{ "_id": "6a1028fcacfb0bcc41c9117f", "Package": "EE.Data", "Type": "Package", "Title": "Objects for Predicting Energy Expenditure", "Version": "0.1.1", "Authors@R": "c(\nperson(c(\"Paul\", \"R.\"), \"Hibbing\", email = \"paulhibbing@gmail.com\", role = c(\"aut\",\"cre\")),\nperson(c(\"Alexander\", \"H.K.\"), \"Montoye\", email = \"alexander.montoye@montcalm.edu\", role = \"ctb\"),\nperson(\"John\", \"Staudenmayer\", email = \"jstauden@math.umass.edu\", role = \"ctb\"),\nperson(\"Children's Mercy Kansas City\", role = \"cph\")\n)", "Description": "This is a data-only package containing model objects that\npredict human energy expenditure from wearable sensor data.\nSupported methods include the neural networks of Montoye et al.\n(2017) and the models of\nStaudenmayer et al. (2015)\n, one a linear model and\nthe other a random forest. The package is intended as a spoke\nfor the hub-package 'accelEE', which brings together the above\nmethods and others from packages such as 'Sojourn' and\n'TwoRegression.'", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "RoxygenNote": "7.3.3", "Repository": "https://paulhibbing.r-universe.dev", "Date/Publication": "2026-03-28 11:17:24 UTC", "RemoteUrl": "https://github.com/paulhibbing/ee.data", "RemoteRef": "HEAD", "RemoteSha": "b8a3e800b0e077b3468b63a4d55bb98b6767430f", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 09:56:14 UTC", "User": "root" }, "Author": "Paul R. Hibbing [aut, cre],\nAlexander H.K. Montoye [ctb],\nJohn Staudenmayer [ctb],\nChildren's Mercy Kansas City [cph]", "Maintainer": "Paul R. 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