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: 0.6.0
Depends: R (≥ 3.5.0), lme4, methods
Imports: lattice, stats, utils, grDevices, RColorBrewer
Published: 2025-09-18
DOI: 10.32614/CRAN.package.GLMMRR
Author: Jean-Paul Fox [aut, cre], Konrad Klotzke [aut], Duco Veen [aut]
Maintainer: Jean-Paul Fox <jpfox00 at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: GLMMRR results

Documentation:

Reference manual: GLMMRR.html , GLMMRR.pdf

Downloads:

Package source: GLMMRR_0.6.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): GLMMRR_0.6.0.tgz, r-oldrel (arm64): GLMMRR_0.6.0.tgz, r-release (x86_64): GLMMRR_0.6.0.tgz, r-oldrel (x86_64): GLMMRR_0.6.0.tgz
Old sources: GLMMRR archive

Linking:

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