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: 0.3.3
Depends: R (≥ 3.5.0)
Suggests: knitr, rmarkdown
Published: 2021-10-05
Author: Shixiang Chen [aut], Justin Huang [aut], Benjamin Jochem [aut], Shiqian Ma [aut], Lingzhou Xue [cre, aut], Hui Zou [aut]
Maintainer: Lingzhou Xue <lzxue at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: amanpg results


Reference manual: amanpg.pdf
Vignettes: An Introduction to amanpg


Package source: amanpg_0.3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): amanpg_0.3.3.tgz, r-release (x86_64): amanpg_0.3.3.tgz, r-oldrel: amanpg_0.3.3.tgz
Old sources: amanpg archive


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