lite

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.

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