R/exursions-package.R

In davidbolin/excursions: Excursion Sets and Contour Credibility Regions for Random Fields

#' Excursions: Excursion Sets and Contour Credibility Regions for Random Fields
#'
#' `excursions` contains functions that compute probabilistic excursion sets,
#' contour credibility regions, contour avoiding regions, contour map quality measures,
#' and simultaneous confidence bands for latent Gaussian
#' random processes and fields. A detailed manual can be found in the paper 
#' Bolin, D and Lindgren, F  (2018) 
#' *Calculating Probabilistic Excursion Sets and Related Quantities Using excursions*,
#' Journal of Statistical Software, 86(5), 1--20.
#' 
#' The main functions in the package fall into three different categories described below.
#' 
#' **Excursion sets, contour credibility regions, and contour avoiding regions**
#'
#' The main functions for computing excursion sets, contour credibility regions, and
#' contour avoiding regions are
#' \describe{
#' \item{[excursions()] }{The main function for Gaussian models.}
#' \item{[excursions.inla()] }{Interface for latent Gaussian models estimated using INLA.}
#' \item{[excursions.mc()] }{Function for analyzing models that have been
#' estimated using Monte Carlo methods.}
#' }
#' The output from the functions above provides a discrete domain estimate of the regions.
#' Based on this estimate, the function [continuous()] computes a continuous
#' domain estimate.
#'
#'  The main reference for these functions is Bolin, D. and Lindgren, F. (2015)
#'  *Excursion and contour uncertainty regions for latent Gaussian models*,
#'  JRSS-series B, vol 77, no 1, pp 85-106.
#'
#' **Contour map quality measures**
#'
#' The package provides several functions for computing contour maps and their quality
#' measures. These quality measures can be used to decide on an appropriate number of
#' contours to use for the contour map.
#'
#' The main functions for computing contour maps and the corresponding quality measures
#' are
#' \describe{
#' \item{[contourmap()] }{The main function for Gaussian models.}
#' \item{[contourmap.inla()] }{Interface for latent Gaussian models estimated
#' using INLA.}
#' \item{[contourmap.mc()] }{Function for analyzing models that have been
#' estimated using Monte Carlo methods.}
#' }
#' Other noteworthy functions relating to contourmaps are [tricontour()] and
#' [tricontourmap()], which compute contour curves for functinos defined on
#' triangulations, as well as [contourmap.colors()] which can be used to
#' compute appropriate colors for displaying contour maps.
#'
#' The main reference for these functions is Bolin, D. and Lindgren, F. (2017)
#' *Quantifying the uncertainty of contour maps*, Journal of Computational and
#' Graphical Statistics, 26:3, 513-524.
#'
#' **Simultaneous confidence bands**
#'
#' The main functions for computing simultaneous confidence bands are
#' \describe{
#' \item{[simconf()] }{Function for analyzing Gaussian models.}
#' \item{[simconf.inla()] }{Function for analyzing latent Gaussian models
#' estimated using INLA.}
#' \item{[simconf.mc()] }{Function for analyzing models estimated using Monte
#' Carlo methods.}
#' \item{[simconf.mixture()] }{Function for analyzing Gaussian mixture models.}
#' }
#'
#' The main reference for these functions is Bolin et al. (2015)
#' *Statistical prediction of global sea level
#' from global temperature*, Statistica Sinica, Vol 25, pp 351-367.
#'
#' @importFrom graphics lines
#' @importFrom methods as is
#' @importFrom stats optimize pnorm qnorm quantile rnorm uniroot
#' @import Matrix
#' @useDynLib excursions, .registration = TRUE
#' @aliases excursions-package
"_PACKAGE"