A collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.

Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.

The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories.
    It is designed to model the progression of an epidemic over time while accounting for the various uncertainties
    inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach,
    using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in
    epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves.
    The forecasts generated by the package are of value to public health officials and researchers who need to
    understand and predict the course of an epidemic to inform decision-making. Beyond its application in public
    health, the package is also a useful resource for researchers and practitioners in fields where the trajectories
    of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for
    data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy.
    The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as 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, and Thamotheram (2024)
    <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.