Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) <doi:10.1016/j.vaccine.2015.02.042> and Machado et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.45.1900268> for influenza vaccination impact.

Various semiparametric and nonparametric statistical tools for
    immune correlates analysis of vaccine clinical trial data. This includes
    calculation of summary statistics and estimation of risk, vaccine efficacy,
    controlled effects (controlled risk and controlled vaccine efficacy), and
    mediation effects (natural direct effect, natural indirect effect,
    proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022)
    <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023)
    <doi:10.48550/arXiv.2208.06465>.

Provides tools for estimating vaccine effectiveness and
    related metrics. The 'vaccineff_data' class manages key features for
    preparing, visualizing, and organizing cohort data, as well as
    estimating vaccine effectiveness. The results and model performance
    are assessed using the 'vaccineff' class.

A method to visualize pharmacometric analyses which are impacted by
    covariate effects. Variability-aligned covariate harmonized-effects
    and time-transformation equivalent ('vachette') facilitates intuitive
    overlays of data and model predictions, allowing for comprehensive
    comparison without dilution effects. 'vachette' improves upon previous
    methods Lommerse et al. (2021) <doi:10.1002/psp4.12679>, enabling its
    application to all pharmacometric models and enhancing Visual Predictive
    Checks (VPC) by integrating data into cohesive plots that can highlight
    model misspecification.

An implementation of three procedures developed by 
    John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM 
    (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. 
    Combined, they provide a way to identify, treat, and analyze outliers 
    in two-way (i.e., contingency) tables, as described in his 
    landmark paper "The Future of Data Analysis", Tukey, John W. (1962) 
    <https://www.jstor.org/stable/2237638>.