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.

A Bayesian framework for inferring influenza infection status from
    serial antibody measurements. Jointly estimates season-specific infection
    probabilities, antibody boosting and waning after infection, and baseline
    hemagglutination inhibition (HAI) titer distributions via Markov chain
    Monte Carlo (MCMC). Supports multi-season analysis and subgroup
    comparisons via a group_by interface. See Tsang et al. (2022)
    <doi:10.1038/s41467-022-29310-8> for methodological details.

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>.