Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.

An extended epidemiological modelling framework that goes beyond the
    classical SIR (Susceptible-Infectious-Recovered) model. Supports
    SEIR (Susceptible-Exposed-Infectious-Recovered),
    SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased),
    SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased),
    and age-stratified compartmental models with flexible intervention
    functions (spline-based, Gaussian process, or user-defined). Inference
    is available via maximum likelihood or sequential Monte Carlo (SMC,
    also known as particle filtering) with no external binary dependencies.
    Includes a dependency-free real-time effective reproduction number (Rt)
    estimator, spatial multi-patch models with gravity-model mobility,
    ensemble forecasting via Bayesian model averaging (BMA), and proper
    scoring rules including CRPS (Continuous Ranked Probability Score),
    coverage, and MAE (Mean Absolute Error) for forecast evaluation.
    Methods follow Anderson and May (1991, ISBN:9780198545996),
    Doucet, de Freitas, and Gordon (2001) <doi:10.1007/978-1-4757-3437-9>,
    Cori et al. (2013) <doi:10.1093/aje/kwt133>,
    and Gneiting and Raftery (2007) <doi:10.1198/016214506000001437>.

Estimates the time-varying reproduction number, rate of
    spread, and doubling time using a renewal equation approach combined
    with Bayesian inference via Stan. Supports Gaussian process and
    random walk priors for modelling changes in
    transmission over time. Accounts for delays between infection and
    observation (incubation period, reporting delays), right-truncation
    in recent data, day-of-week effects, and observation overdispersion.
    Can estimate relationships between primary and secondary outcomes
    (e.g., cases to hospitalisations or deaths) and forecast both. Runs
    across multiple regions in parallel. Based on
    Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1> and
    Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>.