Title: Bayesian Purity Model to Estimate Tumor Purity
Author: Jianzhao Gao, Linghao Shen, Xiaodan Fan
Maintainer: Jianzhao Gao and Xiaodan Fan <gaojz@nankai.edu.cn>
Description: Bayesian purity model to estimate tumor purity using methylation
array data (DNA methylation Infinium 450K array data) without reference samples.

Description: A new regression model for positive random variables with skewed
Imports: base, extraDistr, gamlss, gamlss.dist, ggplot2, Deriv, dplyr,

Title: BRACoD: Bayesian Regression Analysis of Compositional Data
Description: The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.
Config/reticulate: list( packages = list( list(package = "BRACoD") ) )

Title: Binomial Reliability Demonstration Tests
Description: This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data.
The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated.
This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) <doi:10.1080/08982112.2020.1757703>.

Title: Brain Cell Type Specific Gene Expression Analysis
Description: Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.