Diagnostics methods for quantile regression models for detecting influential observations: robust distance methods for general quantile regression models; generalized Cook's distance and Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model. Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, quantreg, purrr, magrittr, ALDqr, bayesQR, MCMCpack, ggplot2, knitr, gridExtra, GIGrvg, dplyr, tidyr, robustbase, ald |
| Suggests: | testthat, rmarkdown |
| Published: | 2017-11-10 |
| Author: | Wenjing Wang, Di Cook, Earo Wang |
| Maintainer: | Wenjing Wang <wenjingwangr at gmail.com> |
| BugReports: | https://github.com/wenjingwang/quokar/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/wenjingwang/quokar |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | quokar results |
| Reference manual: | quokar.pdf |
| Vignettes: |
'quokar': R package for quantile regression outlier diagnostic |
| Package source: | quokar_0.1.0.tar.gz |
| Windows binaries: | r-devel: quokar_0.1.0.zip, r-release: quokar_0.1.0.zip, r-oldrel: quokar_0.1.0.zip |
| macOS binaries: | r-release: quokar_0.1.0.tgz, r-oldrel: quokar_0.1.0.tgz |
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