glmMisrep: Generalized Linear Models Adjusting for Misrepresentation

Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <>.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: MASS, poisson.glm.mix, stats
Published: 2024-04-18
DOI: 10.32614/CRAN.package.glmMisrep
Author: Patrick Rafael [cre, aut], Xia Michelle [aut], Rexford Akakpo [aut]
Maintainer: Patrick Rafael <pbr2608 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: glmMisrep results


Reference manual: glmMisrep.pdf


Package source: glmMisrep_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmMisrep_0.1.1.tgz, r-oldrel (arm64): glmMisrep_0.1.1.tgz, r-release (x86_64): glmMisrep_0.1.1.tgz, r-oldrel (x86_64): glmMisrep_0.1.1.tgz
Old sources: glmMisrep archive


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