{
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  "Package": "bnpa",
  "Type": "Package",
  "Title": "Bayesian Networks & Path Analysis",
  "Version": "0.3.0",
  "Author": "Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner\nMachado, Emerson P Cabrera, Julio C Nievola",
  "Maintainer": "Elias Carvalho <ecacarva@gmail.com>",
  "Description": "This project aims to enable the method of Path Analysis to\ninfer causalities from data. For this we propose a hybrid\napproach, which uses Bayesian network structure learning\nalgorithms from data to create the input file for creation of a\nPA model. The process is performed in a semi-automatic way by\nour intermediate algorithm, allowing novice researchers to\ncreate and evaluate their own PA models from a data set. The\nreferences used for this project are: Koller, D., & Friedman,\nN. (2009). Probabilistic graphical models: principles and\ntechniques. MIT press. <doi:10.1017/S0269888910000275>.\nNagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian\nnetworks in r. Springer, 122, 125-127. Scutari, M., & Denis, J.\nB. <doi:10.1007/978-1-4614-6446-4>. Scutari M (2010). Bayesian\nnetworks: with examples in R. Chapman and Hall/CRC.\n<doi:10.1201/b17065>. Rosseel, Y. (2012). lavaan: An R Package\nfor Structural Equation Modeling. Journal of Statistical\nSoftware, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.",
  "URL": "https://sites.google.com/site/bnparp/.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "LazyData": "true",
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  "Config/pak/sysreqs": "cmake libglpk-dev make default-jdk libicu-dev\nlibjpeg-dev libpng-dev libuv1-dev libxml2-dev zlib1g-dev",
  "Repository": "https://eliascarvalho.r-universe.dev",
  "Date/Publication": "2019-08-01 22:20:02 UTC",
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      "page": "check.dichotomic.one.var",
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      "topics": [
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