- The function
`kgaps_post()`

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,
`NA`

s.

- A new function
`dgaps_post()`

produces random samples from a posterior distribution for the extremal index based on what we 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.`dgaps_post()`

has the same functionality as`kgaps_post()`

.

The print method

`print.evpost`

avoids printing a long list by printing only the original function call.The default value of

`inc_cens`

in`kgaps_post()`

is now`inc_cens = TRUE`

.In the (extremely rare) cases where

`grimshaw_gp_mle()`

errors or returns an estimate for which the observation information is singular, a fallback function is used, which maximises the log-likelihood using`stats::optim()`

In the generalised Pareto example in the introductory vignette, it is now noted that for the Gulf of Mexico data a threshold set at the 95% threshold results in only a small number (16) of threshold excesses.

In the GP section of the introductory vignette a link is given to the binomial-GP analysis in the Posterior Predictive Extreme Value Inference vignette.

In the introductory vignette: corrected references to plots as “on the left” when in fact they were below, and corrected “random example” to “random sample”.

The microbenchmark results have been reinstated in the “Faster simulation using revdbayes” vignette.

Activated 3rd edition of the

`testthat`

package

- Tests in
`test-gp.R`

,`test-gev.R`

and`test-bingp.R`

have been modified to avoid errors in the upcoming new release of the`testthat`

package.

The functions

`grimshaw_gp_mle()`

,`gp_pwm()`

and`gp_lrs()`

are now exported, so that the rust package can access them using :: not :::.The hyperlinks to the Grimshaw (1993) paper in the documentation to

`grimshaw_gp_mle()`

and`set_prior()`

have been corrected.

- Fixed a bug in
`dgp()`

that produced an incorrect value for the log-density (`log = TRUE`

) when`shape`

is negative and very close to zero and`x = -1/shape`

.

Use

`inherits()`

to check the class of objects returned from`try()`

, rather than`class()`

.pkgdown documentation at https://paulnorthrop.github.io/revdbayes/

- The d/p/q function for the GEV and GP distributions now handle
correctly cases where the input has length 0 and/or is
`NA`

and inputs`Inf`

and`-Inf`

.

- In
`set_bin_prior()`

the user can specify their own prior for the binomial probability, by providing an R function.

In

`rpost()`

and`rpost_rcpp()`

an error is thrown if the prior and the model are not compatible. Previously a warning was given.The penultimate example in the documentation for

`set_prior()`

has been corrected by adding`model = "gp". The default`

model = “gev”` is not appropriate here because the prior is set up for the GP model.(This is an amendment to the third minor improvement in the NEWS for v1.3.3.) In

`rpost()`

and`rpost_rcpp()`

an error is thrown if the input threshold`thresh`

is lower than the smallest observation in`data`

. This is only checked when`model = "bingp"`

or`model = "pp"`

. This not checked when`model = "gp"`

because the user may legitimately supply only threshold excesses. (Many thanks to Leo Belzile for spotting this.)

LF line endings used in inst/include/revdbayes.h and inst/include/revdbayes_RcppExports.h to avoid CRAN NOTE.

The format of the

`data`

supplied to`rpost()`

and`rpost_rcpp()`

is checked and an error is thrown if it is not appropriate.In

`rpost()`

and`rpost_rcpp()`

an error is thrown if the input threshold`thresh`

is lower than the smallest observation in`data`

. This is only relevant when`model = "gp"`

,`model = "bingp"`

or`model = "pp"`

.The summary method for class “evpost” is now set up according to Section 8.1 of the R FAQ at (https://cran.r-project.org/doc/FAQ/R-FAQ.html).

A bug in

`grimshaw_gp_mle`

has been fixed, so that now solutions with K greater than 1 are discarded. (Many thanks to Leo Belzile.)In

`grimshaw_gp_mle`

using the starting value equal to the upper bound can result in early termination of the Newton-Raphson search. A starting value away from the upper bound is now used (lines 282 and 519 of frequentist.R). (Many thanks to Jeremy Rohmer for sending me a dataset that triggered this problem.)In

`set_prior()`

if`prior = "norm"`

or`prior = "loglognorm"`

then an explicit error is thrown if`cov`

is not supplied. (Many thanks to Leo Belzile.)The mathematics in the reference manual has been tidied.

The arguments to

`d/p/q/rgev`

and`d/p/q/rgp`

now obey the usual conventions for R’s dpqr probability distribution functions.In

`pp_check.evpost`

the argument`subtype`

is now documented properly.The

`conf`

argument to`kgaps_mle`

didn’t work properly:`conf = 95`

was always used. This has been corrected.

Bayesian and maximum likelihood inference for the K-gaps model for inferring the extremal index using threshold inter-exceedances times. [Suveges, M. and Davison, A. C. (2010), Model misspecification in peaks over threshold analysis, The Annals of Applied Statistics, 4(1), 203-221. doi:10.1214/09-AOAS292.]

New vignette: “Inference for the extremal index using the K-gaps model”.

Added the attribute

`attr(gom, "npy")`

(with value 3) to the`gom`

dataset. This is for compatibility with the**threshr**package.Give an explicit error message if

`plot.evpost`

is called with the logically incompatible arguments`add_pu = TRUE`

and`pu_only = TRUE`

.The documentation for

`set_bin_prior`

has been corrected: only in-built priors are available, i.e. it is not possible for the user to supply their own prior.

