Provides tools to analyze vaccine coverage data and simulate potential disease outbreak scenarios. It allows users to calculate key epidemiological metrics such as the effective reproduction number (Re), outbreak probabilities, and expected infection counts based on county-level vaccination rates, disease characteristics, and vaccine effectiveness. The package includes historical kindergarten vaccination data for Florida counties and offers functions for generating summary tables, visualizations, and exporting the underlying plot data.

Estimate vaccine efficacy (VE) using immunogenicity data.
    The inclusion of immunogenicity data in regression models can increase precision in VE. 
    The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).

Position adjustments for 'ggplot2' to implement "visualize as
    you randomize" principles, which can be especially useful when
    plotting experimental data.

Provides tools for analysis blinding in confirmatory research contexts
  by masking and scrambling test-relevant aspects of data. Vector-, data frame-, and
  row-wise operations support blinding for hierarchical and repeated-measures designs.
  For more details see MacCoun and Perlmutter (2015) <doi:10.1038/526187a> and
  Dutilh, Sarafoglou, and Wagenmakers (2019) <doi:10.1007/s11229-019-02456-7>.

Computes the Gaussian variational approximation of the Bayesian 
    empirical likelihood posterior. This is an implementation of the function 
    found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.

Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.

Provides methods for variance-based sensitivity analysis and weighting estimators in observational studies based on methodology by Huang & Pimentel (2025) <doi:10.1093/biomet/asae040>. Includes bootstrap inference, bias bounds estimation, and visualization tools for sensitivity parameters.