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: 1.6.0
Depends: coda, utils
Published: 2017-06-25
Author: Yuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb]
Maintainer: Yuki Shiraito <shiraito at princeton.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://imai.princeton.edu/software/endorse.html
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: endorse results


Reference manual: endorse.pdf
Package source: endorse_1.6.0.tar.gz
Windows binaries: r-devel: endorse_1.6.0.zip, r-release: endorse_1.6.0.zip, r-oldrel: endorse_1.6.0.zip
OS X binaries: r-release: endorse_1.6.0.tgz, r-oldrel: endorse_1.6.0.tgz
Old sources: endorse archive


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