Statistical Modelling of Extreme Values

Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) , with graphical tools for threshold selection and to diagnose estimation convergence.

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Extreme value modelling with R. Includes univariate modelling using generalized Pareto, generalized extreme value, Weibull and Gumbel distributions, and the multivariate conditional approach of Heffernan and Tawn.

The package contains a test suite that depends on the testthat package. To use the test suite, install using 'R CMD INSTALL --install-tests' and then running 'devtools::test("texmex")' within R, where "texmex" points to the package location.

This work was partially funded by AstraZeneca.


Reference manual

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2.4.8 by Harry Southworth, a year ago

Browse source code at

Authors: Harry Southworth [aut, cre] , Janet E. Heffernan [aut] , Paul D. Metcalfe [aut] , Yiannis Papastathopoulos [ctb] , Alec Stephenson [ctb] , Stuart Coles [ctb]

Documentation:   PDF Manual  

Task views:

GPL (>= 2) license

Imports Rcpp

Depends on mvtnorm, ggplot2, stats

Suggests MASS, gridExtra, parallel, lattice, knitr, rmarkdown, dplyr, tidyr, testthat, devtools, survival, ismev

Linking to Rcpp

Depended on by mobirep.

Suggested by lax.

See at CRAN