The revdbayes package uses the ratio-of-uniforms method to produce
random samples from the posterior distributions that occur in some
relatively simple Bayesian extreme value analyses. The functionality of
revdbayes is similar to the evdbayes
package, which uses Markov
Chain Monte Carlo (MCMC) methods for posterior simulation. Advantages of
the ratio-of-uniforms method over MCMC in this context are that the user
is not required to set tuning parameters nor to monitor convergence and
a random posterior sample is produced. Use of the Rcpp
package enables
revdbayes to be faster than evdbayes. Also provided are functions
for making inferences about the extremal index, using the K-gaps model
of Suveges and Davison (2010) and
the D-gaps model of Holesovsky and Fusek
(2020).
The two main functions in revdbayes are set_prior and rpost.
set_prior sets a prior for extreme value parameters. rpost samples
from the posterior produced by updating this prior using the likelihood
of observed data under an extreme value model. The following code sets a
prior for Generalised Extreme Value (GEV) parameters based on a
multivariate normal distribution and then simulates a random sample of
size 1000 from the posterior distribution based on a dataset of annual
maximum sea levels.
data(portpirie)
mat <- diag(c(10000, 10000, 100))
pn <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)
gevp <- rpost(n = 1000, model = "gev", prior = pn, data = portpirie)
plot(gevp)From version 1.2.0 onwards the faster function rpost_rcpp can be
used.
See the vignette “Faster simulation using revdbayes and Rcpp” for
details. The functions rpost and post_rcpp have the same syntax. For
example:
gevp_rcpp <- rpost_rcpp(n = 1000, model = "gev", prior = pn, data = portpirie)To get the current released version from CRAN:
install.packages("revdbayes")See vignette("revdbayes-a-vignette", package = "revdbayes") for an
overview of the package and
vignette("revdbayes-b-using-rcpp-vignette", package = "revdbayes") for
an illustration of the improvements in efficiency produced using the
Rcpp package. See
vignette("revdbayes-c-predictive-vignette", package = "revdbayes") for
an outline of how to use revdbayes to perform posterior predictive
extreme value inference. Inference for the extremal index using
threshold inter-exceedance times is described in
vignette("revdbayes-d-kgaps-vignette", package = "revdbayes")