# BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features

## TL;DR

BCGLMs is an R package that uses Bayesian methods to analyze microbiome data for predicting diseases and other outcomes.

## Contribution

It introduces Bayesian compositional models with random effects and phylogenetic relationships for microbiome analysis.

## Key findings

- The package supports various response types including continuous, binary, ordinal, and survival data.
- Incorporating phylogenetic relationships improves prediction accuracy in microbiome studies.
- The package provides numerical and graphical tools for model result summarization.

## Abstract

BCGLMs is a freely available R package that provides functions for setting up and fitting Bayesian compositional models for continuous, binary, ordinal and survival responses. It also includes models with random effects to capture sample-related accumulated small effects, improving prediction accuracy. The package includes tools for summarizing results from fitted models both numerically and graphically. Built on top of the widely used brms package, BCGLMs enable users to incorporate phylogenetic relationships between microbiome taxa into the modeling framework. Overall, BCGLMs package offers a flexible and powerful set of tools for analyzing compositional microbiome data.

The package is publicly available via GitHub https://github.com/Li-Zhang28/BCGLMs.