R package PMA: Penalized Multivariate Analysis. Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.

References in zbMATH (referenced in 120 articles )

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  1. Cai, Tony; Li, Hongzhe; Ma, Rong: Optimal structured principal subspace estimation: metric entropy and minimax rates (2021)
  2. Guo, Wenxing; Balakrishnan, Narayanaswamy; Bian, Mengjie: Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model (2021)
  3. Hu, Jian; Li, Mingyao: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  4. Langworthy, Benjamin W.; Stephens, Rebecca L.; Gilmore, John H.; Fine, Jason P.: Canonical correlation analysis for elliptical copulas (2021)
  5. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  6. Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
  7. Cai, Jia; Huo, Junyi: Sparse generalized canonical correlation analysis via linearized Bregman method (2020)
  8. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  9. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  10. Liu, Hongying; Wang, Hao; Song, Mengmeng: Projections onto the intersection of a one-norm ball or sphere and a two-norm ball or sphere (2020)
  11. Malec, Lukáš; Janovský, Vladimír: Connecting the multivariate partial least squares with canonical analysis: a path-following approach (2020)
  12. Ma, Zhuang; Li, Xiaodong: Subspace perspective on canonical correlation analysis: dimension reduction and minimax rates (2020)
  13. Mukhopadhyay, Minerva; Dunson, David B.: Targeted random projection for prediction from high-dimensional features (2020)
  14. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  15. Wang, Yiju; Dong, Manman; Xu, Yi: A sparse rank-1 approximation algorithm for high-order tensors (2020)
  16. Xia, Yin; Li, Lexin; Lockhart, Samuel N.; Jagust, William J.: Simultaneous covariance inference for multimodal integrative analysis (2020)
  17. Xiu, Xianchao; Yang, Ying; Kong, Lingchen; Liu, Wanquan: tSSNALM: a fast two-stage semi-smooth Newton augmented Lagrangian method for sparse CCA (2020)
  18. Zhang, Fan; Miecznikowski, Jeffrey C.; Tritchler, David L.: Identification of supervised and sparse functional genomic pathways (2020)
  19. Zhang, Fan; Wang, Hao; Wang, Jiashan; Yang, Kai: Inexact primal-dual gradient projection methods for nonlinear optimization on convex set (2020)
  20. Zhao, Yi; Lindquist, Martin A.; Caffo, Brian S.: Sparse principal component based high-dimensional mediation analysis (2020)

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