Package: endorse
Version: 1.6.2
Date: 2022-5-2
Title: Bayesian Measurement Models for Analyzing Endorsement
        Experiments
Authors@R: c(person("Yuki", "Shiraito", role = c("aut","cre"), email = "shiraito@umich.edu"),
	     person("Kosuke", "Imai", role = "aut", email = "imai@harvard.edu"),
	     person("Bryn", "Rosenfeld", role = "ctb", email = "brosenfe@usc.edu"))
Author: Yuki Shiraito [aut, cre],
  Kosuke Imai [aut],
  Bryn Rosenfeld [ctb]
Maintainer: Yuki Shiraito <shiraito@umich.edu>
Depends: coda, utils
Description: 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. 
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
URL: https://github.com/SensitiveQuestions/endorse/
NeedsCompilation: yes
Packaged: 2022-05-02 06:43:44 UTC; yuki
Repository: CRAN
Date/Publication: 2022-05-02 07:00:12 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 18:37:56 UTC; unix
Archs: endorse.so.dSYM
