spBayes fits Bayesian univariate and multivariate spatial and
spatio-temporal regression models for point-referenced data. The package
provides MCMC-based model fitting, posterior recovery of spatial random
effects, prediction at new locations, model diagnostics, and utilities for
building spatial covariance matrices.
Install the CRAN release with:
install.packages("spBayes")Install the development version from GitHub with:
remotes::install_github("finleya/spBayes")library(spBayes)
set.seed(1)
n <- 50
coords <- cbind(runif(n), runif(n))
x <- rnorm(n)
y <- 1 + 0.5 * x + rnorm(n)
fit <- spLM(
y ~ x,
coords = coords,
starting = list(phi = 3, sigma.sq = 1, tau.sq = 1),
tuning = list(phi = 0.1, sigma.sq = 0.1, tau.sq = 0.1),
priors = list(
phi.Unif = c(0.1, 30),
sigma.sq.IG = c(2, 1),
tau.sq.IG = c(2, 1)
),
cov.model = "exponential",
n.samples = 100,
verbose = FALSE
)
summary(fit$p.theta.samples)The package provides:
- Gaussian spatial linear models with
spLM(). - Binomial and Poisson spatial generalized linear models with
spGLM(). - Multivariate spatial linear and generalized linear models.
- Spatially varying coefficient and dynamic spatio-temporal models.
- Predictive-process models for larger point-referenced data.
- Posterior recovery, prediction, diagnostics, and covariance utilities.
If you use spBayes, please cite:
Finley, A. O., Banerjee, S., and Carlin, B. P. (2007). spBayes: An R Package for Univariate and Multivariate Hierarchical Point-Referenced Spatial Models. Journal of Statistical Software, 19(4), 1-24.
Finley, A. O., Banerjee, S., and Gelfand, A. E. (2015). spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models. Journal of Statistical Software, 63(13), 1-28. doi:10.18637/jss.v063.i13.
Finley, A. O. and Banerjee, S. (2020). Bayesian spatially varying coefficient models in the spBayes R package. Environmental Modelling & Software, 125, 104608. doi:10.1016/j.envsoft.2019.104608.