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) <arXiv:2301.00360>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling source/ R- | IALS_0.1.3.tar.gz |
4.6 KiB |
0.1.3 |
rolling linux/jammy R-4.5 | IALS_0.1.3.tar.gz |
27.9 KiB |
0.1.3 |
rolling linux/noble R-4.5 | IALS_0.1.3.tar.gz |
27.7 KiB |
0.1.3 |
latest source/ R- | IALS_0.1.3.tar.gz |
4.6 KiB |
0.1.3 |
latest linux/jammy R-4.5 | IALS_0.1.3.tar.gz |
27.9 KiB |
0.1.3 |
latest linux/noble R-4.5 | IALS_0.1.3.tar.gz |
27.7 KiB |
0.1.3 |
2026-04-26 source/ R- | IALS_0.1.3.tar.gz |
4.6 KiB |
0.1.3 |
2026-04-23 source/ R- | IALS_0.1.3.tar.gz |
4.6 KiB |
0.1.3 |
2026-04-09 windows/windows R-4.5 | IALS_0.1.3.zip |
30.3 KiB |
0.1.3 |
2025-04-20 source/ R- | IALS_0.1.3.tar.gz |
4.6 KiB |