SensitiveQuestions/endorse: Bayesian Measurement Models for Analyzing Endorsement Experiments
Version 1.6.0

Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) 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 <[email protected]>
LicenseGPL (>=2)
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
SensitiveQuestions/endorse documentation built on Aug. 13, 2017, 1:17 a.m.