  This article describes an R package bqror that estimates Bayesian quantile
regression for 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 Gibbs sampling only. In line with the Bayesian
literature, we suggest using marginal likelihood for comparing alternative
quantile regression models and explain how to compute the same. The models and
their estimation procedures are illustrated via multiple simulation studies and
implemented in two applications. The article also describes several other
functions contained within the bqror package, which are necessary for
estimation, inference, and assessing model fit.
