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. .


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

For further information on the project see the accompying research paper and the CRAN repository.

Reference manual

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install.packages("GLMMRR")

0.6.0 by Jean-Paul Fox, a year ago


Browse source code at https://github.com/cran/GLMMRR


Authors: Jean-Paul Fox [aut, cre] , Konrad Klotzke [aut] , Duco Veen [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports lattice, stats, utils, grDevices, RColorBrewer

Depends on lme4, methods


See at CRAN