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
In views:	MixedModels, Psychometrics
CRAN checks:	GLMMRR results

Documentation:

Reference manual:

GLMMRR.html , GLMMRR.pdf

Downloads:

Package source:	GLMMRR_0.6.0.tar.gz
Windows binaries:	r-devel: GLMMRR_0.6.0.zip, r-release: GLMMRR_0.6.0.zip, r-oldrel: GLMMRR_0.6.0.zip
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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