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:	1.1.0
Depends:	R (≥ 3.3.0)
Imports:	chandwich, exdex, graphics, revdbayes, rust, sandwich, stats
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2023-01-27
Author:	Paul J. Northrop [aut, cre, cph]
Maintainer:	Paul J. Northrop <p.northrop at ucl.ac.uk>
BugReports:	https://github.com/paulnorthrop/lite/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://paulnorthrop.github.io/lite/, https://github.com/paulnorthrop/lite
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	lite results

Documentation:

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

Downloads:

Package source:	lite_1.1.0.tar.gz
Windows binaries:	r-devel: lite_1.1.0.zip, r-release: lite_1.1.0.zip, r-oldrel: lite_1.1.0.zip
macOS binaries:	r-release (arm64): lite_1.1.0.tgz, r-oldrel (arm64): lite_1.1.0.tgz, r-release (x86_64): lite_1.1.0.tgz, r-oldrel (x86_64): lite_1.1.0.tgz
Old sources:	lite archive

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