Fast Gaussian Process Computation Using Vecchia's Approximation

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Documentation for package ‘GpGp’ version 0.5.1

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A C D E F G I J L M O P S T V

GpGp-package GpGp: Fast Gaussian Process Computing.

-- A --

argo2016 Ocean temperatures from Argo profiling floats

-- C --

condition_number compute condition number of matrix
cond_sim Conditional Simulation using Vecchia's approximation

-- D --

ddpen_hi penalize large values of parameter: penalty, 1st deriative, 2nd derivative
ddpen_lo penalize small values of parameter: penalty, 1st deriative, 2nd derivative
ddpen_loglo penalize small values of log parameter: penalty, 1st deriative, 2nd derivative
dpen_hi penalize large values of parameter: penalty, 1st deriative, 2nd derivative
dpen_lo penalize small values of parameter: penalty, 1st deriative, 2nd derivative
dpen_loglo penalize small values of log parameter: penalty, 1st deriative, 2nd derivative
d_exponential_anisotropic2D Geometrically anisotropic exponential covariance function (two dimensions)
d_exponential_anisotropic3D Geometrically anisotropic exponential covariance function (three dimensions)
d_exponential_anisotropic3D_alt Geometrically anisotropic exponential covariance function (three dimensions, alternate parameterization)
d_exponential_isotropic Isotropic exponential covariance function
d_exponential_nonstat_var Isotropic exponential covariance function, nonstationary variances
d_exponential_scaledim Exponential covariance function, different range parameter for each dimension
d_exponential_spacetime Spatial-Temporal exponential covariance function
d_exponential_sphere Isotropic exponential covariance function on sphere
d_exponential_spheretime Exponential covariance function on sphere x time
d_exponential_spheretime_warp Deformed exponential covariance function on sphere
d_exponential_sphere_warp Deformed exponential covariance function on sphere
d_matern15_isotropic Isotropic exponential covariance function
d_matern15_scaledim Matern covariance function, smoothess = 1.5, different range parameter for each dimension
d_matern25_isotropic Isotropic exponential covariance function
d_matern25_scaledim Matern covariance function, smoothess = 2.5, different range parameter for each dimension
d_matern35_isotropic Isotropic Matern covariance function, smoothness = 3.5
d_matern35_scaledim Matern covariance function, smoothess = 3.5, different range parameter for each dimension
d_matern45_isotropic Isotropic Matern covariance function, smoothness = 3.5
d_matern45_scaledim Matern covariance function, smoothess = 3.5, different range parameter for each dimension
d_matern_anisotropic2D Geometrically anisotropic Matern covariance function (two dimensions)
d_matern_anisotropic3D Geometrically anisotropic Matern covariance function (three dimensions)
d_matern_anisotropic3D_alt Geometrically anisotropic Matern covariance function (three dimensions)
d_matern_categorical Isotropic Matern covariance function with random effects for categories
d_matern_isotropic Isotropic Matern covariance function
d_matern_nonstat_var Isotropic Matern covariance function, nonstationary variances
d_matern_scaledim Matern covariance function, different range parameter for each dimension
d_matern_spacetime Spatial-Temporal Matern covariance function
d_matern_spacetime_categorical Space-Time Matern covariance function with random effects for categories
d_matern_spacetime_categorical_local Space-Time Matern covariance function with local random effects for categories
d_matern_sphere Isotropic Matern covariance function on sphere
d_matern_spheretime Matern covariance function on sphere x time
d_matern_spheretime_warp Deformed Matern covariance function on sphere
d_matern_sphere_warp Deformed Matern covariance function on sphere

-- E --

expit expit function and integral of expit function
exponential_anisotropic2D Geometrically anisotropic exponential covariance function (two dimensions)
exponential_anisotropic3D Geometrically anisotropic exponential covariance function (three dimensions)
exponential_anisotropic3D_alt Geometrically anisotropic exponential covariance function (three dimensions, alternate parameterization)
exponential_isotropic Isotropic exponential covariance function
exponential_nonstat_var Isotropic exponential covariance function, nonstationary variances
exponential_scaledim Exponential covariance function, different range parameter for each dimension
exponential_spacetime Spatial-Temporal exponential covariance function
exponential_sphere Isotropic exponential covariance function on sphere
exponential_spheretime Exponential covariance function on sphere x time
exponential_spheretime_warp Deformed exponential covariance function on sphere
exponential_sphere_warp Deformed exponential covariance function on sphere

