# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- 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: 3.1-1.003 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://spatstat.r-universe.dev repository-code: https://github.com/spatstat/spatstat commit: 4af29c3b8190c0903eed2d9bb8b118bf48dd4e1b url: http://spatstat.org/ date-released: '2024-08-17' 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'