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

Package details

AuthorYuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb]
MaintainerYuki Shiraito <shiraito@umich.edu>
LicenseGPL (>= 2)
URL https://github.com/SensitiveQuestions/endorse/
Package repositoryView on CRAN
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endorse documentation built on May 2, 2019, 2:05 a.m.