-- F --

fast_Gp_sim Approximate GP simulation
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
fisher_scoring Fisher scoring algorithm
fit_model Estimate mean and covariance parameters

-- G --

get_linkfun get link function, whether locations are lonlat and space time
get_penalty get penalty function
get_start_parms get default starting values of covariance parameters
GpGp GpGp: Fast Gaussian Process Computing.
group_obs Automatic grouping (partitioning) of locations

-- I --

intexpit expit function and integral of expit function

-- J --

jason3 Windspeed measurements from Jason-3 Satellite

-- L --

Linv_mult Multiply approximate inverse Cholesky by a vector
Linv_t_mult Multiply transpose of approximate inverse Cholesky by a vector
L_mult Multiply approximate Cholesky by a vector
L_t_mult Multiply transpose of approximate Cholesky by a vector

-- M --

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
matern35_isotropic Isotropic Matern covariance function, smoothness = 3.5
matern35_scaledim Matern covariance function, smoothess = 3.5, different range parameter for each dimension
matern45_isotropic Isotropic Matern covariance function, smoothness = 4.5
matern45_scaledim Matern covariance function, smoothess = 3.5, different range parameter for each dimension
matern_anisotropic2D Geometrically anisotropic Matern covariance function (two dimensions)
matern_anisotropic3D Geometrically anisotropic Matern covariance function (three dimensions)
matern_anisotropic3D_alt Geometrically anisotropic Matern covariance function (three dimensions, alternate parameterization)
matern_categorical Isotropic Matern covariance function with random effects for categories
matern_isotropic Isotropic Matern covariance function
matern_nonstat_var Isotropic Matern covariance function, nonstationary variances
matern_scaledim Matern covariance function, different range parameter for each dimension
matern_spacetime Spatial-Temporal Matern covariance function
matern_spacetime_categorical Space-Time Matern covariance function with random effects for categories
matern_spacetime_categorical_local Space-Time Matern covariance function with local random effects for categories
matern_sphere Isotropic Matern covariance function on sphere
matern_spheretime Matern covariance function on sphere x time
matern_spheretime_warp Deformed Matern covariance function on sphere
matern_sphere_warp Deformed Matern covariance function on sphere

-- O --

order_coordinate Sorted coordinate ordering
order_dist_to_point Distance to specified point ordering
order_maxmin Maximum minimum distance ordering
order_middleout Middle-out ordering

-- P --

pen_hi penalize large values of parameter: penalty, 1st deriative, 2nd derivative
pen_lo penalize small values of parameter: penalty, 1st deriative, 2nd derivative
pen_loglo penalize small values of log parameter: penalty, 1st deriative, 2nd derivative
predictions Compute Gaussian process predictions using Vecchia's approximations

-- S --

sph_grad_xyz compute gradient of spherical harmonics functions
summary.GpGp_fit Print summary of GpGp fit

-- T --

test_likelihood_object test likelihood object for NA or Inf values

-- V --

vecchia_grouped_meanzero_loglik Grouped Vecchia approximation to the Gaussian loglikelihood, zero mean
vecchia_grouped_profbeta_loglik Grouped Vecchia approximation, profiled regression coefficients
vecchia_grouped_profbeta_loglik_grad_info Grouped Vecchia loglikelihood, gradient, Fisher information
vecchia_Linv Entries of inverse Cholesky approximation
vecchia_meanzero_loglik Vecchia's approximation to the Gaussian loglikelihood, zero mean
vecchia_profbeta_loglik Vecchia's approximation to the Gaussian loglikelihood, with profiled regression coefficients.
vecchia_profbeta_loglik_grad_info Vecchia's loglikelihood, gradient, and Fisher information