spBayes
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models. Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude–longitude, Easting–Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.
Keywords for this software
References in zbMATH (referenced in 254 articles , 1 standard article )
Showing results 1 to 20 of 254.
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- Acosta, Jonathan; Vallejos, Ronny: Effective sample size for spatial regression models (2018)
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- Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
- Gonçalves, Flávio B.; Gamerman, Dani: Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2018)
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- Liu, Ying; Flegal, James M.: Weighted batch means estimators in Markov chain Monte Carlo (2018)
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- Müeller, Peter; Quintana, Fernando A.; Page, Garritt: Nonparametric Bayesian inference in applications (2018)