Estimating the force of infection from time varying, age varying, or constant
    serocatalytic models from population based seroprevalence studies using a Bayesian framework,
    including data simulation functions enabling the generation of serological surveys based on this
    models. This tool also provides a flexible prior specification syntax for the force of infection
    and the seroreversion rate, as well as methods to assess model convergence and comparison
    criteria along with useful visualisation functions.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project 'PvSTATEM', which is an international project aiming for malaria elimination.

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>.