Add prior_posterior_overlap() for quantifying prior-to-posterior learning

Hi @jgabry — as part of my GSoC proposal work on prior vs posterior comparison plots, I've been prototyping different visualizations and one thing that kept coming up during testing was the need for a simple numeric summary of how much the posterior has moved away from the prior.

MCMCvis has something like this (MCMCtrace with PPO_out = TRUE), but bayesplot doesn't have an equivalent. I think a standalone prior_posterior_overlap() function would be useful even outside the context of the plotting module — it gives users a quick per-parameter number (0 = completely disjoint, 1 = identical) to answer "did my data actually inform this parameter?"

The implementation would be straightforward:

• Accept posterior and prior draws in the standard bayesplot formats (matrix, 3D array, draws objects)
• Support pars, regex_pars, transformations like other mcmc_* functions
• Return a data frame with Parameter and Overlap columns
• Overlap computed as the integral of min(f_prior, f_posterior) over a shared density grid

Would it make sense for me to implement this as a PR? Happy to hear your thoughts on scope or naming.

Start by reviewing the existing mcmc_* input handling and the MCMCvis prior-to-posterior overlap behavior referenced in the issue. Define how matrix, 3D array, and draws-object inputs, parameter selection, and transformations should work; done means a prior_posterior_overlap() result with Parameter and Overlap columns using the stated 0-to-1 density-overlap measure.