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>.

Package details

AuthorChristina Knudson [aut, cre], Dootika Vats [aut]
MaintainerChristina Knudson <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the stableGR package in your browser

Any scripts or data that you put into this service are public.

stableGR documentation built on March 13, 2020, 2:21 a.m.