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 122 articles , 1 standard article )

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  1. Biscio, Christophe A. N.; Chenavier, Nicolas; Hirsch, Christian; Svane, Anne Marie: Testing goodness of fit for point processes via topological data analysis (2020)
  2. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  3. Moka, Sarat B.; Kroese, Dirk P.: Perfect sampling for Gibbs point processes using partial rejection sampling (2020)
  4. Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes: DRHotNet: An R package for detecting differential risk hotspots on a linear network (2019) arXiv
  5. Gamerman, Dani: Spatiotemporal point processes: regression, model specifications and future directions (2019)
  6. Hingee, Kassel; Baddeley, Adrian; Caccetta, Peter; Nair, Gopalan: Computation of lacunarity from covariance of spatial binary maps (2019)
  7. Lledó, Josep; Pavía, Jose M.; Morillas-Jurado, Francisco G.: Incorporating big microdata in life table construction: A hypothesis-free estimator (2019)
  8. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  9. Xu, Ganggang; Waagepetersen, Rasmus; Guan, Yongtao: Stochastic quasi-likelihood for case-control point pattern data (2019)
  10. Ceyhan, Elvan: A contingency table approach based on nearest neighbour relations for testing self and mixed correspondence (2018)
  11. Choiruddin, Achmad; Coeurjolly, Jean-François; Letué, Frédérique: Convex and non-convex regularization methods for spatial point processes intensity estimation (2018)
  12. Davies, Tilman M.; Baddeley, Adrian: Fast computation of spatially adaptive kernel estimates (2018)
  13. Guillaume Gautier, Rémi Bardenet, Michal Valko: DPPy: Sampling Determinantal Point Processes with Python (2018) arXiv
  14. Meyer, Sebastian: Self-exciting point processes: infections and implementations (2018)
  15. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  16. 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)
  17. Baddeley, Adrian; Hardegen, Andrew; Lawrence, Thomas; Milne, Robin K.; Nair, Gopalan; Rakshit, Suman: On two-stage Monte Carlo tests of composite hypotheses (2017)
  18. Biscio, Christophe Ange Napoléon; Lavancier, Frédéric: Contrast estimation for parametric stationary determinantal point processes (2017)
  19. Coeurjolly, Jean-François: Median-based estimation of the intensity of a spatial point process (2017)
  20. Illian, Janine B.; Burslem, David F. R. P.: Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (2017)

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