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spNNGP fits univariate Bayesian spatial regression
models for large datasets using nearest neighbor Gaussian processes.
The package supports response and latent NNGP models for Gaussian outcomes, latent NNGP models for binomial outcomes, and conjugate NNGP models.
Install the development version from GitHub after the repository is published:
remotes::install_github("finleya/spNNGP")library(spNNGP)
set.seed(1)
n <- 100
coords <- cbind(runif(n), runif(n))
x <- rnorm(n)
y <- 1 + 0.5 * x + rnorm(n)
fit <- spNNGP(
y ~ x,
coords = coords,
method = "latent",
n.neighbors = 10,
starting = list(beta = c(0, 0), sigma.sq = 1, tau.sq = 1, phi = 3),
tuning = list(phi = 0.1),
priors = list(
sigma.sq.IG = c(2, 1),
tau.sq.IG = c(2, 1),
phi.Unif = c(0.1, 30)
),
n.samples = 100,
n.omp.threads = 1,
verbose = FALSE
)
summary(fit)Finley, A. O., Datta, A., and Banerjee, S. (2022). spNNGP R Package for Nearest Neighbor Gaussian Process Models. Journal of Statistical Software, 103(5). doi:10.18637/jss.v103.i05.
Finley, A. O., Datta, A., Cook, B. D., Morton, D. C., Andersen, H. E., and Banerjee, S. (2019). Efficient algorithms for Bayesian nearest neighbor Gaussian processes. Journal of Computational and Graphical Statistics, 28(2), 401-414. doi:10.1080/10618600.2018.1537924.
Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E. (2016). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514), 800-812. doi:10.1080/01621459.2015.1044091.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.