stableGR - A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo
Practitioners of Bayesian statistics often use Markov
chain Monte Carlo (MCMC) samplers to sample from a posterior
distribution. This package determines whether the MCMC sample
is large enough to yield reliable estimates of the target
distribution. In particular, this calculates a Gelman-Rubin
convergence diagnostic using stable and consistent estimators
of Monte Carlo variance. Additionally, this uses the connection
between an MCMC sample's effective sample size and the
Gelman-Rubin diagnostic to produce a threshold for terminating
MCMC simulation. Finally, this informs the user whether enough
samples have been collected and (if necessary) estimates the
number of samples needed for a desired level of accuracy. The
theory underlying these methods can be found in "Revisiting the
Gelman-Rubin Diagnostic" by Vats and Knudson (2018)
<arXiv:1812:09384>.
