bqror: An R package for Bayesian Quantile Regression in Ordinal Models

This article describes an R package bqror that estimates Bayesian quantile regression in ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient yet simple Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with 3 or more outcomes (labeled ORI model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm, whereas an ordinal model with exactly 3 outcomes (labeled ORII model) is estimated using a Gibbs sampling algorithm only. In line with the Bayesian literature, we suggest using the marginal likelihood for comparing alternative quantile regression models and explain how to compute it. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in two applications. The article also describes several functions contained within the bqror package, and illustrates their usage for estimation, inference, and assessing model fit.