DiPPER - Differential Prevalence via Probabilistic Estimation in R

Implements a Bayesian hierarchical model for differential prevalence analysis, tailored for microbiome studies. The method accommodates both cross-sectional and longitudinal designs, providing finite effect estimates and uncertainty intervals that are naturally adjusted for multiple testing via hierarchical shrinkage. Posterior inference is performed via Markov chain Monte Carlo (MCMC) using 'CmdStan' as the backend.