ExtRemes 2.0: An extreme value analysis package in R

By Eric Gilleland, R. Katz. 2016

587 citations recorded by the source corpus.

This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with a focus on weather and climate applications, including the incorporation of covariates, as well as some functionality for assessing bivariate tail dependence.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/998dbccd37d41c506f9c870d80a1e6043c7d1390

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Luc Feyen
2. L. Alfieri
3. S. E. Debele
4. E. Bogdanowicz
5. Lorenzo Mentaschi
6. W. Strupczewski

In Global and Planetary Change

1. S. E. Debele
2. E. Bogdanowicz
3. Giovanni Besio
4. Luc Feyen
5. L. Alfieri
6. Michalis I. Vousdoukas
7. L. Sartini
8. Lorenzo Mentaschi

Citations in the corpus

• The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis (2016, methodology): “, 2016a) and EM (e.g., Cheng et al., 2014; Gilleland and Katz, 2016; Izaguirre et al., 2011; Méndez et al., 2006; Menéndez et al., 2009; Mudersbach and Jensen, 2010; Russo et al., 2014; Sartini et al., 2015), both in terms of accuracy of the results and its conceptual and implementation simplicity.”
• A comparison of three approaches to non-stationary flood frequency analysis (2017, methodology): “Estimation of parameters was done with the help of the R software packages available on https://www.r-project. org/: extRemes (Gilleland and Katz 2016), FAdist (Aucoin 2015), PearsonDS (Becker and Klößner 2013).”