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  "Package": "MetaCycle",
  "Type": "Package",
  "Title": "Evaluate Periodicity in Large Scale Data",
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  "Authors@R": "c(\nperson(\"Gang\", \"Wu\",, email = \"wggucas@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Ron\", \"Anafi\",, email = \"ranafi@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"John\", \"Hogenesch\",, email = \"hogenesch@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Michael\", \"Hughes\",, email = \"michael.evan.hughes@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Karl\", \"Kornacker\",, email = \"kornacker@midohio.twcbc.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Xavier\", \"Li\",, email = \"haohan.li.97@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Matthew\", \"Carlucci\",, email = \"matt.carlucci.96@gmail.com\", role = c(\"aut\", \"ctb\"))\n)",
  "Description": "There are two functions-meta2d and meta3d for detecting\nrhythmic signals from time-series datasets. For analyzing\ntime-series datasets without individual information, 'meta2d'\nis suggested, which could incorporates multiple methods from\nARSER, JTK_CYCLE and Lomb-Scargle in the detection of\ninterested rhythms. For analyzing time-series datasets with\nindividual information, 'meta3d' is suggested, which takes use\nof any one of these three methods to analyze time-series data\nindividual by individual and gives out integrated values based\non analysis result of each individual.",
  "VignetteBuilder": "knitr",
  "License": "GPL (>= 2)",
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  "Author": "Gang Wu [aut, cre], Ron Anafi [aut, ctb], John Hogenesch [aut,\nctb], Michael Hughes [aut, ctb], Karl Kornacker [aut, ctb],\nXavier Li [aut, ctb], Matthew Carlucci [aut, ctb]",
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  "RoxygenNote": "6.1.1",
  "Packaged": {
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  "Repository": "https://gangwug.r-universe.dev",
  "Date/Publication": "2026-03-13 16:45:33 UTC",
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  "_expires": "2026-10-13T07:07:56.000Z",
  "_created": "2026-06-11T07:32:17.000Z",
  "_published": "2026-07-05T07:07:58.427Z",
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