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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.
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.
install.packages("rjmcmc")
library(rjmcmc)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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