The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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 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_1.1.1.tar.gz
Windows binaries: r-devel: lite_1.1.1.zip, r-release: lite_1.1.1.zip, r-oldrel: lite_1.1.1.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=lite to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.