{ "_id": "6ae4c7123d0f299392dd389a", "Package": "mfrmr", "Type": "Package", "Title": "Estimation and Diagnostics for Many-Facet Measurement Models", "Version": "0.2.4", "Date": "2026-10-27", "Config/mfrmr/release-status": "candidate", "Config/mfrmr/public-version": "0.2.3.1", "Authors@R": "person(given = \"Ryuya\", family = \"Komuro\", role = c(\"aut\", \"cre\", \"cph\"),\nemail = \"ryuya.komuro.c4@tohoku.ac.jp\",\ncomment = c(ORCID = \"0000-0001-9205-0926\"))", "Description": "Estimates many-facet ordered-response measurement models\nwith arbitrary facet counts, rating-scale and partial-credit\nparameterizations, a generalized partial-credit extension with\ndiscrimination varying across levels of one selected facet.\nMarginal maximum likelihood permits a different facet to own\ncategory steps; joint maximum likelihood requires the same\nfacet for both roles. Conditional maximum likelihood fits\nmany-facet rating-scale and partial-credit models by\nconditioning on person totals, with structural contrast\nintervals and no assumed ability distribution. Experimental\ntwo-family estimation and conditional scoring retain explicit\nuncertainty limitations. The fit, diagnose and report workflow\ncovers anchoring, linking, bias and differential-functioning\nscreening, with saved results for replay. Observed-score\ngeneralizability and decision studies support assessment\nplanning under declared rating designs. Agreement, rater\nfeedback and conditional classification summaries connect\nanalysis to assessment decisions. See 'Andrich' (1978)\n, 'Masters' (1982)\n, and 'Muraki' (1992)\n for the underlying\nordered-response models.", "URL": "https://ryuya-dot-com.github.io/mfrmr/,\nhttps://github.com/Ryuya-dot-com/mfrmr", "BugReports": "https://github.com/Ryuya-dot-com/mfrmr/issues", "License": "MIT + file LICENSE", "Language": "en", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/Needs/website": "pkgdown, plotly, htmlwidgets", "Config/roxygen2/version": "8.1.1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-30 16:19:59 UTC", "User": "root" }, "Author": "Ryuya Komuro [aut, cre, cph] (ORCID:\n)", "Maintainer": "Ryuya Komuro ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-10-30 14:25:57 UTC", "RemoteUrl": "https://github.com/cran/mfrmr", "RemoteRef": "HEAD", "RemoteSha": "38f6fe90f4be20f551282aafff59af4f946e7e77", "_user": "cran", "_type": "src", "_file": "mfrmr_0.2.4.tar.gz", "_fileid": 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"as.data.frame.mfrm_fit", "title": "Convert mfrm_fit to a tidy data.frame", "topics": [ "as.data.frame.mfrm_fit" ] }, { "page": "assess_mfrm_recovery", "title": "Assess whether recovery-simulation results are ready to use", "topics": [ "assess_mfrm_recovery", "plot.mfrm_recovery_assessment" ] }, { "page": "automated_scoring_table", "title": "Automated-scoring compatibility interface for score comparisons", "topics": [ "automated_scoring_table" ] }, { "page": "bias_count_table", "title": "Build a bias-cell count report", "topics": [ "bias_count_table" ] }, { "page": "bias_interaction_report", "title": "Build a bias-interaction plot-data bundle (FACETS Table 13: ranked bias list)", "topics": [ "bias_interaction_report" ] }, { "page": "bias_iteration_report", "title": "Build a bias-iteration report (FACETS Table 9: iteration / convergence trace)", "topics": [ "bias_iteration_report" ] }, { "page": "bias_pairwise_report", "title": "Build a conditional bias pairwise-contrast report (Table 14 style)", "topics": [ "bias_pairwise_report" ] }, { "page": "bootstrap_mfrm_gpcm", "title": "Parametric bootstrap for GPCM slopes or a matched PCM comparison", "topics": [ "bootstrap_mfrm_gpcm", "confint.mfrm_gpcm_bootstrap", "print.mfrm_gpcm_bootstrap", "summary.mfrm_gpcm_bootstrap" ] }, { "page": "build_apa_outputs", "title": "Build APA text outputs from model results", "topics": [ "build_apa_outputs", "summary.mfrm_apa_outputs" ] }, { "page": "build_conquest_overlap_bundle", "title": "Build a scoped ConQuest-overlap bundle", "topics": [ "build_conquest_overlap_bundle" ] }, { "page": "build_equating_chain", "title": "Build a screened linking chain across ordered calibrations", "topics": [ "build_equating_chain", "plot.mfrm_equating_chain", "print.mfrm_equating_chain", "print.summary.mfrm_equating_chain", "summary.mfrm_equating_chain" ] }, { "page": "build_fixed_reports", "title": "Build legacy-compatible fixed-width text reports", "topics": [ "build_fixed_reports" ] }, { "page": "build_linking_review", "title": "Build a linking-review synthesis object", "topics": [ "build_linking_review", "summary.mfrm_linking_review" ] }, { "page": "build_mfrm_manifest", "title": "Build a reproducibility manifest for an MFRM analysis", "topics": [ "build_mfrm_manifest" ] }, { "page": "build_mfrm_network_review", "title": "Build an MFRM network review", "topics": [ "build_mfrm_network_review", "summary.mfrm_network_review" ] }, { "page": "build_mfrm_replay_script", "title": "Build a package-native replay script for an MFRM analysis", "topics": [ "build_mfrm_replay_script" ] }, { "page": "build_mfrm_resampling_spec", "title": "Build an observed-data resampling specification", "topics": [ "build_mfrm_resampling_spec" ] }, { "page": "build_mfrm_sim_spec", "title": "Build an explicit simulation specification for MFRM design studies", "topics": [ "build_mfrm_sim_spec", "print.mfrm_sim_spec" ] }, { "page": "build_misfit_casebook", "title": "Build a case-level misfit review bundle", "topics": [ "build_misfit_casebook", "summary.mfrm_misfit_casebook" ] }, { "page": "build_model_choice_review", "title": "Build a model-choice review across RSM, PCM, and GPCM fits", "topics": [ "build_model_choice_review", "summary.mfrm_model_choice_review" ] }, { "page": "build_peer_review_sim_spec", "title": "Build a peer-review simulation specification", "topics": [ "build_peer_review_sim_spec" ] }, { "page": "build_summary_table_bundle", "title": "Build a manuscript-oriented table bundle from 'summary()' outputs", "topics": [ "build_summary_table_bundle", "plot.mfrm_summary_table_bundle", "summary.mfrm_summary_table_bundle" ] }, { "page": "build_visual_summaries", "title": "Build warning and narrative summaries for visual outputs", "topics": [ "build_visual_summaries" ] }, { "page": "build_weighting_review", "title": "Build a weighting-policy review between Rasch-family and GPCM fits", "topics": [ "build_weighting_review", "summary.mfrm_weighting_review" ] }, { "page": "category_curves_report", "title": "Build a category curve export bundle (preferred alias)", "topics": [ "category_curves_report" ] }, { "page": "category_structure_report", "title": "Build a category structure report (preferred alias)", "topics": [ "category_structure_report" ] }, { "page": "classification_table", "title": "Evaluate classification accuracy, consistency and conditional precision", "topics": [ "classification_table" ] }, { "page": "compare_mfrm", "title": "Compare two or more fitted MFRM models", "topics": [ "compare_mfrm", "plot.mfrm_extended_comparison", "print.mfrm_extended_comparison", "summary.mfrm_extended_comparison" ] }, { "page": "compatibility_alias_table", "title": "List retained compatibility aliases and preferred names", "topics": [ "compatibility_alias_table" ] }, { "page": "compute_facet_design_effect", "title": "Compute descriptive design-effect approximations for each facet", "topics": [ "compute_facet_design_effect" ] }, { "page": "compute_facet_icc", "title": "Compute intra-class correlations for each facet", "concept": [ "ICC", "confidence intervals", "hierarchical structure" ], "topics": [ "compute_facet_icc" ] }, { "page": "compute_information", "title": "Compute design-weighted precision curves for ordered many-facet fits", "topics": [ "compute_information", "plot.mfrm_information" ] }, { "page": "compute_person_fit_indices", "title": "Person fit indices: lz and Snijders-corrected lz*", "topics": [ "compute_person_fit_indices", "summary.mfrm_person_fit_indices" ] }, { "page": "confint.mfrm_fair_average", "title": "Intervals for common-plan Person expected scores", "topics": [ "confint.mfrm_fair_average", "plot.mfrm_fair_intervals", "print.mfrm_fair_intervals", "summary.mfrm_fair_intervals" ] }, { "page": "confint.mfrm_fit", "title": "Confidence intervals for native fit coefficients and GPCM slopes", "topics": [ "confint.mfrm_cml_fit", "confint.mfrm_comparison_fit", "confint.mfrm_fit" ] }, { "page": "confint.mfrm_multivariate_d_study", "title": "Confidence intervals for dependability and SEM in D-study plans", "topics": [ "confint.mfrm_multivariate_d_study" ] }, { "page": "confint.mfrm_multivariate_gstudy", "title": "Sampling uncertainty in G-study variance and covariance components", "topics": [ "confint.mfrm_multivariate_gstudy" ] }, { "page": "confint.mfrm_random_rater", "title": "Population-SD profiles and explicit shared-rater model intervals", "topics": [ "confint.mfrm_random_rater" ] }, { "page": "confint.mfrm_testlet", "title": "Explicit normal-approximation intervals for testlet calibration", "topics": [ "confint.mfrm_testlet" ] }, { "page": "data_quality_report", "title": "Build a data quality summary report (preferred alias)", "topics": [ "data_quality_report" ] }, { "page": "describe_mfrm_data", "title": "Check rating data before fitting an MFRM", "topics": [ "describe_mfrm_data", "plot.mfrm_data_description", "summary.mfrm_data_description" ] }, { "page": "detect_anchor_drift", "title": "Detect anchor drift across multiple calibrations", "topics": [ "detect_anchor_drift", "plot.mfrm_anchor_drift", "print.mfrm_anchor_drift", "print.summary.mfrm_anchor_drift", "summary.mfrm_anchor_drift" ] }, { "page": "detect_facet_nesting", "title": "Detect nesting structure between facets", "topics": [ "detect_facet_nesting" ] }, { "page": "diagnose_mfrm", "title": "Compute diagnostics for an 'mfrm_fit' object", "topics": [ "diagnose_mfrm", "plot.mfrm_diagnostics" ] }, { "page": "dif_interaction_table", "title": "Compute interaction table between a facet and a grouping variable", "topics": [ "dif_interaction_table", "plot.mfrm_dif_interaction" ] }, { "page": "dif_report", "title": "Generate a differential-functioning interpretation report", "topics": [ "dif_report" ] }, { "page": "displacement_table", "title": "Compute displacement diagnostics for facet levels", "topics": [ "displacement_table" ] }, { "page": "draw_mfrm_resamples", "title": "Draw observed-data MFRM resamples", "topics": [ "draw_mfrm_resamples" ] }, { "page": "ej2021_data", "title": "Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021)", "topics": [ "ej2021_combined", "ej2021_combined_itercal", "ej2021_data", "ej2021_study1", "ej2021_study1_itercal", "ej2021_study2", "ej2021_study2_itercal" ] }, { "page": "estimate_all_bias", "title": "Estimate bias across multiple facet pairs", "topics": [ "estimate_all_bias", "plot.mfrm_bias_collection", "print.mfrm_bias_collection", "print.summary.mfrm_bias_collection", "summary.mfrm_bias_collection" ] }, { "page": "estimate_bias", "title": "Estimate bias and interaction screening terms", "topics": [ "estimate_bias", "plot.mfrm_bias", "summary.mfrm_bias" ] }, { "page": "estimation_iteration_report", "title": "Build an estimation-iteration report (preferred alias)", "topics": [ "estimation_iteration_report" ] }, { "page": "evaluate_mfrm_design", "title": "Evaluate MFRM design conditions by repeated simulation", "topics": [ "evaluate_mfrm_design" ] }, { "page": "evaluate_mfrm_diagnostic_screening", "title": "Evaluate legacy and strict marginal diagnostic screening under controlled misfit scenarios", "topics": [ "evaluate_mfrm_diagnostic_screening" ] }, { "page": "evaluate_mfrm_signal_detection", "title": "Evaluate DIF power and bias-screening behavior under known simulated signals", "topics": [ "evaluate_mfrm_signal_detection", "plot.mfrm_signal_detection" ] }, { "page": "export_mfrm_bundle", "title": "Export a fit-level analysis archive", "topics": [ "export_mfrm_bundle" ] }, { "page": "export_mfrm_model", "title": "Export a measurement model to Stan or brms", "topics": [ "export_mfrm_model" ] }, { "page": "export_mfrm_results", "title": "Export an mfrm_results analysis archive", "topics": [ "export_mfrm_results" ] }, { "page": "export_mfrm_tables", "title": "Export fitted MFRM tables to CSV files", "topics": [ "export_mfrm", "export_mfrm_tables" ] }, { "page": "export_summary_appendix", "title": "Export manuscript appendix tables from documented summary surfaces", "topics": [ "export_summary_appendix" ] }, { "page": "extract_mfrm_sim_spec", "title": "Derive a simulation specification from a fitted MFRM object", "topics": [ "extract_mfrm_sim_spec" ] }, { "page": "facet_quality_dashboard", "title": "Facet-quality dashboard for facet-level screening", "topics": [ "facet_quality_dashboard", "plot.mfrm_facet_dashboard", "summary.mfrm_facet_dashboard" ] }, { "page": "facet_small_sample_review", "title": "Review rating counts for each facet level", "topics": [ "facet_small_sample_review", "plot.mfrm_facet_sample_review" ] }, { "page": "facet_statistics_report", "title": "Build a facet statistics report (preferred alias)", "topics": [ "facet_statistics_report" ] }, { "page": "facets_chisq_table", "title": "Build facet variability diagnostics with fixed/random reference tests", "topics": [ "facets_chisq_table" ] }, { "page": "facets_feature_coverage", "title": "FACETS Feature Coverage Matrix", "topics": [ "facets_feature_coverage" ] }, { "page": "facets_fit_df_guide", "title": "Guide FACETS-style fit df and ZSTD standardization", "topics": [ "facets_fit_df_guide" ] }, { "page": "facets_fit_review", "title": "Review fit standardization against FACETS-style ZSTD conventions", "topics": [ "facets_fit_review" ] }, { "page": "facets_output_contract_review", "title": "Build a FACETS output-contract review", "topics": [ "facets_output_contract_review" ] }, { "page": "facets_output_file_bundle", "title": "Build native graph and observation-score output