R/GpGp.R

In joeguinness/GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

#' GpGp: Fast Gaussian Process Computing.
#' 
#' 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 \code{\link{fit_model}}, 
#' and \code{\link{predictions}}.
#' \code{\link{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 
#' \itemize{
#'     \item \code{\link{matern_isotropic}}
#'     \item \code{\link{exponential_isotropic}}
#'     \item \code{\link{matern_anisotropic2D}}
#'     \item \code{\link{exponential_anisotropic2D}}
#'     \item \code{\link{matern_anisotropic3D}}
#'     \item \code{\link{exponential_anisotropic3D}}
#'     \item \code{\link{matern_anisotropic3D_alt}}
#'     \item \code{\link{matern15_isotropic}}
#'     \item \code{\link{matern25_isotropic}}
#'     \item \code{\link{matern35_isotropic}}
#'     \item \code{\link{matern45_isotropic}}
#'     \item \code{\link{matern_scaledim}}
#'     \item \code{\link{exponential_scaledim}}
#'     \item \code{\link{matern15_scaledim}}
#'     \item \code{\link{matern25_scaledim}}
#'     \item \code{\link{matern35_scaledim}}
#'     \item \code{\link{matern45_scaledim}}
#'     \item \code{\link{matern_spacetime}}
#'     \item \code{\link{exponential_spacetime}}
#'     \item \code{\link{matern_nonstat_var}}
#'     \item \code{\link{exponential_nonstat_var}}
#'     \item \code{\link{matern_sphere}}
#'     \item \code{\link{exponential_sphere}}
#'     \item \code{\link{matern_spheretime}}
#'     \item \code{\link{exponential_spheretime}}
#'     \item \code{\link{matern_sphere_warp}}
#'     \item \code{\link{exponential_sphere_warp}}
#'     \item \code{\link{matern_spheretime_warp}}
#'     \item \code{\link{exponential_spheretime_warp}}
#' }
#' 
#' If there are 
#' covariates, they can be expressed via a design matrix \code{X}, each row containing
#' the covariates corresponding to the same row in \code{locs}. 
#' 
#' For \code{\link{predictions}}, the user should specify prediction locations 
#' \code{locs_pred} and a prediction design matrix \code{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.
#'
#' @docType package
#' @aliases GpGp-package
#' @name GpGp
#' @useDynLib GpGp
#' @importFrom Rcpp sourceCpp
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