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).
The two main functions in
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 syntax of
post_rcpp is identical. For example:
gevp_rcpp <- rpost_rcpp(n = 1000, model = "gev", prior = pn, data = portpirie)
To get the current released version from CRAN:
vignette("revdbayes-vignette", package = "revdbayes") for an overview of the package and
vignette("revdbayes-using-rcpp-vignette", package = "revdbayes") for an illustration of the improvements in efficiency produced using the Rcpp package. See
vignette("revdbayes-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 the K-gaps model is described in
vignette("revdbayes-kgaps-vignette", package = "revdbayes")