The package SpatialExtremes aims to provide tools for the analysis of spatial extremes. Currently, the package uses the max-stable processes framework for the modelling of spatial extremes.
Max-stable processes are the extension of the extreme value theory to random fields. Consequently, they are good candidate to the analysis of spatial extremes. The strategy used in this package is to fit max-stable processes to data using composite likelihood.
In the future, the package will allow for non-stationarity as well as other approaches to model spatial extremes; namely latent variable and copula based approaches.
A package vignette has been writen to help new users. It can be
viewed, from the R console, by invoking
The package provides the following main tools:
rgp, rmaxstab, rmaxlin,
rcopula: simulates gaussian, max-stable, max-linear and copula
based random fields,
condrgp, condrmaxlin: conditional simulations for
gaussian, max-linear processes,
fitspatgev: fits a spatial GEV model to data,
max-stable processes to data,
latent: draws a Markov chain from a Bayesian
hierarchical model for spatial extremes,
predict: allows predictions
for fitted max-stable processes,
condmap: plot a map for GEV
parameter as well as return levels - or conditional return levels
DIC: help users in model selection,
lmadogram: are (kind of) variograms devoted to extremes,
fitextcoeff: estimates semi-parametrically the
extcoeff: plots the evolution of the extremal
coefficient from a fitted max-stable process,
rbpspline: fits a penalized spline with radial
GEV (resp. Frechet) observation to unit Frechet (resp. GEV) ones
gpdmle: fit the GEV/GPD
distributions to data,
distance: computes the distance between each
pair of locations,
profile2d: computes the profile
the covariance/semivariogram function.
The development of the package has been financially supported by the Competence Center Environment and Sustainability (CCES) and more precisely within the EXTREMES project.
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