Provides functions for estimating Bayesian quantile regression
for ordinal models, calculating covariate effects, and computing measures for
model comparision. Specifically, the package offers two estimation functions -
one for an ordinal model with more than three outcomes. For each ordinal model,
the package provides functions to calculate the covariate effect using the MCMC
outputs. The package also computes marginal likelihood (recommended) and the
Deviance Information Criterion (DIC) for comparing alternative quantile regression
models. Besides, the package also contains functions for making trace plots of MCMC draws
and other functions that aids the estimation or inference of quantile ordinal models.
Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian
Analysis, II(I): 1-24 <doi: 10.1214/15-BA939>.
Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and
Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica,
46(1): 33-50 <doi: 10.2307/1913643>.
Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of
the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the
Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.

MASS, pracma, GIGrvg, truncnorm, NPflow, invgamma, graphics,
stats, progress
Package provides functions for estimating Bayesian quantile regression with ordinal outcomes,
computing the covariate effects, model comparison measures, and inefficiency factor. The generic
ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs
sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes
(labeled OR2 model) is estimated using Gibbs sampling only. For each model framework, there is a
specific function for estimation. The summary output produces estimates for regression quantiles
and two measures of model comparison — log of marginal likelihood and Deviance Information Criterion
(DIC). The package also has specific functions for computing the covariate effects and other functions
that aids either the estimation or inference in quantile ordinal models.
Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian
Analysis, II(I): 1-24 <doi: 10.1214/15-BA939>.
Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and
Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica,
46(1): 33-50 <doi: 10.2307/1913643>.
Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of
the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the
Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.