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.

翻译：本文介绍了一种用于估计Rahman（2016）提出的序数模型贝叶斯分位数回归的R包bqror。论文将序数模型分为两类，并提供了计算高效且简便的马尔可夫链蒙特卡洛（MCMC）算法来估计序数分位数回归。对于具有3个或更多结果的通用序数模型（标记为ORI模型），通过结合吉布斯采样和Metropolis-Hastings算法进行估计。而对于恰好具有3个结果的序数模型（标记为ORII模型），则仅使用吉布斯采样进行估计。与贝叶斯文献一致，我们建议使用边际似然来比较替代分位数回归模型，并解释如何计算该值。通过多项模拟研究说明模型及其估计程序，并在两个应用中实施。本文还描述了bqror包中包含的其他几个函数，这些函数对于估计、推理和评估模型拟合度是必要的。