rrat: Robust Regression with Asymmetric Heavy-Tail Noise Distributions

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Version:	1.0.0
Depends:	R (≥ 2.10), quantreg
Published:	2019-10-07
Author:	Yi He and Yuelin Zhao
Maintainer:	Yi He <yi.he at stats.oxon.org>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	rrat results

Documentation:

Reference manual:

rrat.pdf

Downloads:

Package source:	rrat_1.0.0.tar.gz
Windows binaries:	r-devel: rrat_1.0.0.zip, r-release: rrat_1.0.0.zip, r-oldrel: rrat_1.0.0.zip
macOS binaries:	r-release (arm64): rrat_1.0.0.tgz, r-oldrel (arm64): rrat_1.0.0.tgz, r-release (x86_64): rrat_1.0.0.tgz, r-oldrel (x86_64): rrat_1.0.0.tgz

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