A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo R package stableGR version 1.2 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>. Software Depends: R (>= 3.5), mcmcse(>= 1.4-1) Imports: mvtnorm Christina Knudson <drchristinaknudson@gmail.com> Comprehensive R Archive Network (CRAN) Christina Knudson [aut, cre], Dootika Vats [aut] GPL-3 2022-10-07 application/tgz https://CRAN.R-project.org/package=stableGR doi:10.32614/CRAN.package.stableGR