{ "_id": "6a250a834b233be198387c63", "Package": "EpiInvert", "Version": "0.3.2", "Type": "Package", "Title": "Variational Techniques in Epidemiology", "Authors@R": "c(person(\"Luis\", \"Alvarez\", email = \"lalvarez@ulpgc.es\", role = c(\"aut\", \"cre\"), \ncomment = c(ORCID = \"0000-0002-6953-9587\")),\nperson(\"Jean-David\", \"Morel\", role = \"ctb\", comment = c(ORCID = \"0000-0002-7122-9924\")),\nperson(\"Jean-Michel\", \"Morel\", role = \"ctb\", comment = c(ORCID = \"0000-0002-6108-897X\"))\n)", "Maintainer": "Luis Alvarez ", "Description": "Using variational techniques we address some\nepidemiological problems as the incidence curve decomposition\nby inverting the renewal equation as described in Alvarez et\nal. (2021) and Alvarez et al.\n(2022) or the estimation of the\nfunctional relationship between epidemiological indicators. We\nalso propose a learning method for the short time forecast of\nthe trend incidence curve as described in Morel et al. (2022)\n.", "URL": "https://github.com/lalvarezmat/EpiInvert", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "Config/testthat/edition": "3", "LazyLoad": "yes", "RoxygenNote": "7.2.2", "Repository": "https://lalvarezmat.r-universe.dev", "Date/Publication": "2023-12-31 14:58:09 UTC", "RemoteUrl": "https://github.com/lalvarezmat/epiinvert", "RemoteRef": "HEAD", "RemoteSha": "7b6afdda0b55e54c77f601239340552d64d9b132", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-06 09:10:01 UTC", "User": "root" }, "Author": "Luis Alvarez [aut, cre] (ORCID:\n),\nJean-David Morel [ctb] (ORCID: ),\nJean-Michel Morel [ctb] (ORCID:\n)", "MD5sum": "688981f5ec4d42ea70c6fe02d339076e", "_user": "lalvarezmat", "_type": "src", "_file": "EpiInvert_0.3.2.tar.gz", "_fileid": "f4ac31994f696e8786e60e4f7d6b06c6f375d95bcb824a2a682b4edbb8980e08", "_filesize": 3421256, "_sha256": "f4ac31994f696e8786e60e4f7d6b06c6f375d95bcb824a2a682b4edbb8980e08", "_created": "2026-06-06T09:10:01.000Z", 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EpiInvert also corrects the festive and weekly biases present in the registered daily incidence.", "topics": [ "EpiInvert" ] }, { "page": "EpiInvert_plot", "title": "'EpiInvert_plot'", "topics": [ "EpiInvert_plot" ] }, { "page": "EpiInvertForecast", "title": "'EpiInvertForecast' computes a 28-day forecast of the restored incidence curve including a 95 using the weekly seasonality, from the forecasted restored incidence curve we also estimate a 28-day forecast of the original incidence curve.", "topics": [ "EpiInvertForecast" ] }, { "page": "EpiInvertForecast_plot", "title": "'EpiInvertForecast_plot'", "topics": [ "EpiInvertForecast_plot" ] }, { "page": "festives", "title": "A dataset containing festive days in France, Germany, UK and the USA", "topics": [ "festives" ] }, { "page": "incidence", "title": "A dataset containing daily incidence of COVID-19 for France, Germany, UK and the USA", "topics": [ "incidence" ] }, { "page": "incidence_weekly_aggregated", "title": "A dataset 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