logistic4p: Logistic Regression with Misclassification in Dependent Variables

Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.

Version: 1.6
Depends: R (≥ 2.10), MASS
Published: 2023-10-21
DOI: 10.32614/CRAN.package.logistic4p
Author: Haiyan Liu and Zhiyong Zhang
Maintainer: Zhiyong Zhang <johnnyzhz at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: logistic4p results


Reference manual: logistic4p.pdf


Package source: logistic4p_1.6.tar.gz
Windows binaries: r-devel: logistic4p_1.6.zip, r-release: logistic4p_1.6.zip, r-oldrel: logistic4p_1.6.zip
macOS binaries: r-release (arm64): logistic4p_1.6.tgz, r-oldrel (arm64): logistic4p_1.6.tgz, r-release (x86_64): logistic4p_1.6.tgz, r-oldrel (x86_64): logistic4p_1.6.tgz
Old sources: logistic4p archive


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