Comprehensive opensource toolbox for analysing Spatial Point Patterns. Focused mainly on twodimensional point patterns, including multitype/marked points, in any spatial region. Also supports threedimensional point patterns, spacetime point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, modelfitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, Kfunctions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with crossvalidated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chisquared, KolmogorovSmirnov, Monte Carlo, DiggleCressieLoosmoreFord, DaoGenton, twostage Monte Carlo) and tests for covariate effects (CoxBermanWallerLawson, KolmogorovSmirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, NeymanScott cluster processes, and determinantal point processes. Models may involve dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and QQ plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Package details 


Author  Adrian Baddeley <[email protected]>, Rolf Turner <[email protected]> and Ege Rubak <[email protected]>, with substantial contributions of code by Kasper Klitgaard Berthelsen; Ottmar Cronie; Yongtao Guan; Ute Hahn; Abdollah Jalilian; MarieColette van Lieshout; Greg McSwiggan; Tuomas Rajala; Suman Rakshit; Dominic Schuhmacher; Rasmus Waagepetersen; and Hangsheng Wang. Additional contributions by M. Adepeju; C. Anderson; Q.W. Ang; M. Austenfeld; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; A. Bilgrau; L. Bischof; C. Biscio; R. Bivand; J.M. Blanco Moreno; F. Bonneu; J. Burgos; S. Byers; Y.M. Chang; J.B. Chen; I. Chernayavsky; Y.C. Chin; B. Christensen; J.F. Coeurjolly; K. Colyvas; R. Constantine; R. Corria Ainslie; R. Cotton; M. de la Cruz; P. Dalgaard; M. D'Antuono; S. Das; T. Davies; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; A. ElGabbas; B. Fandohan; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. FunwiGabga; O. Garcia; A. Gault; J. Geldmann; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M.B. Hansen; M. Hazelton; J. Heikkinen; M. Hering; M. Herrmann; P. Hewson; K. Hingee; K. Hornik; P. Hunziker; J. Hywood; R. Ihaka; C. Icos; A. Jammalamadaka; R. JohnChandran; D. Johnson; M. Khanmohammadi; R. Klaver; P. Kovesi; L. KozmianLedward; M. Kuhn; J. Laake; F. Lavancier; T. Lawrence; R.A. Lamb; J. Lee; G.P. Leser; H.T. Li; G. Limitsios; A. Lister; B. Madin; M. Maechler; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; F. Mestre; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; E. Miranda; J. Moller; M. Moradi; V. Morera Pujol; E. Mudrak; G.M. Nair; N. Najari; N. Nava; L.S. Nielsen; F. Nunes; J.R. Nyengaard; J. Oehlschlaegel; T. Onkelinx; S. O'Riordan; E. Parilov; J. Picka; N. Picard; M. Porter; S. Protsiv; A. Raftery; S. Rakshit; B. Ramage; P. Ramon; X. Raynaud; N. Read; M. Reiter; I. Renner; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; C. Schank; K. Schladitz; S. Schutte; B.T. Scott; O. Semboli; F. Semecurbe; V. Shcherbakov; G.C. Shen; P. Shi; H.J. Ship; T.L. Silva; I.M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; L. Torres; B. Turlach; T. Tvedebrink; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; S. Voss; S. Wagner; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M. Woringer; M.E. Zamboni and A. Zeileis. 
Date of publication  20171121 08:39:44 UTC 
Maintainer  Adrian Baddeley <[email protected]> 
License  GPL (>= 2) 
Version  1.540 
URL  http://www.spatstat.org 
Package repository  View on CRAN 
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