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.

AuthorYuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb]
Date of publication2017-03-20 00:03:59 UTC
MaintainerYuki Shiraito <shiraito@princeton.edu>
LicenseGPL (>= 2)
Version1.5.1
http://imai.princeton.edu/software/endorse.html

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Files

endorse
endorse/src
endorse/src/Makevars
endorse/src/rand.h
endorse/src/rand.c
endorse/src/subroutines.c
endorse/src/vector.c
endorse/src/vector.h
endorse/src/subroutines.h
endorse/src/geodist.c
endorse/src/models.h
endorse/src/geodist.h
endorse/src/init.c
endorse/src/endorse.c
endorse/src/models.c
endorse/NAMESPACE
endorse/data
endorse/data/pakistan.RData
endorse/R
endorse/R/onAttach.R endorse/R/endorse.plot.R endorse/R/GeoCount.R endorse/R/endorse.R
endorse/README.md
endorse/MD5
endorse/DESCRIPTION
endorse/ChangeLog
endorse/man
endorse/man/endorse.Rd endorse/man/endorse.plot.Rd endorse/man/GeoCount.Rd endorse/man/pakistan.Rd

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