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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

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Version

Install

install.packages('GpGp')

Monthly Downloads

609

Version

1.0.0

License

MIT + file LICENSE

Maintainer

Joseph Guinness

Last Published

December 18th, 2025

Functions in GpGp (1.0.0)

matern25_isotropic

Isotropic Matern covariance function, smoothness = 2.5

exponential_spheretime

Exponential covariance function on sphere x time

exponential_spheretime_warp

Deformed exponential covariance function on sphere

find_ordered_nn

Find ordered nearest neighbors.

find_ordered_nn_brute

Naive brute force nearest neighbor finder

Windspeed measurements from Jason-3 Satellite

matern45_isotropic

Isotropic Matern covariance function, smoothness = 4.5

matern15_isotropic

Isotropic Matern covariance function, smoothness = 1.5

matern_sphere_warp

Deformed Matern covariance function on sphere

matern_spheretime

Matern covariance function on sphere x time

Multiply approximate Cholesky by a vector

GpGp: Fast Gaussian Process Computing.

matern_spacetime_categorical_local

Space-Time Matern covariance function with local random effects for categories

expit function and integral of expit function

compute gradient of spherical harmonics functions

exponential_anisotropic2D

Geometrically anisotropic exponential covariance function (two dimensions)

Compute Gaussian process predictions using Vecchia's approximations

summary.GpGp_fit

Print summary of GpGp fit

exponential_sphere

Isotropic exponential covariance function on sphere

exponential_sphere_warp

Deformed exponential covariance function on sphere

Isotropic Matern covariance function on sphere

penalize large values of parameter: penalty, 1st deriative, 2nd derivative

order_middleout

Middle-out ordering

Fisher scoring algorithm

exponential_isotropic

Isotropic exponential covariance function

exponential_nonstat_var

Isotropic exponential covariance function, nonstationary variances

matern_nonstat_var

Isotropic Matern covariance function, nonstationary variances

matern_scaledim

Matern covariance function, different range parameter for each dimension

matern_anisotropic2D

Geometrically anisotropic Matern covariance function (two dimensions)

Estimate mean and covariance parameters

matern45_scaledim

Matern covariance function, smoothess = 3.5, different range parameter for each dimension

test_likelihood_object

test likelihood object for NA or Inf values

Entries of inverse Cholesky approximation

matern_isotropic

Isotropic Matern covariance function

matern35_isotropic

Isotropic Matern covariance function, smoothness = 3.5

matern25_scaledim

Matern covariance function, smoothess = 2.5, different range parameter for each dimension

matern_categorical

Isotropic Matern covariance function with random effects for categories

penalize small values of parameter: penalty, 1st deriative, 2nd derivative

Multiply transpose of approximate inverse Cholesky by a vector

Ocean temperatures from Argo profiling floats

vecchia_grouped_meanzero_loglik

Grouped Vecchia approximation to the Gaussian loglikelihood, zero mean

penalize small values of log parameter: penalty, 1st deriative, 2nd derivative

vecchia_grouped_profbeta_loglik_grad_info

Grouped Vecchia loglikelihood, gradient, Fisher information

vecchia_grouped_profbeta_loglik

Grouped Vecchia approximation, profiled regression coefficients

matern_anisotropic3D

Geometrically anisotropic Matern covariance function (three dimensions)

vecchia_profbeta_loglik

Vecchia's approximation to the Gaussian loglikelihood, with profiled regression coefficients.

matern_spheretime_warp

Deformed Matern covariance function on sphere

order_coordinate

Sorted coordinate ordering

matern_anisotropic3D_alt

Geometrically anisotropic Matern covariance function (three dimensions, alternate parameterization)

vecchia_meanzero_loglik

Vecchia's approximation to the Gaussian loglikelihood, zero mean

exponential_anisotropic3D

Geometrically anisotropic exponential covariance function (three dimensions)

exponential_anisotropic3D_alt

Geometrically anisotropic exponential covariance function (three dimensions, alternate parameterization)

Approximate GP simulation

fast_Gp_sim_Linv

Approximate GP simulation with specified Linverse

matern_spacetime

Spatial-Temporal Matern covariance function

matern_spacetime_categorical

Space-Time Matern covariance function with random effects for categories

order_dist_to_point

Distance to specified point ordering

Maximum minimum distance ordering

vecchia_profbeta_loglik_grad_info

Vecchia's loglikelihood, gradient, and Fisher information

Conditional Simulation using Vecchia's approximation

condition_number

compute condition number of matrix

Multiply approximate inverse Cholesky by a vector

Multiply transpose of approximate Cholesky by a vector

exponential_spacetime

Spatial-Temporal exponential covariance function

exponential_scaledim

Exponential covariance function, different range parameter for each dimension

matern35_scaledim

Matern covariance function, smoothess = 3.5, different range parameter for each dimension

Automatic grouping (partitioning) of locations

get_start_parms

get default starting values of covariance parameters

get link function, whether locations are lonlat and space time

get penalty function

matern15_scaledim

Matern covariance function, smoothess = 1.5, different range parameter for each dimension