bayesQR: Bayesian Quantile Regression

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001), Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012). To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

Maintainer

Dries F. Benoit (Ghent University)
Repository hosted by the CVAMO research group, Ghent University

Cite

To cite bayesQR in publications use:

Benoit DF, Van den Poel D (2017). "bayesQR: A Bayesian Approach to Quantile Regression." Journal of Statistical Software, 76(7), 1–32. (https://doi.org/10.18637/jss.v076.i07).

Corresponding BibTeX entry:

@Article{,
  title = {{bayesQR}: A Bayesian Approach to Quantile Regression},
  author = {Dries F. Benoit and Dirk {Van den Poel}},
  journal = {Journal of Statistical Software},
  year = {2017},
  volume = {76},
  number = {7},
  pages = {1--32},
  doi = {10.18637/jss.v076.i07},
}

CRAN

(https://CRAN.R-project.org/package=bayesQR)

License

This package is released under the GPL (>= 2) license. See the DESCRIPTION file for details.