A while back I was running a bunch of JAGS models through R, using the rjags (written by Martyn Plummer) and R2jags (by Yu-Sung Su) packages. These packages provide a great interface to the JAGS software, which allows analysis of Bayesian models (written in the BUGS language) through Markov chain Monte Carlo simulation.
Running a JAGS model using these tools returns an rjags object, which when printed to the screen, summarises the posterior distribution of each monitored node, giving its mean and standard deviation, a range of quantiles, and its Gelman-Rubin convergence diagnostic statistic (Rhat), which indicates the ratio of variance within chains to that among chains. The summary is great, but when monitoring a large number of nodes, printing these to the screen can cause R to hang, and can exceed the screen buffer (not to mention making it painful to find the nodes you’re immediately interested in).
To help deal with this I wrote a couple of simple R functions:
jagsresults:return a matrix containing summary results for just the nodes you are interested in (using regular expression pattern-matching, if desired).
rhats: sort the output by the nodes’ Rhat values, making it easy to show the n least converged nodes.