Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
Random sampling from a binomial posterior distribution
Create an external pointer to a C++ prior
Random sampling from D-gaps posterior distribution
Beta-type prior for GEV shape parameter
Flat prior for GEV parameters ()
Flat prior for GEV parameters ()
Trivariate normal prior for GEV parameters (...
Maximal data information (MDI) prior for GEV parameters ($\mu, \sigma,...
Trivariate normal prior for GEV parameters ()
Informative GEV prior on a probability scale
Informative GEV prior on a quantile scale
The Generalised Extreme Value Distribution
Beta-type prior for GP shape parameter
Flat prior for GP parameters ()
Flat prior for GP parameters ()
Jeffreys prior for GP parameters ()
Linear Combinations of Ratios of Spacings estimation of generalised Pa...
Maximal data information (MDI) prior for GP parameters ()
Bivariate normal prior for GP parameters ()
Probability-weighted moments estimation of generalised Pareto paramete...
The Generalised Pareto Distribution
Maximum likelihood estimation of generalised Pareto parameters
Random sampling from K-gaps posterior distribution
Plot diagnostics for an evpost object
Plot diagnostics for an evpred object
Posterior predictive checks for an evpost object
Predictive inference for the largest value observed in years.
Print method for objects of class "evpost"
Print method for objects of class "summary.evpost"
Converts quantiles to GEV parameters
Simulation from a Dirichlet distribution
Internal revdbayes functions
revdbayes: Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analy...
Random sampling from extreme value posterior distributions
Random sampling from extreme value posterior distributions
Prior simulation of GEV parameters - prior on probability scale
Prior simulation of GEV parameters - prior on quantile scale
Construction of a prior distribution for a binomial probability
Construction of prior distributions for extreme value model parameters
Summarizing an evpost object
Random sampling from a binomial posterior distribution, using weights
Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.
Useful links