Extremal Dependence Models
Estimation of the angular density, angular measure and random generati...
Bootstrap Resampling and Bernstein Estimation of Extremal Dependence
Nonparametric Bootstrap Confidence Intervals
Bernstein Polynomials Based Estimation of Extremal Dependence
Univariate extended skew-normal distribution
Univariate extended skew-t distribution
Parametric and non-parametric density of Extremal Dependence
The Generalized Extreme Value Distribution
Diagnostics plots for MCMC algorithm.
Dimensions calculations for parametric extremal dependence models
Bivariate and trivariate extended skew-normal distribution
Bivariate and trivariate extended skew-t distribution
Level sets for bivariate normal, student-t and skew-normal distributio...
Univariate Extreme Quantile
Non-parametric extremal dependence estimation
Extremal dependence estimation
Fitting of a max-stable process
Fitting of the Generalized Extreme Value Distribution
Index of extremal dependence
Madogram-based estimation of the Pickands Dependence Function
Parametric and non-parametric distribution function of Extremal Depend...
Probability of falling into a failure region
Graphical summaries of non-parametric representations of extremal depe...
Graphical summaries of parametric representations of extremal dependen...
Compute return values
Parametric and semi-parametric random generator of extreme events
Random generation of max-stable processes
Definition of a multivariate simplex
Summary of MCMC algorithm.
Transformation to GEV distribution
Transformation to unit Frechet distribution
A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.