Package: stableGR 1.2

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>.

Authors:Christina Knudson [aut, cre], Dootika Vats [aut]

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stableGR.pdf |stableGR.html✨
stableGR/json (API)

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5 exports 0.36 score 37 dependencies 1 dependents 45 scripts 268 downloads

Last updated 2 years agofrom:6972621044. Checks:OK: 7. Indexed: yes.

Exports:asym.varmvn.gibbsn.effstable.GRtarget.psrf

Dependencies:briocallrclicrayondescdiffobjdigestellipseevaluatefansifftwtoolsfsgluejsonlitelifecyclemagrittrmcmcsemvtnormpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RcppRcppArmadillorematch2rlangrprojroottestthattibbleutf8vctrswaldowithr