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.
|Author||Dries F. Benoit, Rahim Al-Hamzawi, Keming Yu, Dirk Van den Poel|
|Date of publication||2014-04-18 00:35:53|
|Maintainer||Dries F. Benoit <Dries.Benoit@UGent.be>|
|License||GPL (>= 2)|
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.summary' object to the...
prior: Create prior for Bayesian quantile regression
Prostate: Prostate Cancer Data
summary.bayesQR: Summarize the output of the 'bayesQR' function