Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) < http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) . Model fitting employs a Fisher scoring algorithm described in Guinness (2019) .

GpGp

GpGp is an R package for fast approximate Gaussian process computation. The package includes implementations of the Vecchia's (1988) original approximation, as well as several updates to it, including the reordered and grouped versions of the approximation outlined in Guinness (2018).

Installing

The package can be installed from CRAN with the usual R command

install.packages("GpGp")

or directly from Github for the latest version

devtools::install_github("joeguinness/GpGp")

Basic Use

The main function for fitting models is called 'fit_model', and the main function for doing predictions is called 'predictions'.

See this youtube video for a tutorial: https://www.youtube.com/watch?v=phyB4n0CDWg&t=4s