A comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions. For users' convenience, the functions also provide the output option 
    for printing decision rules.

A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.

Implements the optimal multiple hypothesis testing correction from
    Viviano, Wuthrich, and Niehaus (2026) <doi:10.48550/arXiv.2104.13367>. Derives the
    optimal per-test significance level from the economic incentives of research
    production, providing a correction that lies between Bonferroni (too
    conservative) and unadjusted (too permissive). Supports two cost models: a
    Linear one calibrated to United States Food and Drug Administration (FDA)
    clinical-trial costs, and a Cobb-Douglas one calibrated to Abdul Latif
    Jameel Poverty Action Lab (J-PAL) project costs. Reports optimal,
    Bonferroni, Holm, Benjamini-Hochberg (BH), and unadjusted results side by
    side.

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.