The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

RTFA: Robust Factor Analysis for Tensor Time Series

Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <doi:10.48550/arXiv.2206.09800>, and Barigozzi et al. (2023) <doi:10.48550/arXiv.2303.18163>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: rTensor, tensor
Published: 2023-04-10
DOI: 10.32614/CRAN.package.RTFA
Author: Matteo Barigozzi [aut], Yong He [aut], Lorenzo Trapani [aut], Lingxiao Li [aut, cre]
Maintainer: Lingxiao Li <lilingxiao at mail.sdu.edu.cn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: TimeSeries
CRAN checks: RTFA results

Documentation:

Reference manual: RTFA.pdf

Downloads:

Package source: RTFA_0.1.0.tar.gz
Windows binaries: r-devel: RTFA_0.1.0.zip, r-release: RTFA_0.1.0.zip, r-oldrel: RTFA_0.1.0.zip
macOS binaries: r-release (arm64): RTFA_0.1.0.tgz, r-oldrel (arm64): RTFA_0.1.0.tgz, r-release (x86_64): RTFA_0.1.0.tgz, r-oldrel (x86_64): RTFA_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RTFA 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.
Health stats visible at Monitor.