hurdle_acat for hurdle partial credit models (H-PCM).
This is written by Kristoffer Magnusson*_hpcm suffix.
infit_statistic_hpcm()q3_statistic_hpcm()item_parameters_hpcm()person_parameters_hpcm()RMUreliability_hpcm()plot_icc_hpcm()plot_targeting_hpcm()median_hdci()
instead of median_hdi() to avoid issues with multimodal posteriors.plot_icc() theta values.Updates:
infit_statistic() is now faster and defaults to not output outfit statistics,
since these are of dubious value (see Müller 2020
and Johansson, 2025).item_restscore_statistic() is now faster and has output similar to infit_post().q3_statistic() is now faster and has output similar to infit_post().plot_residual_pca() is now faster.Three post-processing helper functions that output a list with result table(s) and a plot:
infit_post() for the output of infit_statistic()item_restscore_post() for the output of item_restscore_statistic()q3_post() for the output of q3_statistic()Other new functions:
plot_icc() can both produce a basic conditional Item Characteristic Curves plot
and optionally a DIF ICC plot, the latter also reporting a partial gamma DIF
magnitude coefficient. This is inspired by iarm::ICCplot()item_parameters() to retrieve item threshold locationsperson_parameters() to retrieve person locations (latent scores) using EAP and WLE,
and also a transformation table from ordinal sum score to EAP/WLE.posterior_to_prior() that extracts priors from a brmsfit model
that may be useful in model fit assessment. An article will be added later,
exploring this further.plot_tile(), plot_bars(), and plot_stackedbars() uses wide format item data to create plots to review
response patterns.