SpBayes: An R package for univariate and multivariate hierarchical point-referenced spatial models

Andrew O. Finley, Sudipto Banerjee, Brad Carlin

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-24
Number of pages24
JournalJournal of Statistical Software
Issue number4
StatePublished - Apr 2007


  • Bayesian inference
  • Coregionalization
  • Kriging
  • Markov chain Monte Carlo
  • Multivariate spatial process
  • R


Dive into the research topics of 'SpBayes: An R package for univariate and multivariate hierarchical point-referenced spatial models'. Together they form a unique fingerprint.

Cite this