ddpca

Description

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

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Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’

The Description field contains
  <arXiv:1906.00051>) by decomposing a large covariance matrix into a
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’

The Description field contains
  <arXiv:1906.00051>) by decomposing a large covariance matrix into a
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’

The Description field contains
  <arXiv:1906.00051>) by decomposing a large covariance matrix into a
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’

The Description field contains
  <arXiv:1906.00051>) by decomposing a large covariance matrix into a
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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