Fitting the Metastatistical Extreme Value Distribution MEVD

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, ) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, ) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, ). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

mevr

R-functions for Fitting the Metastatistical Extreme Value Distribution MEVD.

The MEVD assumes daily rainfall extremes being block maxima over a finite and stochastically variable number of “ordinary events” which are defined as samples from the underlying distribution (Marani & Ignaccolo, 2015, Zorzetto et al., 2016).

The functions in this package can be used to fit the MEVD, its simplified sibling SMEV (Schellander et al., 2019, Marra et al., 2019) and the explicitly non-stationary approach TMEV (Falkensteiner et al., 2023) to data series.

The R-package mevr was written during the development of the TMEV (Falkensteiner et al., 2023). See also this GitHub repository which contains the original code.

Installation

The easiest way to get mevr is to install it from CRAN

install.packages("mevr")

Development version

To install the development version from GitHub

# install.packages("pak")
pak::pak("haraldschellander/mevr")