Package index • rankingQ

All functions

add_ipw_weights(): Add IPW Weights to the Original Data
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>) autoplot(<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
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>) print(<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