tsxtreme: Bayesian Modelling of Extremal Dependence in Time Series

Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

Version:	0.3.3
Imports:	evd, mvtnorm, stats, MASS, graphics, tictoc
Published:	2021-04-23
Author:	Thomas Lugrin
Maintainer:	Thomas Lugrin <thomas.lugrin at alumni.epfl.ch>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	tsxtreme citation info
Materials:	README NEWS
CRAN checks:	tsxtreme results

Documentation:

Reference manual:

tsxtreme.pdf

Downloads:

Package source:	tsxtreme_0.3.3.tar.gz
Windows binaries:	r-devel: tsxtreme_0.3.3.zip, r-release: tsxtreme_0.3.3.zip, r-oldrel: tsxtreme_0.3.3.zip
macOS binaries:	r-release (arm64): tsxtreme_0.3.3.tgz, r-oldrel (arm64): tsxtreme_0.3.3.tgz, r-release (x86_64): tsxtreme_0.3.3.tgz, r-oldrel (x86_64): tsxtreme_0.3.3.tgz
Old sources:	tsxtreme archive

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