QICD: Estimate the Coefficients for Non-Convex Penalized Quantile Regression Model by using QICD Algorithm

Extremely fast algorithm "QICD", Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. This algorithm combines the coordinate descent algorithm in the inner iteration with the majorization minimization step in the outside step. For each inner univariate minimization problem, we only need to compute a one-dimensional weighted median, which ensures fast computation. Tuning parameter selection is based on two different method: the cross validation and BIC for quantile regression model. Details are described in Peng,B and Wang,L. (2015) <doi:10.1080/10618600.2014.913516>.

Version: 1.2.0
Depends: R (≥ 3.0.0)
Imports: stats, parallel
Suggests: mvtnorm, knitr
Published: 2017-04-18
Author: Bo Peng [aut, cre], Rondall E. Jones [ctb], John A. Wisniewski [ctb]
Maintainer: Bo Peng <peng0199 at umn.edu>
License: GPL-2
URL: http://www.tandfonline.com/doi/abs/10.1080/10618600.2014.913516#.VsoK-JMrJp8
NeedsCompilation: yes
Materials: README
CRAN checks: QICD results


Reference manual: QICD.pdf
Vignettes: vignette
Package source: QICD_1.2.0.tar.gz
Windows binaries: r-devel: QICD_1.2.0.zip, r-release: QICD_1.2.0.zip, r-oldrel: QICD_1.2.0.zip
OS X binaries: r-release: QICD_1.2.0.tgz, r-oldrel: QICD_1.2.0.tgz
Old sources: QICD archive


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