c(
person("Shixiang", "Chen", role = "aut"),
person("Justin", "Huang", role = "aut"),
person("Benjamin","Jochem", role = "aut"),
person("Shiqian", "Ma", role = "aut"),
person("Lingzhou", "Xue", email = "lzxue@psu.edu", role = c("cre","aut")),
person("Hui", "Zou", role = "aut"))
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>.
Shixiang Chen [aut],
Justin Huang [aut],
Benjamin Jochem [aut],
Shiqian Ma [aut],
Lingzhou Xue [cre, aut],
Hui Zou [aut]

c(
person("Justin", "Huang", role = "aut"),
person("Benjamin","Jochem", role = "aut"),
person("Shiqian", "Ma", role = "aut"),
person("Lingzhou", "Xue", email = "lzxue@psu.edu", role = c("cre","aut")),
person("Hui", "Zou", role = "aut"))
Justin Huang [aut],
Benjamin Jochem [aut],
Shiqian Ma [aut],
Lingzhou Xue [cre, aut],
Hui Zou [aut]

c(
person("Shixiang", "Chen", role = "aut"),
person("Justin", "Huang", role = "aut"),
person("Benjamin","Jochem", role = "aut"),
person("Shiqian", "Ma", role = "aut"),
person("Haichuan", "Xu", role = "aut"),
person("Lingzhou", "Xue", role = "aut"),
person("Zhong", "Zheng", email = "zvz5337@psu.edu", role = c("cre","aut")),
person("Hui", "Zou", role = "aut"))
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]

Hierarchical Clustering and Principal
Component Analysis (With general and robusts
methods).

Parallelized Hierarchical Clustering and Principal
Component Analysis (With general and robusts
methods).