Package: RTFA 0.1.0

Lingxiao Li

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) <arxiv:2206.09800>, and Barigozzi et al. (2023) <arxiv:2303.18163>.

Authors:Matteo Barigozzi [aut], Yong He [aut], Lorenzo Trapani [aut], Lingxiao Li [aut, cre]

RTFA_0.1.0.tar.gz
RTFA_0.1.0.tar.gz(r-4.7-any)RTFA_0.1.0.tar.gz(r-4.6-any)
RTFA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
RTFA/json (API)

# Install 'RTFA' in R:
install.packages('RTFA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 318 downloads 2 exports 2 dependencies

Last updated from:0af62f2afa. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK107
source / vignettesOK175
linux-release-x86_64OK119
wasm-releaseOK114

Exports:TFM_estTFM_FN

Dependencies:rTensortensor