https://github.com/paulnorthrop/revdbayes
Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
https://github.com/paulnorthrop/revdbayes
analysis bayesian extreme extreme-value-statistics extremes generalized-pareto-distribution gev inference nhpp point-process posterior predictive r rcpp value
Last synced: 6 months ago
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Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
- Host: GitHub
- URL: https://github.com/paulnorthrop/revdbayes
- Owner: paulnorthrop
- License: gpl-3.0
- Created: 2016-11-23T19:35:08.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-08-21T16:05:00.000Z (about 1 year ago)
- Last Synced: 2025-03-28T12:06:22.276Z (7 months ago)
- Topics: analysis, bayesian, extreme, extreme-value-statistics, extremes, generalized-pareto-distribution, gev, inference, nhpp, point-process, posterior, predictive, r, rcpp, value
- Language: R
- Homepage: https://paulnorthrop.github.io/revdbayes/
- Size: 256 MB
- Stars: 4
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# revdbayes 
[](https://ci.appveyor.com/project/paulnorthrop/revdbayes)
[](https://github.com/paulnorthrop/revdbayes/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/github/paulnorthrop/revdbayes?branch=master)
[](https://cran.r-project.org/package=revdbayes)
[](https://cran.r-project.org/package=revdbayes)
[](https://cran.r-project.org/package=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](https://cran.r-project.org/package=evdbayes), 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](https://cran.r-project.org/package=evdbayes) 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)](https://doi.org/10.1214/09-AOAS292) and the D-gaps model of
[Holesovsky and Fusek (2020)](https://doi.org/10.1007/s10687-020-00374-3).
### 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.
```{r, eval = FALSE}
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:
```{r, eval = FALSE}
gevp_rcpp <- rpost_rcpp(n = 1000, model = "gev", prior = pn, data = portpirie)
```
### Installation
To get the current released version from CRAN:
```{r installation, eval = FALSE}
install.packages("revdbayes")
```
### Vignettes
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")`