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>.

Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained.