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 compatability 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
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
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_rcpp meant that it was not possible to supply the argument
r to be passed to
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
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 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.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
rgev and the GP functions
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 = 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
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
Unnecessary dependence on package
devtools via Suggests is removed.
Bugs fixed in the (R) prior functions
gev_loglognorm. The effect of the bug was negligible unless the prior variances are not chosen to be large.
In a call to
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.
predict() method for class ‘evpost’ performs predictive inference about the largest observation observed in N years, returning an object of class
plot() for the
evpred object returned by
pp_check() method for class ‘evpost’ performs posterior predictive checks using the bayesplot package.
Interface to the bayesplot package added in the S3
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
prior = "beta" parameter vector
ab has been corrected to
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.