IALS: Iterative Alternating Least Square Estimation for Large-Dimensional Matrix Factor Model

The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <doi:10.48550/arXiv.2301.00360>.

Version: 0.1.3
Depends: R (≥ 4.0)
Imports: RSpectra, pracma, HDMFA
Published: 2024-02-16
Author: Yong He [aut], Ran Zhao [aut, cre], Wen-Xin Zhou [aut]
Maintainer: Ran Zhao <Zhaoran at mail.sdu.edu.cn>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: IALS results


Reference manual: IALS.pdf


Package source: IALS_0.1.3.tar.gz
Windows binaries: r-prerel: IALS_0.1.3.zip, r-release: IALS_0.1.3.zip, r-oldrel: IALS_0.1.3.zip
macOS binaries: r-prerel (arm64): IALS_0.1.3.tgz, r-release (arm64): IALS_0.1.3.tgz, r-oldrel (arm64): IALS_0.1.3.tgz, r-prerel (x86_64): IALS_0.1.3.tgz, r-release (x86_64): IALS_0.1.3.tgz
Old sources: IALS archive


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