Spatial Statistics via spBayes

Spatial statistics is a large collections of tools with different historical developemental settings and results. History aside, one area that has been exploited recently is the class of models for univariate and multivariate hierarchical point-referenced spatial regression models for gaussian and non-guassian responses. The approach taken in spBayes is through generalized hierarchical random effects models estimated via Monte Carlo Markov Chain(MCMC) sampling. Spatial effects are captured via a zero centered multivariate guassian process where a variety of spatial covariance structures can be specified. A new R package http://www.r-project.org called spBayes addresses this area with more success than previous attempts. One advantage of the MCMC approach is the ability to estimate functionals. In particular, a recent entropy based measure call DIC, Deviance Information Criterion, is available to help consider the viability of competing nested or non nested models conditional on the same set of data.

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