Package: bayesQR 2.4
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) <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.
Authors:
bayesQR_2.4.tar.gz
bayesQR_2.4.zip(r-4.5)bayesQR_2.4.zip(r-4.4)bayesQR_2.4.zip(r-4.3)
bayesQR_2.4.tgz(r-4.4-x86_64)bayesQR_2.4.tgz(r-4.4-arm64)bayesQR_2.4.tgz(r-4.3-x86_64)bayesQR_2.4.tgz(r-4.3-arm64)
bayesQR_2.4.tar.gz(r-4.5-noble)bayesQR_2.4.tar.gz(r-4.4-noble)
bayesQR_2.4.tgz(r-4.4-emscripten)bayesQR_2.4.tgz(r-4.3-emscripten)
bayesQR.pdf |bayesQR.html✨
bayesQR/json (API)
| # Install 'bayesQR' in R: |
| install.packages('bayesQR', repos = c('https://driesbenoit.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/driesbenoit/bayesqr/issues
Last updated 1 years agofrom:79cbe8758e. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win-x86_64 | OK | Nov 07 2024 |
| R-4.5-linux-x86_64 | OK | Nov 07 2024 |
| R-4.4-win-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-aarch64 | OK | Nov 07 2024 |
| R-4.3-win-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-aarch64 | OK | Nov 07 2024 |
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian quantile regression | bayesQR |
| Customer Churn Data | Churn |
| Produce quantile plots or traceplots with 'plot.bayesQR' | plot.bayesQR |
| Calculate predicted probabilities for binary quantile regression | predict.bayesQR |
| Prints the contents of 'bayesQR' object to the console | print.bayesQR |
| Prints the contents of 'bayesQR.summary' object to the console | print.bayesQR.summary |
| Create prior for Bayesian quantile regression | prior |
| Prostate Cancer Data | Prostate |
| Summarize the output of the 'bayesQR' function | summary.bayesQR |
