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

On CRAN:

Conda:

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

openblas

1.00 score 1 stars 301 downloads 1 mentions 5 exports 17 dependencies

Last updated 1 years agofrom:5a82266c32. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 12 2025
R-4.5-linux-x86_64OKMar 12 2025
R-4.4-linux-x86_64OKMar 12 2025

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

Dependencies:abindarmbootcodalatticelme4magicMASSMatrixminqanlmenloptrrbibutilsRcppRcppEigenRdpackreformulas