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"),
    person(given = "Paul", family = "Kattuman", role = c("aut"), email = "p.kattuman@jbs.cam.ac.uk"),
    person(given = "Andrew", family = "Harvey", role = c("aut"), email = "ach34@cam.ac.uk"),
    person(given = "Edwin", family = "Tang", role = c("aut"), email = "yiu-nam-edwin.tang@warwick.ac.uk"),
    person(given = "Craig", family = "Thamotheram", role = c("aut"), email = "craig_thamotheram@hotmail.com"),
    person(given = "Guglielmo", family = "Secchi", role = c("aut"), email = "ggas2@cam.ac.uk"),
      person("Cambridge Centre for Health Leadership & Enterprise, Cambridge Judge Business School, University of Cambridge",
           role = "fnd"),
    person("UK Health Security Agency",
           role = "fnd"),
    person("Keynes Fund, Faculty of Economics, University of Cambridge",
           role = "fnd", comment = "Supported A. Harvey"),
    person("University of Cambridge Social Science Impact Fund",
           role = "fnd", comment = "Supported P. Kattuman"),
    person("Cambridge Mathematics Placements Programme, University of Cambridge",
           role = "fnd", comment = "Supported E. Tang"),
    person("Caldecott Bursary Fund, Magdalene College, University of Cambridge",
           role = "fnd", comment = "Supported E. Tang"),
    person("Chancellor's Scholarship Scheme, University of Warwick",
           role = "fnd", comment = "Supported E. Tang"),
    person("Statistics Centre for Doctoral Training, University of Warwick",
           role = "fnd", comment = "Supported E. Tang"))
Description: Provides tools for modelling and forecasting epidemic trajectories
    using a dynamic Gompertz model within a state space framework, with the
    Kalman filter for robust estimation of non-linear growth. Includes a
    reinitialization feature to adapt to new waves, and a leading-indicator
    extension that uses a related series moving ahead of the variable of
    interest (e.g. cases ahead of hospitalisations) to improve short-horizon
    forecasts, with model and lag selection via rolling-origin
    cross-validation. Applicable to data at daily, monthly, quarterly, or
    annual frequency, and to non-epidemic trajectories with similar dynamics,
    such as innovation diffusion and product adoption. Includes functions for
    data preprocessing, model fitting, forecast visualization, and accuracy
    evaluation using standard error measures. Methods are described in Harvey
    and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman
    (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, Tang,
    and Thamotheram (2024)
    <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.
URL: https://github.com/edwintang903/tsgc
License: GPL (>= 3)
Encoding: UTF-8
VignetteBuilder: knitr
Suggests: ggfortify, knitr, RColorBrewer, rmarkdown, ggforce,
        gridExtra, latex2exp, here, testthat, dplyr, ggthemes
Config/testthat/edition: 3
Imports: KFAS, xts, ggplot2 (>= 3.5.0), zoo, magrittr, tidyr, methods,
        abind, purrr, scales, kableExtra
BugReports: https://github.com/edwintang903/tsgc/issues
Depends: R (>= 3.5.0)
LazyData: true
Config/roxygen2/version: 8.0.0
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-08-24 19:07:58 UTC; Michael Ashby
Author: Michael Ashby [aut, cre],
  Paul Kattuman [aut],
  Andrew Harvey [aut],
  Edwin Tang [aut],
  Craig Thamotheram [aut],
  Guglielmo Secchi [aut],
  Cambridge Centre for Health Leadership & Enterprise, Cambridge Judge
    Business School, University of Cambridge [fnd],
  UK Health Security Agency [fnd],
  Keynes Fund, Faculty of Economics, University of Cambridge [fnd]
    (Supported A. Harvey),
  University of Cambridge Social Science Impact Fund [fnd] (Supported P.
    Kattuman),
  Cambridge Mathematics Placements Programme, University of Cambridge
    [fnd] (Supported E. Tang),
  Caldecott Bursary Fund, Magdalene College, University of Cambridge
    [fnd] (Supported E. Tang),
  Chancellor's Scholarship Scheme, University of Warwick [fnd] (Supported
    E. Tang),
  Statistics Centre for Doctoral Training, University of Warwick [fnd]
    (Supported E. Tang)
Maintainer: Michael Ashby <mwa22@cam.ac.uk>
Repository: CRAN
Date/Publication: 2026-09-01 12:50:02 UTC
