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  "Title": "Computation of Sparse Eigenvectors of a Matrix",
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  "Description": "Computation of sparse eigenvectors of a matrix (aka sparse\nPCA) with running time 2-3 orders of magnitude lower than\nexisting methods and better final performance in terms of\nrecovery of sparsity pattern and estimation of numerical\nvalues. Can handle covariance matrices as well as data matrices\nwith real or complex-valued entries. Different levels of\nsparsity can be specified for each individual ordered\neigenvector and the method is robust in parameter selection.\nSee vignette for a detailed documentation and comparison, with\nseveral illustrative examples. The package is based on the\npaper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar,\n\"Orthogonal Sparse PCA and Covariance Estimation via Procrustes\nReformulation,\" IEEE Transactions on Signal Processing, IEEE\nTrans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226,\nDec. 2016. <doi:10.1109/TSP.2016.2605073>.",
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  "Maintainer": "Daniel P. Palomar <daniel.p.palomar@gmail.com>",
  "URL": "https://github.com/dppalomar/sparseEigen,\nhttps://www.danielppalomar.com,\nhttps://doi.org/10.1109/TSP.2016.2605073",
  "BugReports": "https://github.com/dppalomar/sparseEigen/issues",
  "License": "GPL-3 | file LICENSE",
  "Encoding": "UTF-8",
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  "Repository": "https://dppalomar.r-universe.dev",
  "Date/Publication": "2018-12-22 15:14:38 UTC",
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      "title": "sparseEigen: Computation of Sparse Eigenvectors of a Matrix",
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      "title": "Sparse Spectral Decomposition of a Matrix",
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      "title": "Computing Sparse Eigenvectors of a Matrix",
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        "Comparison with other packages",
        "Usage of the package",
        "Computation of sparse eigenvectors of a given matrix",
        "Covariance matrix estimation with sparse eigenvectors",
        "Complex-valued inputs",
        "Explanation of the algorithms",
        "spEigen(): Sparse eigenvectors from a given covariance matrix",
        "spEigenCov(): Covariance matrix estimation with sparse eigenvectors",
        "References"
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