c(
person(c("Paul", "J."), "Northrop", email = "p.northrop@ucl.ac.uk",
role = c("aut", "cre", "cph")),
person("Constantinos", "Christodoulides", role = c("aut", "cph"))
)
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) <doi:10.1007/s10687-015-0221-5> and
Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
are used to increase precision of estimation. The other type of methodology
uses a model for the distribution of threshold inter-exceedance times
(Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Two
versions of this type of approach are provided, following Suveges (2007)
<doi:10.1007/s10687-007-0034-2> and Suveges and Davison (2010)
<doi:10.1214/09-AOAS292>.
chandwich, graphics, methods, Rcpp, RcppRoll, stats
knitr, revdbayes, rmarkdown, testthat, zoo (>= 1.6.4)
Paul J. Northrop [aut, cre, cph],
Constantinos Christodoulides [aut, cph]

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) <doi:10.1007/s10687-015-0221-5> and
Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  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)
<doi:10.1111/1467-9868.00401>). Two versions of this type of approach are
provided: the iterated weight least squares approach of Suveges (2007)
<doi:10.1007/s10687-007-0034-2> and the K-gaps model of
Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. For the K-gaps model
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 censored
inter-exceedance times.  A graphical diagnostic for the threshold level and
the runs parameter K is provided.