A robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2024), <doi:10.1371/journal.pgen.1011423>].

Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.

Facilitates probabilistic record linkage between infectious disease
    surveillance datasets (notifiable disease registers, outbreak line-lists),
    vaccination registries, and hospitalization records using methods based on
    Fellegi and Sunter (1969) <doi:10.1080/01621459.1969.10501049> and
    Sayers et al. (2016) <doi:10.1093/ije/dyv322>. The package provides
    core functions for data preparation, linkage, and analysis: clean_the_nest() 
    standardizes variable names and formats across heterogeneous datasets; 
    murmuration() performs machine learning-based record linkage using blocking 
    variables and similarity metrics; molting() deidentifies datasets for secure 
    sharing; homing() re-identifies previously deidentified datasets; plumage() 
    identifies and categorizes comorbidities; and preening() creates analysis-ready 
    variables including age categories and temporal groupings. Designed for 
    epidemiological research linking acute and post-acute disease outcomes to 
    vaccination status and healthcare utilization. Supports multiple linkage 
    scenarios including case-to-vaccination, case-to-hospitalization, and
    event-based vaccination status determination (e.g., outbreak attendees, flight
    passengers, exposure site visitors).

Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.