Description: 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
Packaged: 2016-10-27 16:57:26 UTC; kaijingzhang

Packaged: 2016-12-02 18:19:08 UTC; kaijingzhang

decisions. For users' convenience, the functions also provide the output option

Description: A comprehensive tool for almost all existing multiple testing

Title: Multiple Hypotheses Testing for Multiple Families/Groups
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.

Title: Bayesian Model Selection in Logistic Regression for the
Author: Matthieu Marbac and Mohammed Sedki
Description: 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.

Title: General Markov Chain Monte Carlo for Bayesian Inference using
Description: Performs general Metropolis-Hastings Markov Chain Monte
Carlo sampling of a user defined function which returns the
un-normalized value (likelihood times prior) of a Bayesian
model.  The proposal variance-covariance structure is updated
adaptively for efficient mixing when the structure of the
target distribution is unknown.  The package also provides some
functions for Bayesian inference including Bayesian Credible
Intervals (BCI) and Deviance Information Criterion (DIC)