inferMM: Variance-Aware Michaelis-Menten Estimation and Inference

Variance-aware Michaelis-Menten estimation, model screening, grouped enzyme-kinetic analyses, and clustered repeated-measurement workflows. The package implements profile-score estimators under built-in or user-defined fixed working variance functions for single curves, optional higher-moment estimating equations for independent observations with asymmetric errors, and a lightweight cluster-aware working-covariance extension with optional conditional projected scores for an unspecified random-effect density, Wald and bootstrap confidence intervals, prediction utilities, and simulation helpers. Related methodology is discussed by Kim and Ma (2012) <doi:10.1007/s10463-011-0332-y>, Kim (2023) <doi:10.1002/sta4.606>, and Ma and Genton (2010) <doi:10.1111/j.1467-9868.2010.00741.x>.

install.packages("inferMM")

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