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
DOI:	10.32614/CRAN.package.IALS
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

Documentation:

Reference manual:

IALS.pdf

Downloads:

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

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