sppmix: Poisson point process modeling using normal mixture models

Athanasios C. Micheas () and Jiaxun Chen
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Athanasios C. Micheas: University of Missouri-Columbia
Jiaxun Chen: University of Missouri-Columbia

Computational Statistics, 2018, vol. 33, issue 4, No 9, 1767-1798

Abstract: Abstract This paper describes the package sppmix for the statistical environment R. The sppmix package implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be a multiple of a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context. In addition, several approaches have been implemented in order to handle the standard label switching issue which arises in any modeling approach involving mixture models. We adapt a hierarchical Bayesian framework in order to model the intensity surfaces and have implemented two major algorithms in order to estimate the parameters of the mixture models involved: the data augmentation and the birth–death Markov chain Monte Carlo (DAMCMC and BDMCMC). We used C++ (via the Rcpp package) in order to implement the most computationally intensive algorithms.

Keywords: Birth–death MCMC; C++ programming language; Data-augmentation MCMC; Hierarchical Bayesian models; Marked point process via conditioning; Poisson point process; R programming language (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-018-0805-z

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