tsgc - Time Series Methods Based on Growth Curves

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>.