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.

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Description file content

Package
endorse
Version
1.6.1
Date
2018-11-4
Title
Bayesian Measurement Models for Analyzing Endorsement Experiments
Author
Yuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb]
Maintainer
Yuki Shiraito
Depends
coda, utils
Description
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.
LazyLoad
yes
LazyData
yes
License
GPL (>= 2)
URL
NeedsCompilation
yes
Packaged
2018-11-05 04:18:37 UTC; yuki
Repository
CRAN
Date/Publication
2018-11-06 15:40:03 UTC

install.packages('endorse')

1.6.1

11 days ago

https://github.com/SensitiveQuestions/endorse/

Yuki Shiraito

GPL (>= 2)

Depends on

coda, utils

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