Modelling Spatial Extremes
Anova Tables
Pairwise empirical and extremal concurrence probabilities
Maps of concurrence probabilities/expected concurrence cell area
Produces a conditional 2D map from a fitted max-stable process
Conditional simulation of Gaussian random fields
Conditional simulation of max-linear random fields
Conditional simulation of max-stable processes
Defines and computes covariance functions
Estimates the penalty coefficient from the cross-validation criterion
Deviance Information Criterion
Computes distance between pairs of locations
Plots the extremal coefficient
Non parametric estimators of the extremal coefficient function
Fit a copula-based model to spatial extremes
Estimates the covariance function for the Schlather's model
Estimates the covariance matrix for the Smith's model
Fits a max-stable process to data
MLE for a spatial GEV model
Computes the F-madogram
Estimates the penalty coefficient from the generalized cross-validatio...
The Generalized Extreme Value Distribution
Transforms GEV data to unit Frechet ones and vice versa
The Generalized Pareto Distribution
Internal functions and methods for the maxstable package.
Simple kriging interpolation
Bayesian hierarchical models for spatial extremes
Computes the lambda-madogram
Extracts Log-Likelihood
Estimates the spatial dependence parameter of a max-stable process by ...
Computes madograms
Two dimensional map from a Bayesian hierarchical model
Produces a 2D map from a fitted max-stable process
Define a model for the spatial behaviour of the GEV parameters
Model checking of a fitted copula based model.
Model checking of a fitted max-stable model
Prediction of the marginal parameters for various models
Printing objects of classes defined in the SpatialExtreme packages
Method for profiling fitted max-stable objects
Method for profiling (in 2d) fitted max-stable objects
QQ-plot for the extremal coefficient
QQ-plot for the GEV parameters
Creates a model using penalized smoothing splines
Fits a penalized spline with radial basis functions to data
Simulation from copula based models with unit Frechet margins
Gaussian Random Fields Simulation
Simulation from max-linear models
Simulation of Max-Stable Random Fields
Analysis of Spatial Extremes
Map of the Switzerland.
Detecting spatial trends graphically
Takeuchi's information criterion
Fits univariate extreme value distributions to data
Empirical variogram
Van der Corput Sequence
Annual maxima wind gusts in the Netherlands.
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>.