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exdex  

Estimation of the Extremal Index
View on CRAN: Click here


Download and install exdex package within the R console
Install from CRAN:
install.packages("exdex")

Install from Github:
library("remotes")
install_github("cran/exdex")

Install by package version:
library("remotes")
install_version("exdex", "1.2.3")



Attach the package and use:
library("exdex")
Maintained by
Paul J. Northrop
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-03
Latest Update: 2022-04-16
Description:
Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.
How to cite:
Paul J. Northrop (2019). exdex: Estimation of the Extremal Index. R package version 1.2.3, https://cran.r-project.org/web/packages/exdex
Previous versions and publish date:
1.0.0 (2019-08-03 08:40), 1.0.1 (2019-08-06 16:20), 1.1.1 (2022-03-25 17:50), 1.2.1 (2022-04-16 09:32), 1.2.2 (2023-09-10 11:30)
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