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  "Title": "Forecastable Component Analysis",
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  "Description": "Implementation of Forecastable Component Analysis\n('ForeCA'), including main algorithms and auxiliary function\n(summary, plotting, etc.) to apply 'ForeCA' to multivariate\ntime series data. 'ForeCA' is a novel dimension reduction (DR)\ntechnique for temporally dependent signals. Contrary to other\npopular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time\ndependency explicitly into account and searches for the most\n''forecastable'' signal. The measure of forecastability is\nbased on the Shannon entropy of the spectral density of the\ntransformed signal.",
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    "foreca.one_weightvector",
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    "initialize_weightvector",
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    "mvspectrum",
    "mvspectrum2wcov",
    "normalize_mvspectrum",
    "Omega",
    "quadratic_form",
    "sfa",
    "spectral_entropy",
    "spectrum_of_linear_combination",
    "sqrt_matrix",
    "weightvector2entropy_wcov",
    "whiten"
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      "page": "ForeCA-package",
      "title": "Implementation of Forecastable Component Analysis (ForeCA)",
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        "ForeCA-package",
        "ForeCA"
      ]
    },
    {
      "page": "common-arguments",
      "title": "List of common arguments",
      "topics": [
        "common-arguments"
      ]
    },
    {
      "page": "complete-controls",
      "title": "Completes several control settings",
      "topics": [
        "complete-controls",
        "complete_algorithm_control",
        "complete_entropy_control",
        "complete_spectrum_control"
      ]
    },
    {
      "page": "continuous_entropy",
      "title": "Shannon entropy for a continuous pdf",
      "topics": [
        "continuous_entropy"
      ]
    },
    {
      "page": "discrete_entropy",
      "title": "Shannon entropy for discrete pmf",
      "topics": [
        "discrete_entropy"
      ]
    },
    {
      "page": "foreca",
      "title": "Forecastable Component Analysis",
      "topics": [
        "foreca",
        "foreca.multiple_weightvectors",
        "foreca.one_weightvector"
      ]
    },
    {
      "page": "foreca-utils",
      "title": "Plot, summary, and print methods for class 'foreca'",
      "topics": [
        "biplot.foreca",
        "foreca-utils",
        "plot.foreca",
        "print.foreca",
        "summary.foreca"
      ]
    },
    {
      "page": "foreca.EM-aux",
      "title": "ForeCA EM auxiliary functions",
      "topics": [
        "foreca.EM-aux",
        "foreca.EM.E_and_M_step",
        "foreca.EM.E_step",
        "foreca.EM.h",
        "foreca.EM.M_step"
      ]
    },
    {
      "page": "foreca.EM.one_weightvector",
      "title": "EM-like algorithm to estimate optimal ForeCA transformation",
      "topics": [
        "foreca.EM.one_weightvector"
      ]
    },
    {
      "page": "foreca.one_weightvector-utils",
      "title": "Plot, summary, and print methods for class 'foreca.one_weightvector'",
      "topics": [
        "foreca.one_weightvector-utils",
        "plot.foreca.one_weightvector",
        "summary.foreca.one_weightvector"
      ]
    },
    {
      "page": "initialize_weightvector",
      "title": "Initialize weightvector for iterative ForeCA algorithms",
      "topics": [
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      ]
    },
    {
      "page": "mvspectrum",
      "title": "Estimates spectrum of multivariate time series",
      "topics": [
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        "get_spectrum_from_mvspectrum",
        "mvpgram",
        "mvspectrum",
        "normalize_mvspectrum",
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        "plot.mvspectrum"
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      "title": "whitens multivariate data",
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