### 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)`