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bayesQR (version 2.0)

Bayesian quantile regression

Description

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.

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Version

Install

install.packages('bayesQR')

Monthly Downloads

931

Version

2.0

License

GPL (>= 2)

Maintainer

Dries Benoit

Last Published

January 4th, 2013

Functions in bayesQR (2.0)

QRsummary

Summarize the betadraws of QRc, QRc.AL, QRb, QRb.AL or QRseq
QRc

Bayesian quantile regression with continuous dependent variable
QRb.AL

Bayesian quantile regression with binary dependent variable using adaptive LASSO
Churn

Customer Churn Data
QRb

Bayesian quantile regression with binary dependent variable
Prostate

Prostate Cancer Data
QRseq

Estimate series of Bayesian quantile regressions with QRc or QRb
QRc.AL

Bayesian quantile regression with continuous dependent variable using adaptive LASSO
QRb.pred

Calculate predicted probabilities with QRb.pred
QRplot

Produce quantile plots with QRplot