A suite of methods to fit and predict case count data using 
    a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) 
    model, based on an assumed specification of the effective reproduction 
    number. The significance of this approach is that it relates epidemic 
    progression to the average number of contacts of infected individuals, 
    which decays as a function of the total susceptible fraction remaining 
    in the population. The main functions are pred.curve(), which computes 
    the epidemic curve for a set of parameters, and estimate.mle(), which 
    finds the best fitting curve to observed data. The easiest way to pass 
    arguments to the functions is via a config file, which contains input 
    settings required for prediction, and the package offers two methods, 
    navigate_to_config()  which points the user to the configuration file, 
    and re_predict() for starting the fit-predict process. The main model was published in
    Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.

Indirect method for the estimation of reference intervals (RIs)
    using Real-World Data ('RWD') and methods for comparing and verifying RIs.
    Estimates RIs by applying advanced statistical methods to routine
    diagnostic test measurements, which include both pathological and
    non-pathological samples, to model the distribution of non-pathological
    samples. This distribution is then used to derive reference intervals
    and support RI verification, i.e., deciding if a specific RI is suitable
    for the local population. The package also provides functions for
    printing and plotting algorithm results. See ?refineR for a detailed
    description of features. Version 1.0 of the algorithm is described in
    'Ammer et al. (2021)' <doi:10.1038/s41598-021-95301-2>. Additional
    guidance is in 'Ammer et al. (2023)' <doi:10.1093/jalm/jfac101>. The
    verification method is described in 'Beck et al. (2025)'
    <doi:10.1515/cclm-2025-0728>.