revdbayes: Ratio-of-uniforms Sampling for Bayesian Extreme Value Analysis

What does revdbayes do?

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.

A simple example

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.

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)


To get the current released version from CRAN:



See vignette("revdbayes-vignette", package = "revdbayes") for an overview of the package.