R package for Nearest Neighbor Gaussian Process models

01/24/2020
by   Andrew O. Finley, et al.
0

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 point-referenced 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 Polya-Gamma latent variable. OpenMP parallelization options are provided to take advantage of multiprocessor systems. Package features are illustrated using simulated and real data sets.

READ FULL TEXT

page 9

page 19

page 20

page 27

page 31

page 33

research
03/07/2019

Bayesian spatially varying coefficient models in the spBayes R package

This paper describes and illustrates the addition of the spSVC function ...
research
08/18/2019

Block Nearest Neighboor Gaussian processes for large datasets

This work develops a valid spatial block-Nearest Neighbor Gaussian proce...
research
02/23/2022

baker: An R package for Nested Partially-Latent Class Models

This paper describes and illustrates the functionality of the baker R pa...
research
10/02/2020

Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation

Even though Nearest Neighbor Gaussian Processes (NNGP) alleviate conside...
research
03/22/2022

Nonstationary Nearest Neighbor Gaussian Process: hierarchical model architecture and MCMC sampling

Nonstationary spatial modeling presents several challenges including, bu...
research
06/18/2021

LNIRT: An R Package for Joint Modeling of Response Accuracy and Times

In computer-based testing it has become standard to collect response acc...
research
02/26/2023

Recursive Nearest Neighbor Co-Kriging Models for Big Multiple Fidelity Spatial Data Sets

Large datasets are daily gathered from different remote sensing platform...

Please sign up or login with your details

Forgot password? Click here to reset