files", "topics": [ "facets_output_file_bundle" ] }, { "page": "facets_positioning_guide", "title": "FACETS Positioning Guide", "topics": [ "facets_positioning_guide" ] }, { "page": "facets_term_crosswalk", "title": "FACETS-to-mfrmr term crosswalk", "topics": [ "facets_term_crosswalk" ] }, { "page": "facets_visual_contract", "title": "FACETS-facing visual contract", "topics": [ "facets_visual_contract" ] }, { "page": "fair_average_table", "title": "Build an adjusted-score reference table bundle", "topics": [ "fair_average_table" ] }, { "page": "fit_measures_table", "title": "Build a FACETS-style fit-measures review table", "concept": [ "confidence intervals", "fit statistics" ], "topics": [ "fit_measures_table" ] }, { "page": "fit_mfrm", "title": "Fit many-facet ordered-response models with a flexible number of facets", "topics": [ "fit_mfrm", "print.mfrm_cml_fit", "print.mfrm_comparison_fit", "summary.mfrm_cml_fit", "summary.mfrm_comparison_fit" ] }, { "page": "fit_mfrm_imputed", "title": "Fit the same MFRM to every completed rating data set", "topics": [ "fit_mfrm_imputed", "print.mfrm_imputed_fits", "summary.mfrm_imputed_fits" ] }, { "page": "fit_mfrm_random_rater", "title": "Fit a rating-scale model with shared random rater severity", "topics": [ "fit_mfrm_random_rater", "plot.mfrm_random_rater", "print.mfrm_random_rater", "summary.mfrm_random_rater" ] }, { "page": "fit_mfrm_testlet", "title": "Fit a rating-scale model with dependence within Person-specific testlets", "topics": [ "fit_mfrm_testlet", "plot.mfrm_testlet", "print.mfrm_testlet", "summary.mfrm_testlet" ] }, { "page": "fit_rater_accuracy", "title": "Estimate criterion-referenced rater accuracy from exact matches", "topics": [ "fit_rater_accuracy" ] }, { "page": "fit_rater_network", "title": "Fit an ERGM to a declared binary rater network", "topics": [ "coef.mfrm_network_ergm", "confint.mfrm_network_ergm", "fit_rater_network", "plot.mfrm_network_ergm", "summary.mfrm_network_ergm", "vcov.mfrm_network_ergm" ] }, { "page": "gpcm_capability_matrix", "title": "GPCM workflow and score availability", "concept": [ "GPCM boundaries", "route selection" ], "topics": [ "gpcm_capability_matrix", "gpcm_runtime_guard_coverage", "gpcm_score_side_contract" ] }, { "page": "gwet_ac2_table", "title": "Compute Gwet's AC2 or unweighted AC1", "topics": [ "gwet_ac2_table" ] }, { "page": "import_erm_fit", "title": "Import an 'eRm' fit to an mfrmr-compatible bundle", "topics": [ "import_erm_fit" ] }, { "page": "import_mirt_fit", "title": "Import an 'mirt' fit to an mfrmr-compatible bundle", "topics": [ "import_mirt_fit", "print.summary.mfrm_imported_fit", "summary.mfrm_imported_fit" ] }, { "page": "import_tam_fit", "title": "Import a 'TAM' fit to an mfrmr-compatible bundle", "topics": [ "import_tam_fit" ] }, { "page": "interaction_effect_table", "title": "Extract model-estimated facet interaction effects", "topics": [ "interaction_effect_table" ] }, { "page": "interrater_agreement_table", "title": "Build an inter-rater agreement report", "topics": [ "interrater_agreement_table" ] }, { "page": "krippendorff_alpha_table", "title": "Compute Krippendorff's alpha across raters", "topics": [ "krippendorff_alpha_table" ] }, { "page": "launch_mfrmr_viewer", "title": "Launch a local Shiny viewer for an mfrm_results object", "topics": [ "launch_mfrmr_viewer" ] }, { "page": "list_mfrmr_data", "title": "List packaged ordered-response MFRM datasets", "topics": [ "list_mfrmr_data" ] }, { "page": "load_mfrmr_data", "title": "Load