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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-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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