# Design-Based Methods for Ranking Questions

## Documentation for package ‘rankingQ’ version 0.2.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

add_ipw_weights | Add IPW Weights to the Original Data
autoplot.rankingQ_output | Plot rankingQ estimator outputs
avg_rank | Compute the Average Rank of All Items
identity | Identity-ranking data analyzed in Atsusaka and Kim (2025)
identity_w | Identity-ranking data with estimated weights based on inverse probability weighting
imprr_direct | Implements Plug-in Bias-Corrected Estimators for Ranking Data
imprr_direct_rcpp | Implements Plug-in Bias-Corrected Estimators for Ranking Data (Rcpp)
imprr_weights | Computes Bias-Correction Weights for Ranking Data
imprr_weights_boot | Bootstrap IPW-Based Bias-Corrected Estimates for Ranking Data
item_to_rank | Return Rankings with Items as Columns
ordinal_seq | Generate an Ordinal Sequence from a Number
permn_augment | Augmenting Permutation Patterns
plot.rankingQ_output | Plot rankingQ estimator outputs
plot_avg_ranking | Plot Average Rank Results
plot_dist_ranking | Plot the Distribution of Rankings Over the Permutation Space
print.summary.rankingQ_output | Summarize rankingQ estimator outputs
rank_longer | Convert Ranking Columns from Wide to Long Format
rank_wider | Turn Long Ranking Data into a Wide Format
recover_recorded_responses | Recover the Recorded Responses Given that Ranking Items were Randomized
rpluce | Draw Samples from the Plackett-Luce Model
stratified_avg | Stratified Estimate of Average Ranks
summary.rankingQ_output | Summarize rankingQ estimator outputs
table_to_tibble | Turn the Frequency Table into a Tibble or Data Frame
tidy.rankingQ_output | Tidy rankingQ estimator outputs
unbiased_correct_prop | Unbiased Estimator of the Proportion of Random and Non-random Responses
uniformity_test | Uniformity Test for Ranking Patterns