SpatialExtremes

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
 <code class="command">vignette("SpatialExtremesGuide")</code>.

spatial

Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.

Mathieu Ribatet

Modelling Spatial Extremes

Richard Singleton

R Core team 

SpatialExtremes 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.

Acknowledgement

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
 <code class='command'>vignette("SpatialExtremesGuide")</code>.

SpatialExtremes: Analysis of Spatial Extremes

Description

Arguments

Acknowledgement

Details