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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2 |
rolling linux/jammy R-4.5 | stableGR_1.2.tar.gz |
59.1 KiB |
1.2 |
rolling linux/noble R-4.5 | stableGR_1.2.tar.gz |
59.0 KiB |
1.2 |
rolling source/ R- | stableGR_1.2.tar.gz |
29.6 KiB |
1.2 |
latest linux/jammy R-4.5 | stableGR_1.2.tar.gz |
59.1 KiB |
1.2 |
latest linux/noble R-4.5 | stableGR_1.2.tar.gz |
59.0 KiB |
1.2 |
latest source/ R- | stableGR_1.2.tar.gz |
29.6 KiB |
1.2 |
2026-04-26 source/ R- | stableGR_1.2.tar.gz |
29.6 KiB |
1.2 |
2026-04-23 source/ R- | stableGR_1.2.tar.gz |
29.6 KiB |
1.2 |
2026-04-09 windows/windows R-4.5 | stableGR_1.2.zip |
62.0 KiB |
1.2 |
2025-04-20 source/ R- | stableGR_1.2.tar.gz |
29.6 KiB |