doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.">

bayesQR: Bayesian Quantile Regression (original) (raw)

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) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

Version: 2.4
Depends: R (≥ 4.2), graphics, methods, stats, utils
Published: 2023-09-08
DOI: 10.32614/CRAN.package.bayesQR
Author: Dries F. Benoit, Rahim Al-Hamzawi, Keming Yu, Dirk Van den Poel
Maintainer: Dries F. Benoit <Dries.Benoit at UGent.be>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: bayesQR citation info
In views: Bayesian
CRAN checks: bayesQR results

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