### Purpose

Performs reversible-jump MCMC, a Bayesian multimodel inference method. The process is simpler than a manual implementation; for instance, all Jacobian matrices are automatically calculated using the madness package. The effort required to find Bayes factors and posterior model probabilities is reduced.

### Usage

For each model considered, the user requires a posterior distribution obtained via MCMC or the like. They then define a bijection between its parameter space and the universal parameter space; the likelihood model on the data; and the priors on the parameters. The `rjmcmcpost`

function uses a post-processing algorithm to estimate posterior model probabilities. See `?rjmcmcpost`

for a simple example using binomial likelihoods.

### Installation

`install.packages("rjmcmc")`

`library(rjmcmc)`