  Randomized response (RR) designs are used to collect response data about
sensitive behaviors (e.g., criminal behavior, sexual desires). The modeling of
RR data is more complex, since it requires a description of the RR process. For
the class of generalized linear mixed models (GLMMs), the RR process can be
represented by an adjusted link function, which relates the expected RR to the
linear predictor, for most common RR designs. The package GLMMRR includes
modified link functions for four different cumulative distributions (i.e.,
logistic, cumulative normal, gumbel, cauchy) for GLMs and GLMMs, where the
package lme4 facilitates ML and REML estimation. The mixed modeling framework
in GLMMRR can be used to jointly analyse data collected under different designs
(e.g., dual questioning, multilevel, mixed mode, repeated measurements designs,
multiple-group designs). The well-known features of the GLM and GLMM (package
lme4) software are remained, while adding new model-fit tests, residual
analyses, and plot functions to give support to a profound RR data analysis.
Data of H\"{o}glinger and Jann (2018) and H\"{o}glinger, Jann, and Diekmann
(2014) is used to illustrate the methodology and software.
