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: 1.0
Depends: R (≥ 3.5), mcmcse (≥ 1.4-1)
Imports: mvtnorm
Published: 2020-03-05
Author: Christina Knudson [aut, cre], Dootika Vats [aut]
Maintainer: Christina Knudson <knud8583 at stthomas.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: stableGR results

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Reference manual: stableGR.pdf
Package source: stableGR_1.0.tar.gz
Windows binaries: r-devel: stableGR_1.0.zip, r-devel-gcc8: stableGR_1.0.zip, r-release: stableGR_1.0.zip, r-oldrel: stableGR_1.0.zip
OS X binaries: r-release: stableGR_1.0.tgz, r-oldrel: stableGR_1.0.tgz

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