a packaged ordered-response MFRM dataset", "topics": [ "load_mfrmr_data" ] }, { "page": "make_anchor_table", "title": "Build a candidate direct-anchor table from fitted estimates", "topics": [ "make_anchor_table" ] }, { "page": "mantel_haenszel_table", "title": "Compare binary responses between matched groups", "topics": [ "mantel_haenszel_table" ] }, { "page": "measurable_summary_table", "title": "Build a measurable-data summary", "topics": [ "measurable_summary_table" ] }, { "page": "mfrm_calibration_capabilities", "title": "Portable fixed-calibration capabilities", "topics": [ "mfrm_calibration_capabilities" ] }, { "page": "mfrm_calibration_methods", "title": "Inspect a portable fixed calibration", "topics": [ "mfrm_calibration_methods", "print.mfrm_calibration", "print.summary.mfrm_calibration", "summary.mfrm_calibration" ] }, { "page": "mfrm_calibration_score_methods", "title": "Review and plot portable fixed-calibration scores", "topics": [ "mfrm_calibration_score_methods", "plot.mfrm_calibration_score", "print.mfrm_calibration_score", "print.summary.mfrm_calibration_score", "summary.mfrm_calibration_score" ] }, { "page": "mfrm_calibration_workflow", "title": "Create and use a portable fixed calibration", "topics": [ "extract_mfrm_calibration", "freeze_mfrm_calibration", "load_mfrm_calibration", "mfrm_calibration_workflow", "retire_mfrm_calibration", "review_mfrm_calibration", "save_mfrm_calibration", "score_mfrm_calibration", "supersede_mfrm_calibration", "validate_mfrm_calibration" ] }, { "page": "mfrm_cluster", "title": "Group mixed external features using Gower distances and PAM", "topics": [ "mfrm_cluster", "mfrm_cluster_pam", "print.mfrm_clusters", "summary.mfrm_clusters" ] }, { "page": "mfrm_cluster_compare", "title": "Compare exploratory groups across settings and clustering methods", "topics": [ "mfrm_cluster_compare", "print.mfrm_cluster_comparison", "summary.mfrm_cluster_comparison" ] }, { "page": "mfrm_cluster_hierarchical", "title": "Hierarchical grouping of external features", "topics": [ "mfrm_cluster_hierarchical", "plot.mfrm_hierarchical_clusters" ] }, { "page": "mfrm_cluster_imputed", "title": "Compare exploratory groups across external-feature imputations", "topics": [ "mfrm_cluster_imputed", "print.mfrm_imputed_clusters", "summary.mfrm_imputed_clusters" ] }, { "page": "mfrm_cluster_kmeans", "title": "K-means groups from numeric features or retained principal components", "topics": [ "mfrm_cluster_kmeans" ] }, { "page": "mfrm_curve_intervals", "title": "Uncertainty in GPCM category probabilities and information curves", "topics": [ "mfrm_curve_intervals", "plot.mfrm_curve_intervals", "print.mfrm_curve_intervals", "summary.mfrm_curve_intervals" ] }, { "page": "mfrm_d_study", "title": "Plan measurement conditions with a D-study", "topics": [ "mfrm_d_study", "mfrm_d_study.default", "mfrm_d_study.mfrm_multivariate_gstudy", "mfrm_multivariate_d_study", "print.mfrm_multivariate_d_study" ] }, { "page": "mfrm_d_study_main_effects", "title": "Main-effects D-study approximation", "topics": [ "mfrm_d_study.mfrm_fit", "mfrm_d_study.mfrm_generalizability", "mfrm_d_study_main_effects", "summary.mfrm_d_study" ] }, { "page": "mfrm_dff_methods", "title": "Review differential-functioning results", "topics": [ "mfrm_dff_methods", "print.mfrm_dff", "print.mfrm_dif", "print.summary.mfrm_dff", "print.summary.mfrm_dif", "summary.mfrm_dff", "summary.mfrm_dif" ] }, { "page": "mfrm_facet_intervals", "title": "Pointwise