Package: endorse 1.6.2

Yuki Shiraito

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.

Authors:Yuki Shiraito [aut, cre], Kosuke Imai [aut], Bryn Rosenfeld [ctb]

endorse_1.6.2.tar.gz
endorse_1.6.2.zip(r-4.7-x86_64)endorse_1.6.2.zip(r-4.7-arm64)endorse_1.6.2.zip(r-4.6-x86_64)endorse_1.6.2.zip(r-4.6-arm64)
endorse_1.6.2.tar.gz(r-4.7-arm64)endorse_1.6.2.tar.gz(r-4.7-x86_64)endorse_1.6.2.tar.gz(r-4.6-arm64)endorse_1.6.2.tar.gz(r-4.6-x86_64)
endorse_1.6.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
endorse/json (API)

# Install 'endorse' in R:
install.packages('endorse', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sensitivequestions/endorse/issues

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • pakistan - Pakistan Survey Experiment on Support for Militant Groups

On CRAN:

Conda:

openblas

1.74 score 11 scripts 360 downloads 5 exports 2 dependencies

Last updated from:9f80302170. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK81
linux-devel-x86_64OK90
source / vignettesOK304
linux-release-arm64OK72
linux-release-x86_64OK89
windows-devel-arm64OK188
windows-devel-x86_64OK155
windows-release-arm64OK187
windows-release-x86_64OK82
wasm-releaseOK86

Exports:endorseendorse.plotGeoCountGeoIdpredict.endorse

Dependencies:codalattice