| GpGp {GpGp} | R Documentation |
GpGp: Fast Gaussian Process Computing.
Description
Vecchia's (1988) Gaussian process approximation has emerged among its competitors as a leader in computational scalability and accuracy. This package includes implementations of the original approximation, as well as several updates to it, including the reordered and grouped versions of the approximation outlined in Guinness (2018) and the Fisher scoring algorithm described in Guinness (2019). The package supports spatial models, spatial-temporal models, models on spheres, and some nonstationary models.
Details
The main functions of the package are fit_model,
and predictions.
fit_model returns estimates of covariance parameters
and linear mean parameters. The user is expected to select a covariance function
and specify it with a string. Currently supported covariance functions are
If there are
covariates, they can be expressed via a design matrix X, each row containing
the covariates corresponding to the same row in locs.
For predictions, the user should specify prediction locations
locs_pred and a prediction design matrix X_pred.
The vignettes are intended to be helpful for getting a sense of how these functions work.
For Gaussian process researchers, the package also provides access to functions for computing the likelihood, gradient, and Fisher information with respect to covariance parameters; reordering functions, nearest neighbor-finding functions, grouping (partitioning) functions, and approximate simulation functions.
Author(s)
Maintainer: Joseph Guinness joeguinness@gmail.com
Authors:
Matthias Katzfuss katzfuss@gmail.com
Youssef Fahmy yf297@cornell.edu