Package: rr 1.4.2

Graeme Blair

rr: Statistical Methods for the Randomized Response Technique

Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal of the American Statistical Association <https://graemeblair.com/papers/randresp.pdf>.

Authors:Graeme Blair [aut, cre], Yang-Yang Zhou [aut], Kosuke Imai [aut], Winston Chou [ctb]

rr_1.4.2.tar.gz
rr_1.4.2.tar.gz(r-4.5-noble)rr_1.4.2.tar.gz(r-4.4-noble)
rr_1.4.2.tgz(r-4.4-emscripten)rr_1.4.2.tgz(r-4.3-emscripten)
rr.pdf |rr.html✨
rr/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • nigeria - Nigeria Randomized Response Survey Experiment on Social Connections to Armed Groups

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.36 score 1 stars 23 scripts 250 downloads 1 mentions 5 exports 14 dependencies

Last updated 9 months agofrom:5a82266c32. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-linux-x86_64OKOct 13 2024

Exports:power.rr.plotpower.rr.testrrregrrreg.bayesrrreg.predictor

Dependencies:abindarmbootcodalatticelme4magicMASSMatrixminqanlmenloptrRcppRcppEigen