Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package

= 1,383). Key findings include strong associations between concurrent substance use and alcohol consumption (nicotine use associated with 13.5% increase in drinks and 14 times higher odds of drinking), discovery of nonlinear age effects on drinking variability (peak at ages 25-30), and high-accuracy daily predictions (median correlation 0.82, median absolute error 1.0 drinks). The Bayesian framework provides uncertainty quantification essential for both research and clinical applications, while the range of algorithms allows researchers to navigate the complexity-interpretability tradeoff. While developed for alcohol research, the methodological framework addresses statistical challenges common across substance use research.