Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes • spNNGP

spNNGP fits univariate Bayesian spatial regression models for large datasets using nearest neighbor Gaussian processes (NNGPs). NNGP models replace dense Gaussian process calculations with sparse neighbor-based approximations, making fully Bayesian spatial regression practical for datasets that are too large for conventional Gaussian process methods.

Installation

Install the CRAN release with:

install.packages("spNNGP")

Install the development version from GitHub with:

remotes::install_github("finleya/spNNGP")

Basic Use

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)

Functionality

The package provides:

Response and latent NNGP models for Gaussian outcomes.
Latent NNGP models for binomial outcomes.
Conjugate NNGP models for faster Gaussian analyses.
Prediction and posterior summaries through familiar R methods.
MCMC diagnostics and model summaries for fitted spatial models.

Citations

If you use spNNGP, please cite:

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. Preprint: https://arxiv.org/pdf/1702.00434