SensitiveQuestions/endorse: Bayesian Measurement Models for Analyzing Endorsement Experiments
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments. Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions. This methodology is helpful when measuring support for socially sensitive political actors such as militant groups. The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Getting started
Browse package contents
Package details |
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| Author | Yuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb] |
| Maintainer | Yuki Shiraito <shiraito@umich.edu> |
| License | GPL (>=2) |
| Version | 1.6.2 |
| URL | https://github.com/SensitiveQuestions/endorse/ |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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