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ExtremalDep (version 0.0.4-4)

Extremal Dependence Models

Description

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) , Marcon et al. (2016) , Marcon et al. (2017) , Marcon et al. (2017) and Beranger et al. (2021) . 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) .

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Install

install.packages('ExtremalDep')

Monthly Downloads

301

Version

0.0.4-4

License

GPL (>= 2)

Maintainer

Simone Padoan

Last Published

December 18th, 2024

Functions in ExtremalDep (0.0.4-4)

Index of extremal dependence

Univariate Extreme Quantile

Bernstein Polynomials Based Estimation of Extremal Dependence

Level sets for bivariate normal, student-t and skew-normal distributions probability densities.

Pollution data for summer and winter months in Milan, Italy.

Bivariate and trivariate extended skew-normal distribution

Bivariate and trivariate extended skew-t distribution

Monthly maxima of log-return exchange rates of the Pound Sterling (GBP) against the US dollar (USD) and the Japanese yen (JPY), between March 1991 and December 2014.

Non-parametric extremal dependence estimation

Parametric and semi-parametric random generator of extreme events

Extremal dependence estimation

Fitting of a max-stable process

Fitting of the Generalized Extreme Value Distribution

Summer temperature maxima in Melbourne, Australia between 1961 and 2010.

Clustering of maxima

Weekly maximum wind speed data collected over 4 stations across Oklahoma, USA, over the March-May preiod between 1996 and 2012.

Hourly wind gust, wind speed and air pressure at Lingen (GER), Ossendorf (GER) and Parcay-Meslay (FRA).

Random generation of max-stable processes

Diagnostics plots for MCMC algorithm.

Dimensions calculations for parametric extremal dependence models

Bootstrap Resampling and Bernstein Estimation of Extremal Dependence

Weekly maxima of hourly rainfall in France

Probability of falling into a failure region

Univariate extended skew-normal distribution

Univariate extended skew-t distribution

Nonparametric Bootstrap Confidence Intervals

Graphical summaries of parametric representations of extremal dependence.

Summary of MCMC algorithm.

Parametric and non-parametric density of Extremal Dependence

Transformation to GEV distribution

Transformation to unit Frechet distribution

Compute return values

The Generalized Extreme Value Distribution

Madogram-based estimation of the Pickands Dependence Function

Parametric and non-parametric distribution function of Extremal Dependence

Definition of a multivariate simplex

Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998.

Graphical summaries of non-parametric representations of extremal dependence.

Estimation of the angular density, angular measure and random generation from the angular distribution.