RRRR: Online Robust Reduced-Rank Regression Estimation

Methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>. The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>. The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.

Version:	1.1.1
Imports:	matrixcalc, expm, ggplot2, magrittr, mvtnorm, stats
Suggests:	lazybar, knitr, rmarkdown
Published:	2023-02-24
DOI:	10.32614/CRAN.package.RRRR
Author:	Yangzhuoran Fin Yang [aut, cre], Ziping Zhao [aut]
Maintainer:	Yangzhuoran Fin Yang <yangyangzhuoran at gmail.com>
BugReports:	https://github.com/FinYang/RRRR/issues/
License:	GPL-3
URL:	https://pkg.yangzhuoranyang.com/RRRR/, https://github.com/FinYang/RRRR
NeedsCompilation:	no
Language:	en-AU
Materials:	README NEWS
CRAN checks:	RRRR results

Documentation:

Reference manual:	RRRR.pdf
Vignettes:	Introduction to RRRR

Downloads:

Package source:	RRRR_1.1.1.tar.gz
Windows binaries:	r-devel: RRRR_1.1.1.zip, r-release: RRRR_1.1.1.zip, r-oldrel: RRRR_1.1.1.zip
macOS binaries:	r-release (arm64): RRRR_1.1.1.tgz, r-oldrel (arm64): RRRR_1.1.1.tgz, r-release (x86_64): RRRR_1.1.1.tgz, r-oldrel (x86_64): RRRR_1.1.1.tgz
Old sources:	RRRR archive

Linking:

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