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spBayes (version 0.1-5)

Univariate and Multivariate Spatial Modeling

Description

spBayes fits univariate and multivariate models with Markov chain Monte Carlo (MCMC).

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Version

Install

install.packages('spBayes')

Monthly Downloads

668

Version

0.1-5

License

GPL (>= 2)

Maintainer

Andrew Finley

Last Published

January 8th, 2010

Functions in spBayes (0.1-5)

hexGrid

Generates a hexagon tessellation within a bounding box
covGivens

Returns covariance matrix C given components of P and L in C=PLP'
mkMvX

Make a multivariate design matrix
FBC07.dat

Synthetic multivariate data with spatial and non-spatial variance structures
mvCovInvLogDet

Utility function for constructing multivariate predictive process covariance matrices
synthetic.dat

Synthetic univariate data used for illustrations
Zurich.dat

Zurichberg Forest inventory data
iDist

Euclidean distance matrix
covInvLogDet

Utility function for constructing predictive process covariance matrices
bayesGeostatExact

Simple Bayesian spatial linear model with fixed semivariogram parameters
pointsInPoly

Finds points in a polygon
spGGT

Function for fitting univariate and multivariate Bayesian spatial regression models
spLM

Function for fitting univariate Bayesian spatial regression models
bayesLMRef

Simple Bayesian linear model with non-informative priors
BEF.dat

Bartlett Experimental Forest inventory data
WEF.dat

Western Experimental Forest inventory data
spMvLM

Function for fitting multivariate Bayesian spatial regression models
FORMGMT.dat

Data used for illustrations
spGLM

Function for fitting univariate Bayesian generalized linear spatial regression models
adaptMetropGibbs

Adaptive Metropolis within Gibbs algorithm
spPredict

Prediction for new points given a model object
bayesLMConjugate

Simple Bayesian linear model via the Normal/inverse-Gamma conjugate
prior

Creates prior distribution definitions
spMvGLM

Function for fitting multivariate Bayesian generalized linear spatial regression models
spDIC

Calculates DIC and associated statistics given a model object