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>.

Implements a regularization method for cumulative link models using the 
    Smooth-Effect-on-Response Penalty (SERP). This method allows flexible 
    modeling of ordinal data by enabling a smooth transition from a general 
    cumulative link model to a simplified version of the same model. As the 
    tuning parameter increases from zero to infinity, the subject-specific 
    effects for each variable converge to a single global effect. 
    The approach addresses common issues in cumulative link models, such as 
    parameter unidentifiability and numerical instability, by maximizing a 
    penalized log-likelihood instead of the standard non-penalized version. 
    Fitting is performed using a modified Newton's method. Additionally, the 
    package includes various model performance metrics and descriptive tools.
    For details on the implemented penalty method, see 
    Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.

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Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
    mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
    using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
    observations of SERRS are highly collinear and lend themselves to a reduced-rank
    representation. The GAMM separates the SERRS signal into three components: a
    sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
    and additive white noise. The parameters of each component of the model are estimated
    iteratively using SMC. The posterior distributions of the parameters given the observed
    spectra are represented as a population of weighted particles.