Title: Sensitivity of the Bayesian Adjustment for Confounding (BAC)
algorithm to the choice of hyperparameter omega.
Author: Denis jf Talbot, Genevi�ve Lefebvre, Juli Atherton
Maintainer: Denis jf Talbot <talbot.denis@courrier.uqam.ca>
Description: The BACprior package provides an approximate sensitivity
analysis of the Bayesian Adjustment for Confounding (BAC)
algorithm (Wang et al., 2012) with regards to hyperparameter
omega. The method is based on Lefebvre, Atherton and Talbot

Title: Choice of the Hyperparameter Omega in the Bayesian Adjustment
Author: Denis Talbot, Genevi�ve Lefebvre, Juli Atherton
Description: The BACprior package provides an approximate sensitivity analysis of the
Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) with regards to the
hyperparameter omega. The package also provides functions to guide the user in their choice
of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Title: Choice of Omega in the BAC Algorithm
Description: The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
This package provides an approximate sensitivity analysis of BAC with regards to the
hyperparameter omega. BACprior also provides functions to guide the user in their choice

Title: Bayesian Analysis of Differential Expression in RNA Sequencing
Author: Andreas Neudecker, Matthias Katzfuss
Description: For RNA sequencing count data, BADER fits a Bayesian
hierarchical model. The algorithm returns the posterior
probability of differential expression for each gene between
two groups A and B. The joint posterior distribution of the
variables in the model can be returned in the form of posterior
samples, which can be used for further down-stream analyses

Title: Fully Bayesian Analysis of Differential Expression in RNA
Description: This package lets you estimate the posterior distribution
of various parameters regarding RNA Sequencing data. The most
interesting parameter is the probability of differential
expression (DE) between two groups A and B. But also estimates
for the log mean and the log dispersion parameter of the
underlying poisson - log-normal model can be returned.