{ "_id": "6a116fa7acfb0bcc41cf629f", "Package": "dbnR", "Type": "Package", "Title": "Dynamic Bayesian Network Learning and Inference", "Version": "0.8.1", "Authors@R": "c(\nperson(\"David\", \"Quesada\", email = \"dkesada@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Gabriel\", \"Valverde\", email = \"gabrielvalverdecastilla@gmail.com\", role = \"ctb\"))", "Description": "Learning and inference over dynamic Bayesian networks of\narbitrary Markovian order. Extends some of the functionality\noffered by the 'bnlearn' package to learn the networks from\ndata and perform exact inference. It offers three structure\nlearning algorithms for dynamic Bayesian networks: Trabelsi G.\n(2013) , Santos F.P. and\nMaciel C.D. (2014) , Quesada D.,\nBielza C. and Larrañaga P. (2021)\n. It also offers the\npossibility to perform forecasts of arbitrary length. A tool\nfor visualizing the structure of the net is also provided via\nthe 'visNetwork' package. Further detailed information and\nexamples can be found in our Journal of Statistical Software\npaper Quesada D., Larrañaga P. and Bielza C. 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