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tsxtreme  

Bayesian Modelling of Extremal Dependence in Time Series
View on CRAN: Click here


Download and install tsxtreme package within the R console
Install from CRAN:
install.packages("tsxtreme")

Install from Github:
library("remotes")
install_github("cran/tsxtreme")

Install by package version:
library("remotes")
install_version("tsxtreme", "0.3.3")



Attach the package and use:
library("tsxtreme")
Maintained by
Thomas Lugrin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-03-23
Latest Update: 2021-04-23
Description:
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.
How to cite:
Thomas Lugrin (2017). tsxtreme: Bayesian Modelling of Extremal Dependence in Time Series. R package version 0.3.3, https://cran.r-project.org/web/packages/tsxtreme
Previous versions and publish date:
0.3.1 (2017-03-23 14:02), 0.3.2 (2018-12-22 01:40), 0.3 (2017-03-14 10:26)
Other packages that cited tsxtreme R package
View tsxtreme citation profile
Other R packages that tsxtreme depends, imports, suggests or enhances
Functions, R codes and Examples using the tsxtreme R package
Some associated functions: bayesfit . bayesparams . densities . dep2fit . depfit . depmeasure . empfit . et2fit . etfit . stepfit . tsxtreme-package . 
Some associated R codes: bounds.R . classes.R . densities.R . empfit.R . et2fit.R . etfit.R . gpdfit.R . ht2distr.R . ht2fit.R . htdistr.R . htfit.R . margtransf.R . tictoc.R .  Full tsxtreme package functions and examples
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