{ "_id": "6aca72ddfe5cc25786e895ee", "Package": "tsgc", "Title": "Time Series Methods Based on Growth Curves", "Version": "2.0.0", "Authors@R": "c(person(given = \"Michael\", family = \"Ashby\", role = c(\"aut\",\"cre\"), email = \"mwa22@cam.ac.uk\"),\nperson(given = \"Paul\", family = \"Kattuman\", role = c(\"aut\"), email = \"p.kattuman@jbs.cam.ac.uk\"),\nperson(given = \"Andrew\", family = \"Harvey\", role = c(\"aut\"), email = \"ach34@cam.ac.uk\"),\nperson(given = \"Edwin\", family = \"Tang\", role = c(\"aut\"), email = \"yiu-nam-edwin.tang@warwick.ac.uk\"),\nperson(given = \"Craig\", family = \"Thamotheram\", role = c(\"aut\"), email = \"cpthamotheram@yahoo.co.uk\"),\nperson(given = \"Guglielmo\", family = \"Secchi\", role = c(\"aut\"), email = \"ggas2@cam.ac.uk\"),\nperson(\"Cambridge Centre for Health Leadership & Enterprise, Cambridge Judge Business School, University of Cambridge\",\nrole = \"fnd\"),\nperson(\"UK Health Security Agency\",\nrole = \"fnd\"),\nperson(\"Keynes Fund, Faculty of Economics, University of Cambridge\",\nrole = \"fnd\", comment = \"Supported A. Harvey\"),\nperson(\"University of Cambridge Social Science Impact Fund\",\nrole = \"fnd\", comment = \"Supported P. Kattuman\"),\nperson(\"Cambridge Mathematics Placements Programme, University of Cambridge\",\nrole = \"fnd\", comment = \"Supported E. Tang\"),\nperson(\"Caldecott Bursary Fund, Magdalene College, University of Cambridge\",\nrole = \"fnd\", comment = \"Supported E. Tang\"),\nperson(\"Chancellor's Scholarship Scheme, University of Warwick\",\nrole = \"fnd\", comment = \"Supported E. Tang\"),\nperson(\"Statistics Centre for Doctoral Training, University of Warwick\",\nrole = \"fnd\", comment = \"Supported E. Tang\"))", "Description": "Provides tools for modelling and forecasting epidemic\ntrajectories using a dynamic Gompertz model within a state\nspace framework, with the Kalman filter for robust estimation\nof non-linear growth. Includes a reinitialization feature to\nadapt to new waves, and a leading-indicator extension that uses\na related series moving ahead of the variable of interest (e.g.\ncases ahead of hospitalisations) to improve short-horizon\nforecasts, with model and lag selection via rolling-origin\ncross-validation. Applicable to data at daily, monthly,\nquarterly, or annual frequency, and to non-epidemic\ntrajectories with similar dynamics, such as innovation\ndiffusion and product adoption. Includes functions for data\npreprocessing, model fitting, forecast visualization, and\naccuracy evaluation using standard error measures. Methods are\ndescribed in Harvey and Kattuman (2020)\n, Harvey and Kattuman (2021)\n, and Ashby, Harvey, Kattuman,\nTang, and Thamotheram (2024)\n.", "URL": "https://github.com/edwintang903/tsgc", "License": "GPL (>= 3)", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "BugReports": "https://github.com/edwintang903/tsgc/issues", "LazyData": "true", "Config/roxygen2/version": "8.0.0", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev libicu-dev libpng-dev\nlibuv1-dev libxml2-dev", "Repository": "https://edwintang903.r-universe.dev", "Date/Publication": "2026-09-01 09:41:52 UTC", "RemoteUrl": "https://github.com/edwintang903/tsgc", "RemoteRef": "HEAD", "RemoteSha": "02b7bbba8a444cac24376bb6c7fd0cfd8ed74e7e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-10 16:13:31 UTC", "User": "root" }, "Author": "Michael Ashby [aut, cre],\nPaul Kattuman [aut],\nAndrew Harvey [aut],\nEdwin Tang [aut],\nCraig Thamotheram [aut],\nGuglielmo Secchi [aut],\nCambridge Centre for Health Leadership & Enterprise, Cambridge Judge\nBusiness School, University of Cambridge [fnd],\nUK Health Security Agency [fnd],\nKeynes Fund, Faculty of Economics, University of Cambridge [fnd]\n(Supported A. Harvey),\nUniversity of Cambridge Social Science Impact Fund [fnd] (Supported P.\nKattuman),\nCambridge Mathematics Placements Programme, University of Cambridge\n[fnd] (Supported E. Tang),\nCaldecott Bursary Fund, Magdalene College, University of Cambridge\n[fnd] (Supported E. Tang),\nChancellor's Scholarship Scheme, University of Warwick [fnd] (Supported\nE. Tang),\nStatistics Centre for Doctoral Training, University of Warwick [fnd]\n(Supported E. Tang)", "Maintainer": "Michael Ashby ", "_user": "edwintang903", "_type": "src", "_file": "tsgc_2.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/41132a08fc817ce14084a39bcadaf2f5f6bf990b3bc8613e0c7be1d19ee498bf", "_filesize": 3329830, "_sha256": "41132a08fc817ce14084a39bcadaf2f5f6bf990b3bc8613e0c7be1d19ee498bf", "_expires": "2027-01-18T17:16:12.000Z", "_created": "2026-10-10T16:13:31.000Z", "_published": "2026-10-10T17:16:13.715Z", "_buildurl": "https://github.com/r-universe/edwintang903/actions/runs/38066549108", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114256015859, "time": 153, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11674879988" }, { "job": 114256015914, "time": 167, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11674229380" }, { "job": 114256015916, "time": 242, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11676537618" }, { "job": 114256015939, "time": 208, 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Thamotheram", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Theory", "Gompertz curve", "Dynamic Gompertz model", "State space form and estimation", "Forecasts and peak prediction", "Reproduction Number $R_t$", "Reinitialization", "Reinitializing the data series", "Reinitializing the model", "Functionality of tsgc", "Leading indicator model", "Forecast accuracy and rolling-origin validation", "Illustration of the tsgc package", "Setting up the forecasting exercise", "Estimation", "Model diagnostics", "Results", "Triggering reinitialization", "Estimating the reinitialized model", "Leading indicator model: England cases and hospitalisations", "Adding regressors and future covariate paths", "Comparing the dynamic Gompertz and leading-indicator models", "Selecting the lag by rolling cross-validation", "Extensions: non-epidemiological applications and alternative data frequencies", "Conclusions", "Acknowledgements", "References", "Appendix: Incorporating seasonal terms into the state space model", "Appendix: How to import data from CSV file", "Appendix: Controlling the plot x-axis with idx_axis_opts()", "mode = \"date\" (the default here, since gauteng_cal$posixct = TRUE)", "mode = \"position\"", "mode = \"steps\"", "mode = \"time_since\"", "Adding an info box", "Appendix: Compound calendar steps with idx_step() and idx_calendar_step()", "Appendix: Heterogeneous repeating cycles with multi_step_pattern() and idx_calendar_multi_step()", "Appendix: Non-calendar idx_calendar anchors and idx_offset_to_pos()" ], "created": "2024-02-27 13:16:53", "modified": "2026-08-13 22:15:14", "commits": 26 } ], "_score": 6.9834007381805385, "_indexed": true, "_nocasepkg": "tsgc", "_universes": [ "edwintang903", "mashby22" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.0.0", "date": "2026-10-10T16:15:40.000Z", "distro": "resolute", "commit": "02b7bbba8a444cac24376bb6c7fd0cfd8ed74e7e", "fileid": 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