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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.
| Version: | 1.6.2 |
| Depends: | coda, utils |
| Published: | 2022-05-02 |
| Author: | Yuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb] |
| Maintainer: | Yuki Shiraito <shiraito at umich.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/SensitiveQuestions/endorse/ |
| NeedsCompilation: | yes |
| Materials: | README ChangeLog |
| CRAN checks: | endorse results |
| Reference manual: | endorse.pdf |
| Package source: | endorse_1.6.2.tar.gz |
| Windows binaries: | r-devel: endorse_1.6.2.zip, r-release: endorse_1.6.2.zip, r-oldrel: endorse_1.6.2.zip |
| macOS binaries: | r-release (arm64): endorse_1.6.2.tgz, r-oldrel (arm64): endorse_1.6.2.tgz, r-release (x86_64): endorse_1.6.2.tgz, r-oldrel (x86_64): endorse_1.6.2.tgz |
| Old sources: | endorse archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.