{ "_id": "6ab8e39999f7aa8c6aa381c8", "Package": "SAutomata", "Type": "Package", "Title": "Inference and Learning in Stochastic Automata", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Muhammad Kashif\", \"Hanif\", role = c(\"cre\",\"aut\"),\nemail = \"mkashifhanif@gcuf.edu.pk\"),\nperson(\"Muhammad Umer\", \"Sarwar\", role = \"aut\", email = \"Mumersarwar@gcuf.edu.pk\"),\nperson(\"Rehman\", \"Ahmad\", role = \"aut\", email = \"rehman.ahmad777@gmail.com\"),\nperson(\"Zeeshan\", \"Ahmad\", role = \"aut\", email = \"zeeshan.cs822@gmail.com \"),\nperson(\"Karl-Heinz\", \"Zimmermann\", role = \"aut\", email = \"k.zimmermann@tuhh.de\")\n)", "Maintainer": "Muhammad Kashif Hanif ", "Description": "Machine learning provides algorithms that can learn from\ndata and make inferences or predictions. Stochastic automata is\na class of input/output devices which can model components.\nThis work provides implementation an inference algorithm for\nstochastic automata which is similar to the Viterbi algorithm.\nMoreover, we specify a learning algorithm using the\nexpectation-maximization technique and provide a more efficient\nimplementation of the Baum-Welch algorithm for stochastic\nautomata. This work is based on Inference and learning in\nstochastic automata was by Karl-Heinz Zimmermann(2017)\n.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "6.0.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-27 09:34:07 UTC", "User": "root" }, "Author": "Muhammad Kashif Hanif [cre, aut], Muhammad Umer Sarwar [aut],\nRehman Ahmad [aut], Zeeshan Ahmad [aut], Karl-Heinz Zimmermann\n[aut]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2018-11-02 17:00:03 UTC", "RemoteUrl": "https://github.com/cran/SAutomata", "RemoteRef": "HEAD", "RemoteSha": "a3a3572abe1b35c97b2249493cc9700bd60e45d8", "_user": "cran", "_type": "src", "_file": "SAutomata_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/20598dc9bf0a108fa56524e52171a508c3fd3ab8f61e902236391c7332f405cc", "_filesize": 101842, "_sha256": "20598dc9bf0a108fa56524e52171a508c3fd3ab8f61e902236391c7332f405cc", "_expires": "2027-01-05T09:36:24.000Z", "_created": "2026-09-27T09:34:07.000Z", 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