ExtremeCI: Realistic Confidence Intervals for Non-Stationary Extreme Value
Statistics
This framework provides versatile algorithms to efficiently infer confidence intervals for
extreme value statistics, such as extreme quantiles and return levels, that are representative
of the asymmetric uncertainty spread, using extreme value theory extrapolation and the profile likelihood
(see e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>).
Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range,
can be covariate-dependent, and can be based on weighted samples.
This package is motivated by Zeder et al. (2023) <doi:10.1029/2023GL104090> and by
Pasche et al. (2026) <doi:10.1007/s10687-026-00536-9>.
| Version: |
0.2.1 |
| Imports: |
doFuture, dplyr, evd, foreach, future, ggplot2, magrittr, rlang, stats, tibble, tidyr, tidyselect |
| Published: |
2026-05-12 |
| DOI: |
10.32614/CRAN.package.ExtremeCI |
| Author: |
Olivier C. Pasche 
[aut, cre, cph] |
| Maintainer: |
Olivier C. Pasche <olivier_pasche at alumni.epfl.ch> |
| BugReports: |
https://github.com/opasche/ExtremeCI/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/opasche/ExtremeCI,
https://opasche.github.io/ExtremeCI/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
ExtremeCI results |
Documentation:
Downloads:
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