Likelihood-Based Inference for Time Series Extremes
Overview
Performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) doi:10.1002/env.2133. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) doi:10.1093/biomet/asm015, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) doi:10.1214/09-AOAS292. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.
Bug fixes and minor improvements
- Fixed 2 \link{} targets with missing (exdex) package anchors in Rd files for
blite()andflite()
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 13 (1 internal)includes a package overview page
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 84%
- Return-value docs
- 100%
- Help pages citing references
- 31%
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.1.1Latest
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.1.0
- RR 4.2.0 released · 2022-04-22
- 1.0.02022-04-08
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2022-04-08
- Total releases
- 3 / 4 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.3.0
- Download size
- 120 KB
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