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rankingQ 0.2.0

• First CRAN submission. The package was previously available on GitHub under version 0.1.0.
• The bundled identity and identity_w datasets now use item names for the marginal-rank columns: party, religion, gender, race (previously app_identity_1–app_identity_4) and household, neighborhood, city, state (previously anc_identity_1–anc_identity_4). Examples and vignettes now pass these columns explicitly, e.g. main_q = c("party", "religion", "gender", "race").
• New exported function rank_wider() for converting long-format ranking data to wide format (one column per item or a single pasted ranking string).
• imprr_direct(): anc_correct is now optional. Users can instead supply p_random directly, or rely on anchor-based estimation of the non-random response rate.
• New exported convenience wrapper add_ipw_weights() for returning the original data augmented with respondent-level IPW weights, with an option to also keep ranking summaries.
• imprr_direct_rcpp(): correctly delegates to imprr_direct() when method = "fixed", forwarding all population and assumption inputs.
• rpluce(): when all remaining Plackett-Luce weights become zero after earlier draws, the remaining items are now sampled uniformly at random rather than being ordered by input position.
• rank_longer(): stricter validation on the reference argument and clearer handling of single- vs multi-column input.
• Stronger input validation across the package: weights must be finite, non-negative, and sum to a positive number; J must be finite; permutation labels are parsed consistently across compact, delimited, and fixed-width formats (including J > 9).
• Permutation-label infrastructure rewritten to support rankings of arbitrary size.
• Documentation: switched to \href{}{} for external URLs; expanded examples and \value{} sections.

rankingQ 0.1.0

• Initial GitHub release (not submitted to CRAN).
• Implements design-based bias correction methods for ranking survey questions, based on Atsusaka and Kim (2025) doi:10.1017/pan.2024.33.
• Core functions for bias correction: imprr_direct(), imprr_direct_rcpp(), and imprr_weights().
• Rcpp backend for bootstrap-based estimation.
• Visualization functions: plot_avg_ranking() and plot_dist_ranking().
• Utility functions for ranking data manipulation: rank_longer(), item_to_rank(), recover_recorded_responses(), and permn_augment().
• Statistical testing with uniformity_test().
• Simulation support with rpluce() for Plackett-Luce sampling.
• Includes example dataset from an identity ranking survey.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.