Changelog • exdex

exdex 1.2.4

CRAN release: 2026-01-11

Implements the patch described in Rcpp Issue #1406 to avoid masking of Rf_error().

CRAN release: 2023-12-02

The issue described at https://github.com/RcppCore/Rcpp/issues/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 in some Rd files.

CRAN release: 2023-09-10

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.

CRAN release: 2022-04-16

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

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

CRAN release: 2022-03-25

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()) is now inc_cens = TRUE.

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.

CRAN release: 2019-08-06