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 70 articles )

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  1. Biscio, Christophe Ange Napoléon; Lavancier, Frédéric: Contrast estimation for parametric stationary determinantal point processes (2017)
  2. Coeurjolly, Jean-François: Median-based estimation of the intensity of a spatial point process (2017)
  3. Klatt, Michael A.; Last, Günter; Mecke, Klaus; Redenbach, Claudia; Schaller, Fabian M.; Schröder-Turk, Gerd E.: Cell shape analysis of random tessellations based on Minkowski tensors (2017)
  4. Prokešová, Michaela; Dvořák, Jiří; Vedel Jensen, Eva B.: Two-step estimation procedures for inhomogeneous shot-noise Cox processes (2017)
  5. Baddeley, Adrian; Rubak, Ege; Turner, Rolf: Spatial point patterns: methodology and applications with R (2016)
  6. Baldin, Nikolay; Reiß, Markus: Unbiased estimation of the volume of a convex body (2016)
  7. Cronie, O.; van Lieshout, M.N.M.: Summary statistics for inhomogeneous marked point processes (2016)
  8. Dvořák, Jiří; Prokešová, Michaela: Parameter estimation for inhomogeneous space-time shot-noise Cox point processes (2016)
  9. Fuentes-Santos, Isabel; González-Manteiga, Wenceslao; Mateu, Jorge: Consistent smooth bootstrap kernel intensity estimation for inhomogeneous spatial Poisson point processes (2016)
  10. Hahn, Ute; Jensen, Eva B.Vedel: Hidden second-order stationary spatial point processes (2016)
  11. Lavancier, Frédéric; Møller, Jesper: Modelling aggregation on the large scale and regularity on the small scale in spatial point pattern datasets (2016)
  12. Lee, T.C.; Long, D.S.; Clarke, R.J.: Effect of endothelial glycocalyx layer redistribution upon microvessel poroelastohydrodynamics (2016)
  13. Cronie, O.; van Lieshout, M.N.M.: A $J$-function for inhomogeneous spatio-temporal point processes (2015)
  14. Gallego, María Ángeles; Ibáñez, María Victoria; Simó, Amelia: Parameter estimation in non-homogeneous Boolean models: an application to plant defense response (2015)
  15. Ver Hoef, Jay M.; Jansen, John K.: Estimating abundance from counts in large data sets of irregularly spaced plots using spatial basis functions (2015)
  16. Yue, Yu (Ryan); Loh, Ji Meng: Variable selection for inhomogeneous spatial point process models (2015)
  17. Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv
  18. Zanella, Giacomo: Random partition models and complementary clustering of Anglo-Saxon place-names (2015)
  19. Anderssen, R.S.; Baddeley, A.J.; de Hoog, F.R.; Nair, G.M.: Numerical solution of an integral equation from point process theory (2014)
  20. Ceyhan, Elvan: Comparison of relative density of two random geometric digraph families in testing spatial clustering (2014)

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