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  "Title": "Mixture-of-Experts Wishart Models for Covariance Data",
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  "Description": "Methods for maximum likelihood and Bayesian estimation for\nthe Wishart mixture model and the mixture-of-experts Wishart\n(MoE-Wishart) model. The package provides four inference\nalgorithms for these models, each implemented using the\nexpectation–maximization (EM) algorithm for maximum likelihood\nestimation and a fully Bayesian approach via\nGibbs-within-Metropolis–Hastings sampling.",
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    "moewishart",
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    "sampleIW",
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      "title": "CTRP drug response covariances data",
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      "page": "computeIC",
      "title": "Information criteria for Wishart mixtures and MoE models",
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      ]
    },
    {
      "page": "CTRP",
      "title": "CTRP drug response covariances data",
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      ]
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      "title": "density of Wishart distribution",
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      "title": "Log multivariate gamma function",
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      "title": "EM/Bayesian estimation for Wishart mixture model",
      "topics": [
        "mixturewishart"
      ]
    },
    {
      "page": "moewishart",
      "title": "EM/Bayesian estimation for Wishart MoE model",
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        "moewishart"
      ]
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      ]
    },
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      ]
    },
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      "title": "Fast sampler for the inverse-Wishart distribution",
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      ]
    },
    {
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      ]
    }
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        "3. Working model: MoE-Wishart model via EM algorithm",
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