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  "Title": "Fit Statistical Models Using Hamiltonian Monte Carlo",
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  "Authors@R": "c(person(given = \"Samuel\",\nfamily = \"Thomas\",\nrole = c(\"cre\", \"aut\"),\nemail = \"samthoma@iu.edu\"),\nperson(given = \"Wanzhu\",\nfamily = \"Tu\",\nrole = \"ctb\",\nemail = \"wtu1@iu.edu\"))",
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  "Description": "Provide users with a framework to learn the intricacies of\nthe Hamiltonian Monte Carlo algorithm with hands-on experience\nby tuning and fitting their own models.  All of the code is\nwritten in R.  Theoretical references are listed below:. Neal,\nRadford (2011) \"Handbook of Markov Chain Monte Carlo\" ISBN:\n978-1420079418, Betancourt, Michael (2017) \"A Conceptual\nIntroduction to Hamiltonian Monte Carlo\" <arXiv:1701.02434>,\nThomas, S., Tu, W. (2020) \"Learning Hamiltonian Monte Carlo in\nR\" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S.,\nDunson, D. B., Vehtari, A., & Rubin, D. B. (2013) \"Bayesian\nData Analysis\" ISBN: 978-1439840955, Agresti, Alan (2015)\n\"Foundations of Linear and Generalized Linear Models ISBN:\n978-1118730034, Pinheiro, J., Bates, D. (2006) \"Mixed-effects\nModels in S and S-Plus\" ISBN: 978-1441903174.",
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  "Repository": "https://sthomas522.r-universe.dev",
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        "mcmc_acf.hmclearn",
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