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  "Title": "Low-Rank Decomposition of Brain Connectivity Matrices with\nUniform Sparsity",
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  "Description": "To decompose symmetric matrices such as brain connectivity\nmatrices so that one can extract sparse latent component\nmatrices and also estimate mixing coefficients, a blind source\nseparation (BSS) method named LOCUS was proposed in Wang and\nGuo (2023) <arXiv:2008.08915>. For brain connectivity matrices,\nthe outputs correspond to sparse latent connectivity traits and\nindividual-level trait loadings.",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-04 06:56:21 UTC",
    "User": "root"
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  "Author": "Yikai Wang [aut, cph], Jialu Ran [aut, cre], Ying Guo [aut,\nths]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-10-04 06:20:05 UTC",
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      "title": "LOCUS: Low-rank decomposition of brain connectivity matrices with uniform sparsity",
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        "LOCUS"
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