QBLD - Quantile Regression for Binary Longitudinal Data

This project follows Rahman and Vossmeyer (2019) as its motivating literature, and contributes to the three literatures by extending the various methodologies to a hierarchical Bayesian quantile regression model for binary longitudinal data (QBLD) and proposing a Markov chain Monte Carlo (MCMC) algorithm to estimate the model. The model handles both common (fixed) and individual-specific (random) parameters (commonly referred to as mixed effects in statistics). The algorithm implements a blocking procedure that is computationally efficient and the distributions involved allow for straightforward calculations of covariate effects.