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.

Install the latest version of this package by entering the following in R:

`install.packages("bayesQR")`

Author | Dries F. Benoit, Rahim Al-Hamzawi, Keming Yu, Dirk Van den Poel |

Date of publication | 2017-01-27 10:38:21 |

Maintainer | Dries F. Benoit <Dries.Benoit@UGent.be> |

License | GPL (>= 2) |

Version | 2.3 |

**bayesQR:** Bayesian quantile regression

**Churn:** Customer Churn Data

**plot.bayesQR:** Produce quantile plots or traceplots with 'plot.bayesQR'

**predict.bayesQR:** Calculate predicted probabilities for binary quantile...

**print.bayesQR:** Prints the contents of 'bayesQR' object to the console

**print.bayesQR.summary:** Prints the contents of 'bayesQR.summary' object to the...

**prior:** Create prior for Bayesian quantile regression

**Prostate:** Prostate Cancer Data

**summary.bayesQR:** Summarize the output of the 'bayesQR' function

inst

inst/CITATION

src

src/Makevars

src/QRb_AL_mcmc.f95

src/QRc_mcmc.f95

src/setseed.f95

src/QRb_mcmc.f95

src/QRc_AL_mcmc.f95

NAMESPACE

data

data/Prostate.rda

data/Churn.rda

R

R/plot.bayesQR.r
R/prior.r
R/summary.bayesQR.single.r
R/bayesQR.single.r
R/print.bayesQR.r
R/print.bayesQR.summary.r
R/summary.bayesQR.r
R/predict.bayesQR.r
R/bayesQR.r
MD5

DESCRIPTION

man

man/print.bayesQR.summary.Rd
man/print.bayesQR.Rd
man/Churn.Rd
man/Prostate.Rd
man/summary.bayesQR.Rd
man/bayesQR.Rd
man/prior.Rd
man/predict.bayesQR.Rd
man/plot.bayesQR.Rd
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