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decisionfacets 0.2.0

• Designs with one examiner per case (or any partially overlapping rating design) are now supported. df_counterfactual() compares each candidate’s observed examiners with random reassignments that draw, for each case, an examiner qualified for that case, without repeating an examiner for the same candidate. df_simulate() gains design = "per_item" and raters_per_item.
• Continuous scores (for example 0-100) are supported through a linear many-facet model, score = ability - case difficulty - examiner severity + error. df_data() gains scale = c("auto", "ordinal", "continuous"); df_fit() uses the new "linear" engine for continuous scores; df_simulate() gains scale = "continuous".
• New decision rule df_cut(value, "raw_mean"): pass if the mean observed rating reaches value (e.g. 70), convenient when candidates have different numbers of ratings.
• df_fit() stops with a clear message when the rating network is disconnected, because rater severities would then not be comparable.
• New synthetic data set oral_exam: 300 candidates, 12 cases, one examiner per case, 0-100 scores.
• Results for the existing crossed designs with ordinal scores are unchanged.

decisionfacets 0.1.0

• Initial release.
• Many-facet Rasch rating scale model fitting via ‘TAM’ (marginal ML) or built-in JMLE (df_fit()).
• Explicit decision rules: raw total, fair average, measure (df_cut(), df_classify()).
• Counterfactual pass probabilities under observed, average and random rater panels, with rater-dependence flags (df_counterfactual()).
• Attribution of expected misclassification to measurement error and rater assignment (df_attribute()).
• Known-truth simulation (df_simulate()).

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.