NEWS.md
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.
predict.evpost(object, ...), if object$model = "bingp" and object$sim_vals has a third column named "theta" containing a posterior sample for the extremal index, then predictive inferences incorporate this posterior sample. This feature is introduced to facilitate the predict.blite() function in the upcoming version 1.1.0 of the lite package.Dependence on the previously suggested package evdbayes has been removed because evdbayes has been archived on CRAN.
WARNINGs in the CRAN package check results, like “init.c:120:52: warning: a function declaration without a prototype is deprecated in all versions of C [-Wstrict-prototypes] extern SEXP _revdbayes_RcppExport_registerCCallable();” have been avoided.
kgaps_post() can now accept a data argument that
NAs.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
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.
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/
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 defaultmodel = “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.