get.LML: Log marginal likelihood of model
In braunm/bayesGDS: Scalable Rejection Sampling for Bayesian Hierarchical Models

Estimate log marginal likelihood of model

1 2	get.LML(counts, log.phi, post.mode, fn.dens.post, fn.dens.prop, prop.params, ...)

`counts`	vector of counts of the number of proposals that were generated before accepting a draw. Length of vector is equal to the number of draws from the posterior. If the first proposal for a particular posterior draw is accepted, that count is a 1.
`log.phi`	Numeric vector of draws of log.phi from the proposal draws.
`post.mode`	The posterior mode.
`fn.dens.post`	Function that returns the log posterior density. Function should take the parameter vector as the first argument. Additional arguments are passed as ...
`fn.dens.prop`	Function that returns the log density of the proposal distribution. The first argument of the function should take either a vector or a matrix. If the argument is a matrix, each row is considered a sample. Additional parameters are passed as a list, prop.params.
`prop.params`	Object (list or vector) to be passed to both fn.dens.prop and fn.draw.prop.Contains parameters for the proposal distribution. See details.
`...`	Additional parameters to be passed to fn.dens.post