Year: 2022
Type: article
Abstract: This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian pointreferenced outcomes that are spatially indexed. The package implements several Markov chain Monte Carlo (MCMC) and MCMC-free nearest neighbor Gaussian process (NNGP) models for inference about large spatial data. Non-Gaussian outcomes are modeled using a NNGP Pólya-Gamma latent variable. OpenMP parallelization options are provided to take advantage of multiprocessor systems. Package features are illustrated using simulated and real data sets.
Source: Journal of Statistical Software
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
Awards: Collaborative Research: MRA: Estimating and forecasting nonstationary, multi-scale climate and land-use effects on avian communities, Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets, Highly Multivariate Geo-Statistics Using Graphical Models, III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions, CAREER: Advancements in Spatio-temporal Modeling and Education in Support of NEON and Large-scale and Long-term Ecological Research, Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems, G7006908063, Collaborative Research: Statistical Inference for High-dimensional Spatial-Temporal Process Models, Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems
OpenAlex ID: W3154070451