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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

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:

Please use the canonical form https://CRAN.R-project.org/package=RRRR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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