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  "Title": "Variable-Lag Time Series Causality Inference Framework",
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  "Authors@R": "person(given = \"Chainarong\",\nfamily = \"Amornbunchornvej\",\nrole = c(\"aut\", \"cre\"),\nemail = \"grandca@gmail.com\",\ncomment =c(ORCID = \"0000-0003-3131-0370\"))",
  "Description": "A framework to infer causality on a pair of time series of\nreal numbers based on variable-lag Granger causality and\ntransfer entropy. Typically, Granger causality and transfer\nentropy have an assumption of a fixed and constant time delay\nbetween the cause and effect. However, for a non-stationary\ntime series, this assumption is not true. For example,\nconsidering two time series of velocity of person A and person\nB where B follows A. At some time, B stops tying his shoes,\nthen running to catch up A. The fixed-lag assumption is not\ntrue in this case. We propose a framework that allows\nvariable-lags between cause and effect in Granger causality and\ntransfer entropy to allow them to deal with variable-lag\nnon-stationary time series. Please see Chainarong\nAmornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021)\n<doi:10.1145/3441452> when referring to this package in\npublications.",
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  "URL": "https://github.com/DarkEyes/VLTimeSeriesCausality",
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  "Date/Publication": "2024-06-04 02:58:54 UTC",
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      "title": "GrangerFunc",
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      "page": "MultipleSimulationVLtimeseries",
      "title": "MultipleSimulationVLtimeseries",
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      "page": "multipleVLGrangerFunc",
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      "title": "VLTransferEntropy",
      "topics": [
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