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 working variance functions, together with a lightweight cluster-aware working-covariance extension, 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>.

Version:	0.0.2
Depends:	R (≥ 4.1.0)
Imports:	graphics, grDevices, stats
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-05-27
DOI:	10.32614/CRAN.package.inferMM
Author:	Mijeong Kim [aut, cre], Minkyoung Cha [aut], Ah Young Jeong [aut]
Maintainer:	Mijeong Kim <m.kim at ewha.ac.kr>
License:	GPL-3
NeedsCompilation:	no
Citation:	inferMM citation info
Materials:	README, NEWS
CRAN checks:	inferMM results

Documentation:

Reference manual:	inferMM.html , inferMM.pdf
Vignettes:	A Workflow for Variance-Aware Michaelis-Menten Analysis (source, R code)

Downloads:

Package source:	inferMM_0.0.2.tar.gz
Windows binaries:	r-devel: inferMM_0.0.2.zip, r-release: inferMM_0.0.2.zip, r-oldrel: inferMM_0.0.2.zip
macOS binaries:	r-release (arm64): inferMM_0.0.2.tgz, r-oldrel (arm64): inferMM_0.0.2.tgz, r-release (x86_64): inferMM_0.0.2.tgz, r-oldrel (x86_64): inferMM_0.0.2.tgz

Linking:

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