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")