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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

482

Version

0.5.0

License

MIT + file LICENSE

Maintainer

Joseph Guinness

Last Published

February 18th, 2024

Functions in GpGp (0.5.0)

Approximate GP simulation

get_start_parms

get default starting values of covariance parameters

fast_Gp_sim_Linv

Approximate GP simulation with specified Linverse

find_ordered_nn

Find ordered nearest neighbors.

find_ordered_nn_brute

Naive brute force nearest neighbor finder

exponential_spheretime_warp

Deformed exponential covariance function on sphere

Windspeed measurements from Jason-3 Satellite

matern15_isotropic

Isotropic Matern covariance function, smoothness = 1.5

matern15_scaledim

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

matern25_isotropic

Isotropic Matern covariance function, smoothness = 2.5

matern25_scaledim

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

Automatic grouping (partitioning) of locations

matern35_isotropic

Isotropic Matern covariance function, smoothness = 3.5

matern_nonstat_var

Isotropic Matern covariance function, nonstationary variances

Estimate mean and covariance parameters

matern_anisotropic2D

Geometrically anisotropic Matern covariance function (two dimensions)

matern_spacetime

Spatial-Temporal Matern covariance function

matern_spacetime_categorical

Space-Time Matern covariance function with random effects for categories

matern_scaledim

Matern covariance function, different range parameter for each dimension

matern45_scaledim

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

Fisher scoring algorithm

matern_spacetime_categorical_local

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

matern_anisotropic3D_alt

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

Maximum minimum distance ordering

matern_anisotropic3D

Geometrically anisotropic Matern covariance function (three dimensions)

order_dist_to_point

Distance to specified point ordering

order_coordinate

Sorted coordinate ordering

matern_spheretime_warp

Deformed Matern covariance function on sphere

Isotropic Matern covariance function on sphere

matern_sphere_warp

Deformed Matern covariance function on sphere

Entries of inverse Cholesky approximation

vecchia_grouped_profbeta_loglik

Grouped Vecchia approximation, profiled regression coefficients

vecchia_grouped_meanzero_loglik

Grouped Vecchia approximation to the Gaussian loglikelihood, zero mean

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

order_middleout

Middle-out ordering

get link function, whether locations are lonlat and space time

get penalty function

vecchia_profbeta_loglik_grad_info

Vecchia's loglikelihood, gradient, and Fisher information

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

matern35_scaledim

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

matern45_isotropic

Isotropic Matern covariance function, smoothness = 4.5

Compute Gaussian process predictions using Vecchia's approximations

matern_isotropic

Isotropic Matern covariance function

matern_categorical

Isotropic Matern covariance function with random effects for categories

compute gradient of spherical harmonics functions

vecchia_profbeta_loglik

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

vecchia_meanzero_loglik

Vecchia's approximation to the Gaussian loglikelihood, zero mean

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

summary.GpGp_fit

Print summary of GpGp fit

test_likelihood_object

test likelihood object for NA or Inf values

vecchia_grouped_profbeta_loglik_grad_info

Grouped Vecchia loglikelihood, gradient, Fisher information

matern_spheretime

Matern covariance function on sphere x time

Multiply transpose of approximate Cholesky by a vector

exponential_anisotropic2D

Geometrically anisotropic exponential covariance function (two dimensions)

Multiply transpose of approximate inverse Cholesky by a vector

Conditional Simulation using Vecchia's approximation

condition_number

compute condition number of matrix

GpGp: Fast Gaussian Process Computing.

Ocean temperatures from Argo profiling floats

expit function and integral of expit function

Multiply approximate inverse Cholesky by a vector

Multiply approximate Cholesky by a vector

exponential_nonstat_var

Isotropic exponential covariance function, nonstationary variances

exponential_anisotropic3D

Geometrically anisotropic exponential covariance function (three dimensions)

exponential_spheretime

Exponential covariance function on sphere x time

exponential_anisotropic3D_alt

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

exponential_sphere_warp

Deformed exponential covariance function on sphere

exponential_sphere

Isotropic exponential covariance function on sphere

exponential_isotropic

Isotropic exponential covariance function

exponential_scaledim

Exponential covariance function, different range parameter for each dimension

exponential_spacetime

Spatial-Temporal exponential covariance function