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  "Title": "Generalized Gelman-Rubin Diagnostic and Effective Sample Size\nfor MCMC",
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  "Date": "2026-07-26",
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  "Description": "Provides generalized univariate and multivariate\n'Gelman-Rubin' convergence diagnostics, effective sample size\n('ESS') estimates, and principled termination thresholds for\nMarkov chain Monte Carlo ('MCMC') simulations, based on Vats\nand Knudson (2021) <doi:10.1214/20-STS812>. The package\nincorporates replicated lugsail batch means variance estimators\nto construct stable convergence statistics for single and\nmultiple chains. Additionally, it offers comprehensive tools\nfor evaluating 'MCMC' output generated from user-supplied\nprobability density functions ('PDF') or log-likelihoods,\nincluding implementations for censored data models under right,\nleft, interval, 'Type-I', 'Type-II', progressive, and hybrid\ncensoring schemes.",
  "License": "GPL (>= 2)",
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  "Author": "Shikhar Tyagi [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-1606-0844>), Arvind Pandey [aut],\nBhupendra Singh [aut], Vrijesh Tripathi [aut]",
  "Maintainer": "Shikhar Tyagi <shikhar1093tyagi@gmail.com>",
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      "title": "Calculate Minimum Required Effective Sample Size (M_alpha_eps_p)",
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      "title": "Upgraded Univariate and Multivariate Gelman-Rubin Diagnostic",
      "topics": [
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