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Bayesian Measurement Models for Analyzing Endorsement Experiments

Installation

About

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.

github.com/SensitiveQuestions/endorse/

Key Metrics

Version 1.6.2
Published 2022-05-02 726 days ago
Needs compilation? yes
License GPL-2
License GPL-3
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Maintainer

Maintainer

Yuki Shiraito

shiraito@umich.edu

Authors

Yuki Shiraito

aut / cre

Kosuke Imai

aut

Bryn Rosenfeld

ctb

Material

README
ChangeLog
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

endorse archive

Depends

coda
utils