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 136 articles , 1 standard article )

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  1. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  2. Maximillian H.K. Hesselbarth: shar: An R package to analyze species-habitat associations using point pattern analysis (2021) not zbMATH
  3. Privault, Nicolas: Cardinality estimation for random stopping sets based on Poisson point processes (2021)
  4. Ushio Tanaka, Masami Saga, Junji Nakano: NScluster: An R Package for Maximum Palm Likelihood Estimation for Cluster Point Process Models Using OpenMP (2021) not zbMATH
  5. Biscio, Christophe A. N.; Chenavier, Nicolas; Hirsch, Christian; Svane, Anne Marie: Testing goodness of fit for point processes via topological data analysis (2020)
  6. Cronie, Ottmar; Moradi, Mehdi; Mateu, Jorge: Inhomogeneous higher-order summary statistics for point processes on linear networks (2020)
  7. Ebner, Bruno; Nestmann, Franz; Schulte, Matthias: Testing multivariate uniformity based on random geometric graphs (2020)
  8. Flagg, Kenneth A.; Hoegh, Andrew; Borkowski, John J.: Modeling partially surveyed point process data: inferring spatial point intensity of geomagnetic anomalies (2020)
  9. Julia A. Brettschneider, Oscar T. Giles, Wilfrid S. Kendall, Tomas Lazauskas: DetectorChecker: analyzing patterns of defects in detector screens (2020) not zbMATH
  10. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  11. Moka, Sarat B.; Kroese, Dirk P.: Perfect sampling for Gibbs point processes using partial rejection sampling (2020)
  12. Abdollah Jalilian: ETAS: An R Package for Fitting the Space-Time ETAS Model to Earthquake Data (2019) not zbMATH
  13. Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes: DRHotNet: An R package for detecting differential risk hotspots on a linear network (2019) arXiv
  14. Gamerman, Dani: Spatiotemporal point processes: regression, model specifications and future directions (2019)
  15. Hingee, Kassel; Baddeley, Adrian; Caccetta, Peter; Nair, Gopalan: Computation of lacunarity from covariance of spatial binary maps (2019)
  16. Lledó, Josep; Pavía, Jose M.; Morillas-Jurado, Francisco G.: Incorporating big microdata in life table construction: A hypothesis-free estimator (2019)
  17. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  18. Xu, Ganggang; Waagepetersen, Rasmus; Guan, Yongtao: Stochastic quasi-likelihood for case-control point pattern data (2019)
  19. Ceyhan, Elvan: A contingency table approach based on nearest neighbour relations for testing self and mixed correspondence (2018)
  20. Choiruddin, Achmad; Coeurjolly, Jean-François; Letué, Frédérique: Convex and non-convex regularization methods for spatial point processes intensity estimation (2018)

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