Univariate and Multivariate Spatial-Temporal Modeling
Adaptive Metropolis within Gibbs algorithm
Simple Bayesian spatial linear model with fixed semivariogram paramete...
Simple Bayesian linear model via the Normal/inverse-Gamma conjugate
Simple Bayesian linear model with non-informative priors
Bartlett Experimental Forest inventory data
Synthetic multivariate data with spatial and non-spatial variance stru...
Data used for illustrations
Euclidean distance matrix
Make a multivariate design matrix
Function for calculating univariate and multivariate covariance matric...
Monthly weather station temperature data across the Northeastern US
Observations of ozone concentration levels.
Observed and modeled PM10 concentrations across Europe
Finds points in a polygon
Model fit diagnostics
Function for fitting univariate Bayesian dynamic space-time regression...
Function for fitting univariate Bayesian generalized linear spatial re...
Function for fitting univariate Bayesian spatial regression models
Function for fitting multivariate generalized linear Bayesian spatial ...
Function for fitting multivariate Bayesian spatial regression models t...
Function for fitting multivariate Bayesian generalized linear spatial ...
Function for fitting multivariate Bayesian spatial regression models
Function for new locations given a model object
Function for recovering regression coefficients and spatial random eff...
Function for fitting univariate Bayesian spatially-varying coefficient...
Synthetic data from a space-varying coefficients model
Western Experimental Forest inventory data
Zurichberg Forest inventory data
Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.