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 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, with graphical tools for threshold selection and to diagnose estimation convergence.

Extreme value modelling with R. Includes univariate modelling using generalized Pareto distributions and generalized extreme value 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 'test_package("texmex")' within R.

This work was partially funded by AstraZeneca.


Reference manual

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2.3 by Harry Southworth, 10 months ago

Browse source code at

Authors: Harry Southworth [aut, cre], Janet E. Heffernan [aut], Paul D. Metcalfe [aut]

Documentation:   PDF Manual  

Task views: Extreme Value Analysis

GPL (>= 2) license

Depends on mvtnorm, ggplot2, stats

Suggests MASS, gridExtra, parallel, lattice, knitr, testthat, devtools, GGally

See at CRAN