An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()).
  The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

A regularization method for the cumulative link
    models.  The smooth-effect-on-response penalty (SERP) provides
    flexible modelling of the ordinal model by enabling the smooth
    transition from the general cumulative link model to a coarser form of
    the same model. In other words, as the tuning parameter goes from zero
    to infinity, the subject-specific effects associated with each
    variable in the model tend to a unique global effect. The parameter
    estimates of the general cumulative model are mostly unidentifiable or
    at least only identifiable within a range of the entire parameter
    space. Thus, by maximizing a penalized rather than the usual
    non-penalized log-likelihood, this and other numerical problems common
    with the general model are to a large extent eliminated. Fitting is
    via a modified Newton's method. Several standard model performance and
    descriptive methods are also available. For more details on the penalty
    implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.

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