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ememax: Estimation for Binary Emax Models with Missing Responses and Bias Reduction

Provides estimation utilities for binary Emax dose-response models. Includes Expectation-Maximization based maximum likelihood estimation when the binary response is missing, as well as bias-reduced estimators including Jeffreys-penalized likelihood, Firth-score, and Cox-Snell corrections.The methodology is described in Zhang, Pradhan, and Zhao (2025) <doi:10.1177/09622802251403356> and Zhang, Pradhan, and Zhao (2026) <doi:10.1080/10543406.2026.2627387>.

Version: 0.1.0
Depends: R (≥ 4.0.0), clinDR (≥ 2.5.2)
Imports: BB, brglm, boot, formula.tools, MASS, maxLik, numDeriv, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-03-17
DOI: 10.32614/CRAN.package.ememax
Author: Jiangshan Zhang [aut, cre], Vivek Pradhan [aut], Yuxi Zhao [aut]
Maintainer: Jiangshan Zhang <jiszhang at ucdavis.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: ememax results

Documentation:

Reference manual: ememax.html , ememax.pdf

Downloads:

Package source: ememax_0.1.0.tar.gz
Windows binaries: r-devel: ememax_0.1.0.zip, r-release: ememax_0.1.0.zip, r-oldrel: ememax_0.1.0.zip
macOS binaries: r-release (arm64): ememax_0.1.0.tgz, r-oldrel (arm64): ememax_0.1.0.tgz, r-release (x86_64): ememax_0.1.0.tgz, r-oldrel (x86_64): ememax_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.