Bayesian Modelling of Extremal Dependence in Time Series
Overview
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.
- fixed an error due to _R_CXX_USE_NO_REMAP_=true becoming standard.
- updated the NEWS file not conforming to the new accepted format
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 11includes a package overview page
- Examples that run
- 100%7 run in a plain check
- Exported functions with examples
- 70%
- Documented parameters
- 90%
- Return-value docs
- 90%
- Help pages citing references
- 9%
Downloads
Dependencies
Code & Tests
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.3.4Latest
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.3.32021-04-23 · diff ↗
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.3.22018-12-22 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 0.3.12017-03-23 · diff ↗
- 0.32017-03-14
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2017-03-14
- Total releases
- 5 / 9 yrs
- License
- GPL (>= 2) OSI
- Download size
- 47 KB
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