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  "Title": "A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo",
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  "Date": "2022-10-7",
  "Authors@R": "c(person(\"Christina\", \"Knudson\", role = c(\"aut\", \"cre\"),\nemail = \"drchristinaknudson@gmail.com\"), person(\"Dootika\", \"Vats\", role = c(\"aut\"), email = \"dootika.vats@gmail.com\"))",
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  "Description": "Practitioners of Bayesian statistics often use Markov\nchain Monte Carlo (MCMC) samplers to sample from a posterior\ndistribution. This package determines whether the MCMC sample\nis large enough to yield reliable estimates of the target\ndistribution. In particular, this calculates a Gelman-Rubin\nconvergence diagnostic using stable and consistent estimators\nof Monte Carlo variance. Additionally, this uses the connection\nbetween an MCMC sample's effective sample size and the\nGelman-Rubin diagnostic to produce a threshold for terminating\nMCMC simulation. Finally, this informs the user whether enough\nsamples have been collected and (if necessary) estimates the\nnumber of samples needed for a desired level of accuracy. The\ntheory underlying these methods can be found in \"Revisiting the\nGelman-Rubin Diagnostic\" by Vats and Knudson (2018)\n<arXiv:1812:09384>.",
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