spNNGP

R package spNNGP: Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes. Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924> and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.

Keywords for this software

MCMC
Gaussian process
big data
R package
arXiv_publication
nearest-neighbor Gaussian processes
low-rank approximation
Bayesian inference
R
arXiv_stat.CO
NNGP
latent spatial processes
Gaussian processes
Nearest Neighbor Gaussian Process
kriging
parallel computing

References in zbMATH (referenced in 3 articles , 1 standard article )

Showing results 1 to 3 of 3.

Sorted by year (citations)

Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
Zhang, Lu; Datta, Abhirup; Banerjee, Sudipto: Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments (2019)