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.

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 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.

Multiple Hypotheses Testing for Multiple Families/Groups
Structure
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.

Bayesian Model Selection in Logistic Regression for the
Detection of Adverse Drug Reactions
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.