In some extreme cases (datasets with very small numbers of threshold excesses) calling

`predict.evpost`

with`type = "q"`

and`x`

close to 1 returns an imprecise value for the requested predictive quantiles. This has been corrected by using`stats::uniroot`

rather than`stats::nlminb`

.A bug (missing

`drop = FALSE`

in subsetting a matrix) in`plot.evpred`

produced an error message if`n_years`

was scalar in the prior call to`predict.evpost`

. This bug has been corrected.The placing of … in the function definitions of

`rpost`

and`rpost_rcpp`

meant that it was not possible to supply the argument`r`

to be passed to`rust::ru`

or`rust::ru_rcpp`

to change the ratio-of-uniforms tuning parameter`r`

. Furthermore, if`model = "os"`

then trying to do this sets`ros`

in error. This has been corrected.A bug meant that the values returned by

`predict(evpost_object, type = "d")`

being incorrect if`evpost_object`

was returned from a call to`rpost`

using`model = bingp`

. The values returned were too small: they differ from the correct values by a factor approximately equal to the proportion of observations that lie above the threshold. This bug has been corrected.

Faster computation, owing to the use of packages Rcpp and RcppArmadillo in package rust (https://CRAN.R-project.org/package=rust).

New function:

`rpost_rcpp`

.New vignette. “Faster simulation using revdbayes”.

`set_prior`

has been extended so that informative priors for GEV parameters can be specified using the arguments`prior = "prob"`

or`prior = "quant"`

. It is no longer necessary to use the functions`prior.prob`

and`prior.quant`

from the evdbayes package to set these priors.

The list returned from

`set_prior`

now contains default values for all the required arguments of a given in-built prior, if these haven’t been specified by the user. This simplifies the evaluation of prior densities using C++.The GEV functions

`dgev`

,`pgev`

,`qgev`

,`rgev`

and the GP functions`dgp`

,`pgp`

,`qgp`

,`rgp`

have been rewritten to conform with the vectorised style of the standard functions for distributions, e.g. those found at`?Normal`

. This makes these functions more flexible, but also means that the user take care when calling them with vectors arguments or different lengths.The documentation for

`rpost`

has been corrected: previously it stated that the default for`use_noy`

is`use_noy = FALSE`

, when in fact it is`use_noy = TRUE`

.Bug fixed in

`plot.evpost`

: previously, in the`d = 2`

case, providing the graphical parameter`col`

produced an error because`col = 8`

was hard-coded in a call to`points`

. Now the extra argument`points_par`

enables the user to provide a list of arguments to`points`

.All the (R, not C++) prior functions described in the documentation of

`set_prior`

are now exported. This means that they can now be used in the function`posterior`

in the`evdbayes`

package.Unnecessary dependence on package

`devtools`

via Suggests is removed.Bugs fixed in the (R) prior functions

`gp_norm`

,`gev_norm`

and`gev_loglognorm`

. The effect of the bug was negligible unless the prior variances are not chosen to be large.In a call to

`rpost`

or`rpost_rcpp`

with`model = "os"`

the user may provide`data`

in the form of a vector of block maxima. In this instance the output is equivalent to a call to these functions with`model = "gev"`

with the same data.

A new vignette (Posterior Predictive Extreme Value Inference using the revdbayes Package) provides an overview of most of the new features. Run browseVignettes(“revdbayes”) to access.

S3

`predict()`

method for class ‘evpost’ performs predictive inference about the largest observation observed in N years, returning an object of class`evpred`

.S3

`plot()`

for the`evpred`

object returned by`predict.evpost`

.S3

`pp_check()`

method for class ‘evpost’ performs posterior predictive checks using the bayesplot package.Interface to the bayesplot package added in the S3

`plot.evpost`

method.`model = bingp`

can now be supplied to`rpost()`

to add inferences about the probability of threshold exceedance to inferences about threshold excesses based on the Generalised Pareto (GP) model.`set_bin_prior()`

can be used to set a prior for this probability.`rprior_quant()`

: to simulate from the prior distribution for GEV parameters proposed in Coles and Tawn (1996) [A Bayesian analysis of extreme rainfall data. Appl. Statist., 45, 463-478], based on independent gamma priors for differences between quantiles.`prior_prob()`

: to simulate from the prior distribution for GEV parameters based on Crowder (1992), in which independent beta priors are specified for ratios of probabilities (which is equivalent to a Dirichlet prior on differences between these probabilities).

The spurious warning messages relating to checking that the model argument to

`rpost()`

is consistent with the prior set using`set-prior()`

have been corrected. These occurred when`model = "pp"`

or`model = "os"`

.The hyperparameter in the MDI prior was

`a`

in the documentation and`a_mdi`

in the code. Now it is`a`

everywhere.In

`set_prior`

with`prior = "beta"`

parameter vector`ab`

has been corrected to`pq`

.In the documentation of

`rpost()`

the description of the argument`noy`

has been corrected.Package spatstat removed from the Imports field in description to avoid NOTE in CRAN checks.