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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
DOI: 10.32614/CRAN.package.stableGR
Author: Christina Knudson [aut, cre], Dootika Vats [aut]
Maintainer: Christina Knudson <drchristinaknudson at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: stableGR results

Documentation:

Reference manual: stableGR.pdf

Downloads:

Package source: stableGR_1.2.tar.gz
Windows binaries: r-devel: stableGR_1.2.zip, r-release: stableGR_1.2.zip, r-oldrel: stableGR_1.2.zip
macOS binaries: r-release (arm64): stableGR_1.2.tgz, r-oldrel (arm64): stableGR_1.2.tgz, r-release (x86_64): stableGR_1.2.tgz, r-oldrel (x86_64): stableGR_1.2.tgz
Old sources: stableGR archive

Reverse dependencies:

Reverse imports: qbld

Linking:

Please use the canonical form https://CRAN.R-project.org/package=stableGR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.