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  "Title": "Visually Learning the Graphical Structure of Bayesian Networks\nand Performing MCMC with 'Stan'",
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  "Date": "2019-05-20",
  "Authors@R": "c(person(\"Viet-Phuong\", \"La\", role = c(\"aut\", \"cre\"), email = \"lvphuong@gmail.com\"), person(\"Quan-Hoang\", \"Vuong\", role = c(\"aut\"), email = \"hoang.vuongquan@phenikaa-uni.edu.vn\"))",
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  "Description": "Provides users with its associated functions for\npedagogical purposes in visually learning Bayesian networks and\nMarkov chain Monte Carlo (MCMC) computations. It enables users\nto: a) Create and examine the (starting) graphical structure of\nBayesian networks; b) Create random Bayesian networks using a\ndataset with customized constraints; c) Generate Stan code for\nstructures of Bayesian networks for sampling the data and\nlearning parameters; d) Plot the network graphs; e) Perform\nMarkov chain Monte Carlo computations and produce graphs for\nposteriors checks. The package refers to one reference item,\nwhich describes the methods and algorithms: Vuong, Quan-Hoang\nand La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The\n'bayesvl' R package. Open Science Framework (May 18).",
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