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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.6.0 |
rolling source/ R- | GLMMRR_0.6.0.tar.gz |
207.0 KiB |
0.6.0 |
rolling linux/jammy R-4.5 | GLMMRR_0.6.0.tar.gz |
625.5 KiB |
0.6.0 |
rolling linux/noble R-4.5 | GLMMRR_0.6.0.tar.gz |
625.6 KiB |
0.6.0 |
latest source/ R- | GLMMRR_0.6.0.tar.gz |
207.0 KiB |
0.6.0 |
latest linux/jammy R-4.5 | GLMMRR_0.6.0.tar.gz |
625.5 KiB |
0.6.0 |
latest linux/noble R-4.5 | GLMMRR_0.6.0.tar.gz |
625.6 KiB |
0.6.0 |
2026-04-23 source/ R- | GLMMRR_0.6.0.tar.gz |
207.0 KiB |
0.6.0 |
2026-04-09 windows/windows R-4.5 | GLMMRR_0.6.0.zip |
628.2 KiB |
0.5.0 |
2025-04-20 source/ R- | GLMMRR_0.5.0.tar.gz |
208.8 KiB |