https://github.com/paulnorthrop/exdex/releases/tag/v1.2.3

exdex 1.2.3

Bug fixes and minor improvements

• The issue described at RcppCore/Rcpp#1287 has been fixed to avoid WARNINGs from CRAN checks on some platforms. Thank you to Dirk Eddelbuettel for providing the fix so quickly!

• Fixed issues with the incorrect use of \itemize in some Rd files.

exdex: Estimation of the Extremal Index v1.2.2

exdex 1.2.2

Bug fixes and minor improvements

• If the argument k = 0 is supplied to kgaps() then an estimate of 1 is returned for the extremal index for any input data. For this very special case the estimated standard error associated with this estimate is set to zero and confidence intervals have a width of zero.

• Corrected a typing error in the description of uprob in the documentation for plot.choose_uk() and plot.choose_ud().

• The unnecessary C++11 specification has been dropped to avoid a CRAN Package Check NOTE.

• README.md: Used app.codecov.io as base for codecov link.

• Create the help file for the package correctly, with alias exdex-package.

exdex: Estimation of the Extremal Index v1.2.1

exdex 1.2.1

New features

• A new estimator has been implemented, based on what we will call the D-gaps model of Holesovsky, J. and Fusek, M. Estimation of the extremal index using censored distributions. Extremes 23, 197–213 (2020). doi: 10.1007/s10687-020-00374-3

Bug fixes and minor improvements

• The value returned by nobs.kgaps() was incorrect in cases where there are censored K-gaps that are equal to zero. These K-gaps should not contribute to the number of observations. This has been corrected.

• In cases where the data used in kgaps are split into separate sequences, the threshold exceedance probability is estimated using all the data rather than locally within each sequence.

• A logLik method for objects inheriting from class "kgaps" has been added.

• In the (unexported, internal) function kgaps_conf_int() the limits of the confidence intervals for the extremal index based on the K-gaps model are constrained manually to (0, 1) to avoid problems in calculating likelihood-based confidence intervals in cases where the the log-likelihood is greater than the interval cutoff when theta = 1.

• In the documentation of the argument k to kgaps() it is noted that in practice k should be no smaller than 1.

• The function kgaps() also return standard errors based on the expected information.

• In the package manual related functions have been arranged in sections for easier reading.

• Activated 3rd edition of the testthat package

exdex: Estimation of the Extremal Index v1.1.1

exdex 1.1.1

New features

• The functions kgaps(), kgaps_imt() and choose_uk() can now accept a data argument that
  • is a matrix of independent subsets of data, such as monthly or seasonal time series from different years
  • contains missing values, that is, NAs
• A new dataset cheeseboro is included, which is a matrix containing some missing values.
• In addition to kgaps(), the functions kgaps_imt() and choose_uk() now have an extra argument inc_cens, which allows contributions from censored K-gaps to be included in the log-likelihood for the extremal index.
• The default value of inc_cens in kgaps() (and in kgaps_imt() and choose_uk()) and is now inc_cens = TRUE.

Bug fixes and minor improvements

• Plot and print methods have been added for objects of class "confint_gaps" returned from confint.kgaps().
• In confint.spm() and confint.kgaps() the input confidence level is included in the output object.

exdex: Estimation of the Extremal Index

Bug fixes and minor improvements

• An overloading ambiguity has been corrected to ensure installation on Solaris.

exdex: Estimation of the Extremal Index

Estimation of the Extremal Index

What does exdex do?

The extremal index θ is a measure of the degree of local dependence in the extremes of a stationary process. The exdex package performs frequentist inference about θ using two types of methodology.

One type (Northrop, 2015) is based on a model that relates the distribution of block maxima to the marginal distribution of the data, leading to a semiparametric maxima estimator. Two versions of this type of estimator are provided, following Northrop, 2015 and Berghaus and Bücher, 2018). A slightly modified version of the latter is also provided. Estimates are produced using both disjoint and sliding block maxima, that latter providing greater precision of estimation.

The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers, 2003). Two versions of this type of approach are provided, following Süveges (2007) and
Süveges and Davison (2010).

A simple example

The following code estimates the extremal index using the semiparametric maxima estimators, for an example dataset containing a time series of sea surges measured at Newlyn, Cornwall, UK over the period 1971-1976.

library(exdex)
theta <- spm(newlyn, 20)
theta
summary(theta)

Installation

To get the current released version from CRAN:

install.packages("exdex")

Vignette

See vignette("exdex-vignette", package = "exdex") for an overview of the package.