# Given MCMC samples, what are the options for estimating posterior of parameters?

Markov chain Monte Carlo (MCMC) is a class of algorithms for sampling from probability distributions. In the end, given a parametric model explaining the data, MCMC could also be used for parameter estimation, in which case I wonder what the different options for processing MCMC samples are. For monomodal distributions for instance, I can think of averaging the $n$ last samples of a run, averaging the last sample of different MCMC runs. What are the options for estimating posterior of parameters?

Are you looking for techniques, or might you be looking for packages, such as R's coda?