The ExtremeCI R package provides versatile algorithms to
efficently infer confidence intervals for extreme value statistics,
using extreme value theory extrapolation and the profile likelihood.
These intervals capture the uncertainty spread of extreme estimates more
realistically than most alternative methods, especially for return
levels (high quantiles) and for the shape parameter, whose uncertainties
are typically highly asymmetric (Coles, 2001). For return
levels and extreme quantiles, the profile likelihood method requires
reparametrization of the likelihood function. With nonstationary models,
this reparametrization is nontrivial and requires repetition for each
local interval, which was not possible with alternative software. This
package provides a framework to construct both stationary and
nonstationary models with novel confidence endpoint search procedures
based on binary search, which do no require a prespecified range. The
CIs can also be inferred from weighted samples (work in progress). This
package is motivated by Zeder et
al. (2023) and by Pasche et
al. (2026).
To install the development version of ExtremeCI from R, run
# install.packages("devtools")
devtools::install_github("opasche/ExtremeCI")Package created by Olivier C. PASCHE
Research Institute for Statistics and Information Science,
University of Geneva (CH), 2025.
Supported by the Swiss National Science Foundation.