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.
- Version1.1.1
- R version≥ 3.3.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release07/17/2024
Documentation
Team
Paul J. Northrop
MaintainerShow author details
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Last 30 days
This package has been downloaded 180 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
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Last 365 days
This package has been downloaded 2,679 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 37 downloads.
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Dependencies
- Imports5 packages
- Suggests3 packages