{ "_id": "6ac4cfeff511ae58c09a1b4c", "Package": "hmclearn", "Type": "Package", "Title": "Fit Statistical Models Using Hamiltonian Monte Carlo", "Version": "0.0.5", "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\"))", "Maintainer": "Samuel Thomas ", "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\" ,\nThomas, S., Tu, W. (2020) \"Learning Hamiltonian Monte Carlo in\nR\" , 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. 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