Package: rankingQ 0.2.0

rankingQ: Design-Based Methods for Ranking Questions

Provides a design-based toolkit for survey ranking questions. Estimates average ranks, marginal rank probabilities, pairwise comparisons, and ranking distributions, with optional bias correction for random responding via anchor-ranking items or user-supplied random-response rates. Includes Plackett-Luce simulation, visualization, format conversion, and diagnostic checks. Methods are described in Atsusaka and Kim (2025) <doi:10.1017/pan.2024.33>.

Authors:Seo-young Silvia Kim [aut, cre, cph], Yuki Atsusaka [aut, cph]

rankingQ_0.2.0.tar.gz
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rankingQ_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rankingQ/json (API)

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

Bug tracker:https://github.com/sysilviakim/rankingq/issues

Pkgdown/docs site:https://sysilviakim.com

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • identity - Identity-ranking data analyzed in Atsusaka and Kim
  • identity_w - Identity-ranking data with estimated weights based on inverse probability weighting

On CRAN:

Conda:

measurement-errorquestionnaire-designrank-orderrankingranking-datasurveysurvey-designcpp

5.86 score 3 stars 9 scripts 316 downloads 19 exports 34 dependencies

Last updated from:3ad42ba186. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK191
linux-devel-x86_64OK173
source / vignettesOK218
linux-release-arm64OK142
linux-release-x86_64OK180
macos-release-arm64OK215
macos-release-x86_64OK361
macos-oldrel-arm64OK211
macos-oldrel-x86_64OK343
windows-devel-arm64OK367
windows-devel-x86_64OK167
windows-release-arm64OK373
windows-release-x86_64OK128
windows-oldrel-x86_64OK134
wasm-releaseOK121

Exports:add_ipw_weightsavg_rankimprr_directimprr_direct_rcppimprr_weightsimprr_weights_bootitem_to_rankordinal_seqpermn_augmentplot_avg_rankingplot_dist_rankingrank_longerrank_widerrecover_recorded_responsesrplucestratified_avgtable_to_tibbleunbiased_correct_propuniformity_test

Dependencies:clicombinatcpp11dplyrestimatrfarverFormulagenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigpurrrR6RColorBrewerRcppRcppEigenrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Overview
Example Data | Estimate Ranking-Based Quantities | Input | Output | Apply Bias Correction | Plug-in Bias-corrected Estimator | Using Attention Checks or Other Methods | Inverse-Probability Weighting | Next Steps
Rendered fromv1-getting-started.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-04-20

Test
Recorded Responses | Recovering Recorded Responses | Uniformity Test | Theory | Test | Validity of the Anchor Question | Caveat: Augment Missing Permutation Patterns
Rendered fromv6-uniformity-test.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-04-20

Data
Target ranking question | Anchor ranking question
Rendered fromv2-basic-setup.Rmdusingknitr::rmarkdown

Last update: 2026-07-09
Started: 2026-04-20

Methods
Estimating the Proportion of Random Responses | Direct Bias Correction via imprr_direct | Results: Estimated Proportion of Random Responses | Results: Estimated Quantities of Interest | Weighting-Based Bias Correction via imprr_weights | Results: Estimated Weights | Results: Estimated PMF with Bias Corrected Data | Estimated Weights with Original Data
Rendered fromv3-bias-correction.Rmdusingknitr::rmarkdown

Last update: 2026-07-09
Started: 2026-04-20

Analysis
Computing Average Ranks
Rendered fromv4-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-07-09
Started: 2026-04-20

Visualization
Visualizing Average Ranks
Rendered fromv5-visualizing-rankings.Rmdusingknitr::rmarkdown

Last update: 2026-07-09
Started: 2026-04-20

Readme and manuals

Help Manual

Help pageTopics
Add IPW Weights to the Original Dataadd_ipw_weights
Compute the Average Rank of All Itemsavg_rank
Identity-ranking data analyzed in Atsusaka and Kim (2025)identity
Identity-ranking data with estimated weights based on inverse probability weightingidentity_w
Implements Plug-in Bias-Corrected Estimators for Ranking Dataimprr_direct
Implements Plug-in Bias-Corrected Estimators for Ranking Data (Rcpp)imprr_direct_rcpp
Computes Bias-Correction Weights for Ranking Dataimprr_weights
Bootstrap IPW-Based Bias-Corrected Estimates for Ranking Dataimprr_weights_boot
Return Rankings with Items as Columnsitem_to_rank
Generate an Ordinal Sequence from a Numberordinal_seq
Augmenting Permutation Patternspermn_augment
Plot Average Rank Resultsplot_avg_ranking
Plot the Distribution of Rankings Over the Permutation Spaceplot_dist_ranking
Plot rankingQ estimator outputsautoplot.rankingQ_output plot.rankingQ_output
Convert Ranking Columns from Wide to Long Formatrank_longer
Turn Long Ranking Data into a Wide Formatrank_wider
Recover the Recorded Responses Given that Ranking Items were Randomizedrecover_recorded_responses
Draw Samples from the Plackett-Luce Modelrpluce
Stratified Estimate of Average Ranksstratified_avg
Summarize rankingQ estimator outputsprint.summary.rankingQ_output summary.rankingQ_output
Turn the Frequency Table into a Tibble or Data Frametable_to_tibble
Tidy rankingQ estimator outputstidy.rankingQ_output
Unbiased Estimator of the Proportion of Random and Non-random Responsesunbiased_correct_prop
Uniformity Test for Ranking Patternsuniformity_test