quokar: Quantile Regression Outlier Diagnostics with K Left Out Analysis
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 |
Documentation:
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