Lgcp: Inference with spatial and spatio-temporal log-gaussian cox processes in R

Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle

Research output: Contribution to journalArticle

Abstract

This paper introduces an R package for spatial and spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo (MCMC) and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The modelling framework and details of inferential procedures are first presented before a tour of lgcp functionality is given via a walk-through data-analysis. Topics covered include reading in and converting data, estimation of the key components and parameters of the model, specifying output and simulation quantities, computation of Monte Carlo expectations, post-processing and simulation of data sets.

Original languageEnglish (US)
Pages (from-to)1-40
Number of pages40
JournalJournal of Statistical Software
Volume52
Issue number4
Publication statusPublished - Feb 2013
Externally publishedYes

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Keywords

  • Cox process
  • R
  • Spatio-temporal point process

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Taylor, B. M., Davies, T. M., Rowlingson, B. S., & Diggle, P. J. (2013). Lgcp: Inference with spatial and spatio-temporal log-gaussian cox processes in R. Journal of Statistical Software, 52(4), 1-40.