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.

Version: 1.6.2
Depends: coda, utils
Published: 2022-05-02
DOI: 10.32614/CRAN.package.endorse
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

Documentation:

Reference manual: endorse.pdf

Downloads:

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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