SpatialExtremes {SpatialExtremes} | R Documentation |
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
vignette("SpatialExtremesGuide")
.
The package provides the following main tools:
fitspatgev
: fits a spatial GEV model to data,
fitmaxstab
: fits max-stable processes to data,
predict
: allows predictions
for fitted max-stable processes,
map
, condmap
: plot a map for GEV
parameter as well as return levels - or conditional return levels
anova
, TIC
: help
users in model selection,
madogram
: are (kind of) variograms devoted to
extremes,
fitextcoeff
: estimates semi-parametrically the
extremal coefficient,
extcoeff
: plots the evolution of the extremal
coefficient from a fitted max-stable process,
rbpspline
: fits a penalized spline with radial
basis function,
gev2frech
, frech2gev
: transform
GEV (resp. Frechet) observation to unit Frechet (resp. GEV) ones
gevmle
, gpdmle
: fit the GEV/GPD
distributions to data,
distance
: computes the distance between each
pair of locations,
profile
,
profile2d
: computes the profile
composite likelihood,
covariance
: computes the covariance 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 (http://www.cces.ethz.ch/projects/hazri/EXTREMES).
Mathieu Ribatet