Fast Gaussian Process Computation Using Vecchia's Approximation
Compute Gaussian process predictions using Vecchia's approximations
compute gradient of spherical harmonics functions
Print summary of GpGp fit
test likelihood object for NA or Inf values
Multiply approximate Cholesky by a vector
Isotropic Matern covariance function, smoothness = 4.5
Conditional Simulation using Vecchia's approximation
compute condition number of matrix
expit function and integral of expit function
Geometrically anisotropic exponential covariance function (two dimensi...
Geometrically anisotropic exponential covariance function (three dimen...
Geometrically anisotropic exponential covariance function (three dimen...
Isotropic exponential covariance function
Isotropic exponential covariance function, nonstationary variances
Exponential covariance function, different range parameter for each di...
Spatial-Temporal exponential covariance function
Deformed exponential covariance function on sphere
Approximate GP simulation
Isotropic exponential covariance function on sphere
Deformed exponential covariance function on sphere
Exponential covariance function on sphere x time
Approximate GP simulation with specified Linverse
Naive brute force nearest neighbor finder
Find ordered nearest neighbors.
Fisher scoring algorithm
Estimate mean and covariance parameters
get link function, whether locations are lonlat and space time
get penalty function
get default starting values of covariance parameters
GpGp: Fast Gaussian Process Computing.
Automatic grouping (partitioning) of locations
Multiply transpose of approximate Cholesky by a vector
Multiply approximate inverse Cholesky by a vector
Multiply transpose of approximate inverse Cholesky by a vector
Geometrically anisotropic Matern covariance function (two dimensions)
Geometrically anisotropic Matern covariance function (three dimensions...
Geometrically anisotropic Matern covariance function (three dimensions...
Isotropic Matern covariance function with random effects for categorie...
Isotropic Matern covariance function
Isotropic Matern covariance function, nonstationary variances
Isotropic Matern covariance function, smoothness = 1.5
Matern covariance function, different range parameter for each dimensi...
Space-Time Matern covariance function with local random effects for ca...
Space-Time Matern covariance function with random effects for categori...
Spatial-Temporal Matern covariance function
Deformed Matern covariance function on sphere
Isotropic Matern covariance function on sphere
Deformed Matern covariance function on sphere
Matern covariance function on sphere x time
Matern covariance function, smoothess = 1.5, different range parameter...
Isotropic Matern covariance function, smoothness = 2.5
Matern covariance function, smoothess = 2.5, different range parameter...
Isotropic Matern covariance function, smoothness = 3.5
Matern covariance function, smoothess = 3.5, different range parameter...
Matern covariance function, smoothess = 3.5, different range parameter...
Sorted coordinate ordering
Distance to specified point ordering
Maximum minimum distance ordering
Middle-out ordering
penalize large values of parameter: penalty, 1st deriative, 2nd deriva...
penalize small values of parameter: penalty, 1st deriative, 2nd deriva...
penalize small values of log parameter: penalty, 1st deriative, 2nd de...
Grouped Vecchia approximation to the Gaussian loglikelihood, zero mean
Grouped Vecchia loglikelihood, gradient, Fisher information
Grouped Vecchia approximation, profiled regression coefficients
Entries of inverse Cholesky approximation
Vecchia's approximation to the Gaussian loglikelihood, zero mean
Vecchia's loglikelihood, gradient, and Fisher information
Vecchia's approximation to the Gaussian loglikelihood, with profiled r...
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) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.