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spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models

Author

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  • Finley, Andrew O.
  • Banerjee, Sudipto
  • Carlin, Bradley P.

Abstract

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.

Suggested Citation

  • Finley, Andrew O. & Banerjee, Sudipto & Carlin, Bradley P., 2007. "spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i04).
  • Handle: RePEc:jss:jstsof:v:019:i04
    DOI: http://hdl.handle.net/10.18637/jss.v019.i04
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    Cited by:

    1. repec:spr:testjl:v:27:y:2018:i:4:d:10.1007_s11749-018-0619-x is not listed on IDEAS
    2. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    3. Maria Terres & Alan Gelfand, 2015. "Using spatial gradient analysis to clarify species distributions with application to South African protea," Journal of Geographical Systems, Springer, vol. 17(3), pages 227-247, July.
    4. repec:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9150-3 is not listed on IDEAS
    5. Yi Liu & Gavin Shaddick & James V. Zidek, 0. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-23.
    6. repec:spr:advdac:v:13:y:2019:i:3:d:10.1007_s11634-018-0341-2 is not listed on IDEAS
    7. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    8. repec:bla:biomet:v:74:y:2018:i:3:p:863-873 is not listed on IDEAS

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