Alternating Manifold Proximal Gradient Method for Sparse PCA R package amanpg version 0.3.4 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>. Software Depends: R (>= 3.5.0) Suggests: knitr, rmarkdown Zhong Zheng <zvz5337@psu.edu> Comprehensive R Archive Network (CRAN) 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] MIT + file LICENSE (https://CRAN.R-project.org/package=amanpg/LICENSE) 2022-10-02 application/tgz https://CRAN.R-project.org/package=amanpg doi:10.32614/CRAN.package.amanpg