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>.
| Version: |
1.2 |
| Depends: |
R (≥ 3.5), mcmcse (≥ 1.4-1) |
| Imports: |
mvtnorm |
| Published: |
2022-10-07 |
| Author: |
Christina Knudson [aut, cre],
Dootika Vats [aut] |
| Maintainer: |
Christina Knudson <drchristinaknudson at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
stableGR results |
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