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  "Title": "Distance-Correlation Based Methods for Blind Source Separation\nand Dependence Analysis",
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  "Description": "Independent component analysis based on distance\ncorrelation, including a robust variant using the bowl\ntransformation. The package provides user-facing\nimplementations of distance covariance and distance\ncorrelation, including memory-efficient blockwise computations\nfor large data sets. It includes a sequential ICA estimator\nbased on minimizing distance correlation, as well as tools for\nanalyzing serial dependence via distance autocorrelation,\ndependograms, and permutation-based tests. In addition, it\nprovides functions for testing serial dependence based on\ndistance correlation and the Hilbert–Schmidt independence\ncriterion. The methodology is related to Matteson and Tsay\n(2017) <doi:10.1080/01621459.2016.1150851> and to the robust\nframework of Leyder et al. (2026)\n<doi:10.1007/s11634-026-00674-9>.",
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  "Author": "Sarah Leyder [aut] (ORCID:\n<https://orcid.org/0000-0002-0828-0399>), Klaus Nordhausen\n[aut, cre] (ORCID: <https://orcid.org/0000-0002-3758-8501>)",
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    "dacov_large",
    "dacov2_large",
    "dcor_large",
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      "title": "Biloop Transformation",
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        "dacov_large"
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    {
      "page": "dcorICA",
      "title": "Independent Component Analysis via Distance Correlation",
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      ]
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      "title": "Distance covariance with optional blockwise computation",
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        "dcov_large"
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      "title": "Test Serial Dependence Using HSIC",
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    {
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      "title": "Normalized Hilbert-Schmidt Independence Criterion",
      "topics": [
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    },
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        "sdt"
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        "Measuring dependence with distance correlation",
        "Robust transformations",
        "Independent component analysis with dcorICA()",
        "Serial dependence diagnostics",
        "Normalized HSIC",
        "Overview over Functions",
        "References"
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