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  "Title": "Semi-Supervised Learning with Mixed Missingness in Finite\nMixture Models",
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  "Description": "Semi-supervised Gaussian finite mixture models for\npartially labelled data under complete-case, missing completely\nat random (MCAR), entropy-dependent missing at random (MAR),\nand mixed MCAR/MAR label-missingness formulations. For the\nmixed formulation, the source of a missing label may be\nobserved or latent. The package supports equal and\ncomponent-specific covariance matrices, model fitting,\nsimulation, initialization, prediction, classification\nperformance assessment, and entropy-based diagnostics. A\nsemi-synthetic Blood Transfusion data set is included to\nillustrate the applied workflow.",
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      "title": "SSLfmm: Semi-Supervised Finite Mixture Models with Mixed Label Missingness",
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        "SSLfmm-package",
        "SSLfmm"
      ]
    },
    {
      "page": "blood_transfusion",
      "title": "Semi-Synthetic Blood Transfusion Data",
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      ]
    },
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      "title": "Classification Performance Metrics",
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    },
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