evmissing: Extreme Value Analyses with Missing Data

Performs likelihood-based extreme value inferences with adjustment for the presence of missing values. A Generalised Extreme Value (GEV) distribution is fitted to block maxima using maximum likelihood estimation, with the GEV location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing.

The evmissing package was created to accompany Simpson, E. S. and Northrop, P. J. (2025) Accounting for missing data when modelling block maxima. doi:10.48550/arXiv.2512.15429

Details

The main functions are

• gev_mle: maximum likelihood inference for block maxima based on a GEV distribution, with S3 methods including confint.

• gev_bayes: Bayesian inference for block maxima based on a GEV distribution.

For objects returned by gev_mle, inferences about return levels are performed by gev_return, with with S3 methods including confint.

The function gev_influence quantifies the influence that individual extreme (small or large) block maxima have on the maximum likelihood estimators of GEV parameters.

The following example datasets are provided.

• BloomsburyOzoneMaxima: Annual maxima ozone levels at Bloomsbury, London, UK, 1992-2024.

• PlymouthOzoneMaxima: Annual maxima ozone levels at Plymouth, Devon, UK, 1998-2024.

• BrestSurgeMaxima: Annual maxima surge heights at Brest, France, 1846-2007.

See also

Useful links:

• https://paulnorthrop.github.io/evmissing/

• https://github.com/paulnorthrop/evmissing

• Report bugs at https://github.com/paulnorthrop/evmissing/issues

Author

Maintainer: Paul J. Northrop p.northrop@ucl.ac.uk [copyright holder]

Authors:

• Emma S. Simpson [copyright holder]