spatstat: Spatial Point Pattern analysis, model-fitting, simulation, tests , A package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Also supports three-dimensional point patterns, and space-time point patterns in any number of dimensions. Contains over 1000 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 and tessellations. Exploratory methods include K-functions, 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 etc. Point process models can be fitted to point pattern data using functions ppm, kppm, slrm similar to glm. Models may include dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Fitted models can be simulated automatically. Also provides facilities for formal inference (such as chi-squared tests) and model diagnostics (including simulation envelopes, residuals, residual plots and Q-Q plots). (Source:

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 52 articles )

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  1. Coeurjolly, Jean-François: Median-based estimation of the intensity of a spatial point process (2017)
  2. Baddeley, Adrian; Rubak, Ege; Turner, Rolf: Spatial point patterns: methodology and applications with R (2016)
  3. Baldin, Nikolay; Reiß, Markus: Unbiased estimation of the volume of a convex body (2016)
  4. Cronie, O.; van Lieshout, M.N.M.: Summary statistics for inhomogeneous marked point processes (2016)
  5. Lee, T.C.; Long, D.S.; Clarke, R.J.: Effect of endothelial glycocalyx layer redistribution upon microvessel poroelastohydrodynamics (2016)
  6. Gallego, María Ángeles; Ibáñez, María Victoria; Simó, Amelia: Parameter estimation in non-homogeneous Boolean models: an application to plant defense response (2015)
  7. Ver Hoef, Jay M.; Jansen, John K.: Estimating abundance from counts in large data sets of irregularly spaced plots using spatial basis functions (2015)
  8. Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv
  9. Ceyhan, Elvan: Comparison of relative density of two random geometric digraph families in testing spatial clustering (2014)
  10. Coeurjolly, Jean-François; Guan, Yongtao: Covariance of empirical functionals for inhomogeneous spatial point processes when the intensity has a parametric form (2014)
  11. Coeurjolly, Jean-François; Møller, Jesper: Variational approach for spatial point process intensity estimation (2014)
  12. Prokešová, Michaela; Dvořák, Jiří: Statistics for inhomogeneous space-time shot-noise Cox processes (2014)
  13. Stucki, Kaspar; Schuhmacher, Dominic: Bounds for the probability generating functional of a Gibbs point process (2014)
  14. Baddeley, Adrian: Spatial point patterns: models and statistics (2013)
  15. Baddeley, Adrian; Chang, Ya-Mei; Song, Yong: Leverage and influence diagnostics for spatial point processes (2013)
  16. Baddeley, Adrian; Dereudre, David: Variational estimators for the parameters of Gibbs point process models (2013)
  17. Bar-Hen, A.; Chadoeuf, J.; Dessard, H.; Monestiez, P.: Estimating second order characteristics of point processes with known independent noise (2013)
  18. Bivand, Roger S.; Pebesma, Edzer J.; Gómez-Rubio, Virgilio: Applied spatial data analysis with R (2013)
  19. Coeurjolly, Jean-François; Rubak, Ege: Fast covariance estimation for innovations computed from a spatial Gibbs point process (2013)
  20. Ghorbani, Mohammad: Cauchy cluster process (2013)

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