lite: Likelihood-Based Inference for Time Series Extremes

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.

Version: 1.1.1
Depends: R (≥ 3.3.0)
Imports: chandwich, exdex, graphics, revdbayes, rust, sandwich, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-07-17
DOI: 10.32614/CRAN.package.lite
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: lite results


Reference manual: lite.pdf
Vignettes: Frequentist Likelihood-Based Inference for Time Series Extremes
Bayesian Likelihood-Based Inference for Time Series Extremes


Package source: lite_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lite_1.1.1.tgz, r-oldrel (arm64): lite_1.1.1.tgz, r-release (x86_64): lite_1.1.1.tgz, r-oldrel (x86_64): lite_1.1.1.tgz
Old sources: lite archive


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