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  "Title": "Variance-Aware Michaelis-Menten Estimation and Inference",
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  "Description": "Variance-aware Michaelis-Menten estimation, model\nscreening, grouped enzyme-kinetic analyses, and clustered\nrepeated-measurement workflows. The package implements\nprofile-score estimators under working variance functions,\ntogether with a lightweight cluster-aware working-covariance\nextension, Wald and bootstrap confidence intervals, prediction\nutilities, and simulation helpers. Related methodology is\ndiscussed by Kim and Ma (2012) <doi:10.1007/s10463-011-0332-y>,\nKim (2023) <doi:10.1002/sta4.606>, and Ma and Genton (2010)\n<doi:10.1111/j.1467-9868.2010.00741.x>.",
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  "Author": "Mijeong Kim [aut, cre], Minkyoung Cha [aut], Ah Young Jeong\n[aut]",
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    "variance_function"
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      "title": "inferMM: Variance-Aware Michaelis-Menten Estimation and Inference",
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        "inferMM-package",
        "inferMM"
      ]
    },
    {
      "page": "alves_demo",
      "title": "Subset of the Alves Soil Enzyme Kinetics Data",
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    {
      "page": "cluster_mm",
      "title": "Fit a Cluster-Aware Michaelis-Menten Model",
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        "coef.cluster_mm",
        "confint.cluster_mm",
        "plot.cluster_mm",
        "predict.cluster_mm",
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        "print.summary.cluster_mm",
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        "coef.fit_mm",
        "confint.fit_mm",
        "fit_mm",
        "plot.fit_mm",
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        "print.group_mm"
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