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

Downloads:

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
OS X El Capitan binaries: r-release: quokar_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: quokar_0.1.0.tgz

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