bqror: Bayesian Quantile Regression for Ordinal Models

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>.

Getting started

Package details

AuthorMohammad Arshad Rahman Developer [aut], Prajual Maheshwari [cre]
MaintainerPrajual Maheshwari <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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bqror documentation built on Sept. 28, 2021, 5:07 p.m.