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]
Date of publication2017-06-25 15:31:04 UTC
MaintainerYuki Shiraito <[email protected]>
LicenseGPL (>= 2)
URL http://imai.princeton.edu/software/endorse.html
Package repositoryView on CRAN
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endorse documentation built on June 25, 2017, 5:04 p.m.