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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.
Health stats visible at Monitor.