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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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      "title": "inferMM: Variance-Aware Michaelis-Menten Estimation and Inference",
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        "inferMM"
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    {
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