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.
- Version0.5.0
- R version≥ 3.5.0
- LicenseGPL-3
- Needs compilation?No
- Last release01/13/2021
Documentation
Team
Konrad Klotzke
Jean-Paul Fox
Show author detailsRolesAuthorDuco Veen
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 191 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,865 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Apr 10, 2024 with 53 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports2 packages