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 107 articles , 1 standard article )

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  1. Hingee, Kassel; Baddeley, Adrian; Caccetta, Peter; Nair, Gopalan: Computation of lacunarity from covariance of spatial binary maps (2019)
  2. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  3. Ceyhan, Elvan: A contingency table approach based on nearest neighbour relations for testing self and mixed correspondence (2018)
  4. Choiruddin, Achmad; Coeurjolly, Jean-François; Letué, Frédérique: Convex and non-convex regularization methods for spatial point processes intensity estimation (2018)
  5. Davies, Tilman M.; Baddeley, Adrian: Fast computation of spatially adaptive kernel estimates (2018)
  6. Guillaume Gautier, Rémi Bardenet, Michal Valko: DPPy: Sampling Determinantal Point Processes with Python (2018) arXiv
  7. Meyer, Sebastian: Self-exciting point processes: infections and implementations (2018)
  8. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  9. Tao, Long; Weber, Karoline E.; Arai, Kensuke; Eden, Uri T.: A common goodness-of-fit framework for neural population models using marked point process time-rescaling (2018)
  10. Baddeley, Adrian; Hardegen, Andrew; Lawrence, Thomas; Milne, Robin K.; Nair, Gopalan; Rakshit, Suman: On two-stage Monte Carlo tests of composite hypotheses (2017)
  11. Biscio, Christophe Ange Napoléon; Lavancier, Frédéric: Contrast estimation for parametric stationary determinantal point processes (2017)
  12. Coeurjolly, Jean-François: Median-based estimation of the intensity of a spatial point process (2017)
  13. Illian, Janine B.; Burslem, David F. R. P.: Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (2017)
  14. 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)
  15. Lledó, Josep; Pavía, Jose M.; Morillas, Francisco G.: Assessing implicit hypotheses in life table construction (2017)
  16. McSwiggan, Greg; Baddeley, Adrian; Nair, Gopalan: Kernel density estimation on a linear network (2017)
  17. Prokešová, Michaela; Dvořák, Jiří; Vedel Jensen, Eva B.: Two-step estimation procedures for inhomogeneous shot-noise Cox processes (2017)
  18. Robinson, Andrew; Turner, Katharine: Hypothesis testing for topological data analysis (2017)
  19. Turner, Rolf; Jeffs, Celeste: An extension of Monte Carlo hypothesis tests (2017)
  20. Baddeley, Adrian; Rubak, Ege; Turner, Rolf: Spatial point patterns: methodology and applications with R (2016)

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