Package: RTFA
Type: Package
Title: Robust Factor Analysis for Tensor Time Series
Version: 0.1.0
Authors@R: c(person("Matteo", "Barigozzi", role="aut"),
    person("Yong", "He", role = "aut"),
    person("Lorenzo", "Trapani", role = "aut"),
    person("Lingxiao", "Li", role = c("aut", "cre"), email = "lilingxiao@mail.sdu.edu.cn"))
Author: Matteo Barigozzi [aut],
  Yong He [aut],
  Lorenzo Trapani [aut],
  Lingxiao Li [aut, cre]
Maintainer: Lingxiao Li <lilingxiao@mail.sdu.edu.cn>
Description: 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>.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: rTensor, tensor
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2023-04-07 09:27:09 UTC; 李凌霄
Repository: CRAN
Date/Publication: 2023-04-10 14:00:05 UTC
Built: R 4.6.0; ; 2026-04-25 22:04:52 UTC; unix
