GLMMRR: Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data

Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.

Version: 0.5.0
Depends: R (≥ 3.5.0), lme4, methods
Imports: lattice, stats, utils, grDevices, RColorBrewer
Published: 2021-01-13
DOI: 10.32614/CRAN.package.GLMMRR
Author: Jean-Paul Fox [aut], Konrad Klotzke [aut], Duco Veen [aut]
Maintainer: Konrad Klotzke < at>
License: GPL-3
NeedsCompilation: no
In views: MixedModels, Psychometrics
CRAN checks: GLMMRR results


Reference manual: GLMMRR.pdf


Package source: GLMMRR_0.5.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GLMMRR_0.5.0.tgz, r-oldrel (arm64): GLMMRR_0.5.0.tgz, r-release (x86_64): GLMMRR_0.5.0.tgz, r-oldrel (x86_64): GLMMRR_0.5.0.tgz
Old sources: GLMMRR archive


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