
  end: '43'
- type: article
  title: 'spatstat: An R Package for Analyzing Spatial Point Patterns'
  authors:
  - family-names: Baddeley
    given-names: Adrian
  - family-names: Turner
    given-names: Rolf
  journal: Journal of Statistical Software
  year: '2005'
  volume: '12'
  issue: '6'
  doi: 10.18637/jss.v012.i06
  start: '1'
  end: '42'