lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes

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http://www.jstatsoft.org/v52/i04

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes. / Taylor, Benjamin; Davies, Tilman; Rowlingson, Barry et al.

In: Journal of Statistical Software, Vol. 52, No. 4, 02.2013.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{66205a2784a04ba2b3b950a318e3c5c6,

title = "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes",

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 modeling 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.",

author = "Benjamin Taylor and Tilman Davies and Barry Rowlingson and Peter Diggle",

year = "2013",

month = feb,

language = "English",

volume = "52",

journal = "Journal of Statistical Software",

issn = "1548-7660",

publisher = "University of California at Los Angeles",

number = "4",

}

RIS

TY - JOUR

T1 - lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes

AU - Taylor, Benjamin

AU - Davies, Tilman

AU - Rowlingson, Barry

AU - Diggle, Peter

PY - 2013/2

Y1 - 2013/2

N2 - 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 modeling 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.

AB - 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 modeling 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.

M3 - Journal article

VL - 52

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 4

ER -

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