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spBayes (version 0.2-0)
Univariate and Multivariate Spatial Modeling
Description
spBayes fits univariate and multivariate models with Markov chain Monte Carlo (MCMC).
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Install
install.packages('spBayes')
Monthly Downloads
822
Version
0.2-0
License
GPL (>= 2)
Maintainer
Andrew Finley
Last Published
August 23rd, 2010
Functions in spBayes (0.2-0)
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bayesGeostatExact
Simple Bayesian spatial linear model with fixed semivariogram parameters
FORMGMT.dat
Data used for illustrations
FBC07.dat
Synthetic multivariate data with spatial and non-spatial variance structures
spPredict
Prediction for new locations given a model object
spMvLM
Function for fitting multivariate Bayesian spatial regression models
hexGrid
Generates a hexagon tessellation within a bounding box
synthetic.dat
Synthetic univariate data used for illustrations
pointsInPoly
Finds points in a polygon
mvCovInvLogDet
Utility function for constructing multivariate predictive process covariance matrices
bayesLMRef
Simple Bayesian linear model with non-informative priors
spLM
Function for fitting univariate Bayesian spatial regression models
bayesLMConjugate
Simple Bayesian linear model via the Normal/inverse-Gamma conjugate
Zurich.dat
Zurichberg Forest inventory data
spGLM
Function for fitting univariate Bayesian generalized linear spatial regression models
WEF.dat
Western Experimental Forest inventory data
BEF.dat
Bartlett Experimental Forest inventory data
covGivens
Returns covariance matrix C given components of P and L in C=PLP'
spGGT
Function for fitting univariate and multivariate Bayesian spatial regression models
spMvGLM
Function for fitting multivariate Bayesian generalized linear spatial regression models
prior
Creates prior distribution definitions
covInvLogDet
Utility function for constructing predictive process covariance matrices
mvLM
Function for fitting multivariate Bayesian regression models
iDist
Euclidean distance matrix
spDiag
Model fit diagnostics DIC and GP
mkMvX
Make a multivariate design matrix
adaptMetropGibbs
Adaptive Metropolis within Gibbs algorithm