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 ORCID iD [aut, cre], Ziping Zhao ORCID iD [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


Reference manual: RRRR.pdf
Vignettes: Introduction to RRRR


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


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