amanpg: Alternating Manifold Proximal Gradient Method for Sparse PCA
Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides a novel algorithm for solving the sparse principal component analysis problem which provides advantages over existing methods in terms of efficiency and convergence guarantees. Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>. Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>. Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.
| Version: | 0.3.4 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | knitr,rmarkdown |
| Published: | 2022-10-02 |
| DOI: | 10.32614/CRAN.package.amanpg |
| Author: | Shixiang Chen [aut], Justin Huang [aut], Benjamin Jochem [aut], Shiqian Ma [aut], Haichuan Xu [aut], Lingzhou Xue [aut], Zhong Zheng [cre, aut], Hui Zou [aut] |
| Maintainer: | Zhong Zheng <zvz5337 at psu.edu> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | amanpg results |
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