R: Analysis of Spatial Extremes

SpatialExtremes {SpatialExtremes} | R Documentation

## Analysis of Spatial Extremes

### Description

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")`.

### Details

The package provides the following main tools:

1. `rgp, rmaxstab, rmaxlin, rcopula`: simulates gaussian, max-stable, max-linear and copula based random fields,

2. `condrgp, condrmaxlin`: conditional simulations for gaussian, max-linear processes,

3. `fitspatgev`: fits a spatial GEV model to data,

4. `fitmaxstab`, `lsmaxstab`: fits max-stable processes to data,

5. `latent`: draws a Markov chain from a Bayesian hierarchical model for spatial extremes,

6. `predict`: allows predictions for fitted max-stable processes,

7. `map`, `condmap`: plot a map for GEV parameter as well as return levels - or conditional return levels

8. `anova`, `TIC`, `DIC`: help users in model selection,

9. `madogram`, `fmadogram`, `lmadogram`: are (kind of) variograms devoted to extremes,

10. `fitextcoeff`: estimates semi-parametrically the extremal coefficient,

11. `extcoeff`: plots the evolution of the extremal coefficient from a fitted max-stable process,

12. `rbpspline`: fits a penalized spline with radial basis function,

13. `gev2frech`, `frech2gev`: transform GEV (resp. Frechet) observation to unit Frechet (resp. GEV) ones

14. `gevmle`, `gpdmle`: fit the GEV/GPD distributions to data,

15. `distance`: computes the distance between each pair of locations,

16. `profile`, `profile2d`: computes the profile composite likelihood,

17. `covariance`, `variogram`: computes the covariance/semivariogram function.

### Acknowledgement

The development of the package has been financially supported by the Competence Center Environment and Sustainability (CCES) and more precisely within the EXTREMES project.

### Author(s)

Mathieu Ribatet

[Package SpatialExtremes version 2.0-5 Index]