intervals for fixed facet estimates and contrasts", "topics": [ "confint.mfrm_facet_intervals", "mfrm_facet_intervals", "plot.mfrm_facet_intervals", "print.mfrm_facet_intervals", "summary.mfrm_facet_intervals", "vcov.mfrm_facet_intervals" ] }, { "page": "mfrm_features", "title": "Prepare external features for exploratory grouping", "topics": [ "mfrm_features", "print.mfrm_features", "summary.mfrm_features" ] }, { "page": "mfrm_fit_extractors", "title": "Extract coefficients, covariance, likelihood and observation counts", "topics": [ "AIC.mfrm_cml_fit", "AIC.mfrm_fit", "BIC.mfrm_cml_fit", "BIC.mfrm_fit", "coef.mfrm_cml_fit", "coef.mfrm_comparison_fit", "coef.mfrm_fit", "logLik.mfrm_cml_fit", "logLik.mfrm_comparison_fit", "logLik.mfrm_fit", "mfrm_fit_extractors", "nobs.mfrm_cml_fit", "nobs.mfrm_comparison_fit", "nobs.mfrm_fit", "vcov.mfrm_cml_fit", "vcov.mfrm_comparison_fit", "vcov.mfrm_fit" ] }, { "page": "mfrm_fit_responses", "title": "Extract fitted rating values and residuals", "topics": [ "fitted.mfrm_cml_fit", "fitted.mfrm_comparison_fit", "fitted.mfrm_fit", "mfrm_fit_responses", "predict.mfrm_cml_fit", "predict.mfrm_comparison_fit", "predict.mfrm_fit", "residuals.mfrm_cml_fit", "residuals.mfrm_comparison_fit", "residuals.mfrm_fit" ] }, { "page": "mfrm_g_study", "title": "G-study for one or several observed scores", "topics": [ "mfrm_g_study", "mfrm_multivariate_gstudy", "print.mfrm_multivariate_gstudy", "summary.mfrm_multivariate_gstudy" ] }, { "page": "mfrm_generalizability", "title": "Main-effects generalizability approximation for an MFRM design", "topics": [ "mfrm_generalizability" ] }, { "page": "mfrm_misfit_thresholds", "title": "MnSq misfit threshold pair used across mfrmr screening helpers", "topics": [ "mfrm_misfit_thresholds" ] }, { "page": "mfrm_multivariate_d_compare", "title": "Compare prespecified multivariate D-study plans", "topics": [ "mfrm_multivariate_d_compare", "plot.mfrm_multivariate_d_comparison", "print.mfrm_multivariate_d_comparison", "summary.mfrm_multivariate_d_comparison" ] }, { "page": "mfrm_network_analysis", "title": "Analyze the MFRM design network", "topics": [ "mfrm_network_analysis" ] }, { "page": "mfrm_pca", "title": "Principal components of numeric external features", "topics": [ "mfrm_pca", "plot.mfrm_pca", "print.mfrm_pca", "summary.mfrm_pca" ] }, { "page": "mfrm_population_intervals", "title": "Pointwise intervals for a conditional-normal population model", "topics": [ "mfrm_population_intervals", "plot.mfrm_population_intervals", "print.mfrm_population_intervals", "summary.mfrm_population_intervals", "vcov.mfrm_population_intervals" ] }, { "page": "mfrm_random_rater_intervals", "title": "Bootstrap prediction intervals for observed random raters", "topics": [ "confint.mfrm_random_rater_intervals", "mfrm_random_rater_intervals", "plot.mfrm_random_rater_intervals", "print.mfrm_random_rater_intervals", "summary.mfrm_random_rater_intervals" ] }, { "page": "mfrm_report", "title": "Build reports from measurement, G/D-study or residual results", "topics": [ "mfrm_report" ] }, { "page": "mfrm_residual_network", "title": "Explore residual associations and groups of facet levels", "topics": [ "mfrm_residual_network", "plot.mfrm_residual_network", "print.mfrm_residual_network", "summary.mfrm_residual_network" ] }, { "page": "mfrm_response_diagnostics", "title": "Describe fitted response probabilities and residuals", "topics": [ "mfrm_response_diagnostics", "plot.mfrm_response_diagnostics", "print.mfrm_response_diagnostics", "summary.mfrm_response_diagnostics" ] }, { "page": "mfrm_response_imputations", "title": "Review multiple imputations of scores on assigned ratings", "topics": [ "mfrm_response_imputations", "print.mfrm_response_imputations", "review_mfrm_imputations", "summary.mfrm_response_imputations" ] }, { "page": "mfrm_results", "title": "Build comprehensive first-screen MFRM results", "topics": [ "mfrm_results" ] }, { "page": "mfrm_results_interactive", "title": "Interactively choose data-frame columns before calling 'mfrm_results()'", "topics": [ "mfrm_results_interactive" ] }, { "page": "mfrm_screening_performance", "title": "Evaluate screening outcomes against a planned simulation roster", "topics": [ "mfrm_screening_performance", "plot.mfrm_screening_performance", "print.mfrm_screening_performance", "summary.mfrm_screening_performance" ] }, { "page": "mfrm_screening_sensitivity", "title": "Compare fit-screening thresholds against known simulation truth", "topics": [ "mfrm_screening_sensitivity", "plot.mfrm_screening_sensitivity", "print.mfrm_screening_sensitivity", "summary.mfrm_screening_sensitivity" ] }, { "page": "mfrm_threshold_profiles", "title": "List literature-based warning threshold profiles", "topics": [ "mfrm_threshold_profiles", "summary.mfrm_threshold_profiles" ] }, { "page": "mfrmr_agreement_data", "title": "Synthetic ratings illustrating agreement, offsets and missing scores", "topics": [ "mfrmr_agreement_data", "mfrmr_example_agreement" ] }, { "page": "mfrmr_compatibility_layer", "title": "mfrmr Compatibility Layer Map", "topics": [ "mfrmr_compatibility_layer" ] }, { "page": "mfrmr_example_data", "title": "Synthetic many-facet rating examples", "topics": [ "mfrmr_example_bias", "mfrmr_example_core", "mfrmr_example_data", "mfrmr_example_operational" ] }, { "page": "mfrmr_example_operational_design", "title": "Planned assignment roster for the operational example", "topics": [ "mfrmr_example_operational_design" ] }, { "page": "mfrmr_gtheory_data", "title": "Synthetic crossed and nested data for learning G theory", "topics": [ "mfrmr_example_gtheory_crossed", "mfrmr_example_gtheory_nested", "mfrmr_gtheory_data" ] }, { "page": "mfrmr_interval_guide", "title": "Confidence-interval and uncertainty route guide", "concept": [ "confidence intervals", "reporting workflow", "uncertainty displays", "visual diagnostics" ], "topics": [ "mfrmr_interval_guide" ] }, { "page": "mfrmr_linking_and_dff", "title": "mfrmr Linking and DFF Guide", "topics": [ "mfrmr_linking_and_dff" ] }, { "page": "mfrmr_methods_reference", "title": "What mfrmr Calculates and Where to Read More", "concept": [ "calculation methods", "methodological references" ], "topics": [ "mfrmr_methods_reference" ] }, { "page": "mfrmr_model_maps", "title": "Wright and Pathway maps for extended RSMs", "topics": [ "mfrmr_model_maps" ] }, { "page": "mfrmr_output_guide", "title": "Choose an mfrmr output helper by user goal", "concept": [ "GPCM boundaries", "reporting workflow", "route selection" ], "topics": [ "mfrmr_output_guide" ] }, { "page": "mfrmr_plot_migration", "title": "Plotting after the 0.2.4 API consolidation", "topics": [ "mfrmr_plot_migration" ] }, { "page": "mfrmr_reporting_and_apa", "title": "mfrmr Reporting and APA Guide", "topics": [ "mfrmr_reporting_and_apa" ] }, { "page": "mfrmr_reports_and_tables", "title": "mfrmr Reports and Tables Map", "topics": [ "mfrmr_reports_and_tables" ] }, { "page": "mfrmr_visual_diagnostics", "title": "mfrmr Visual Diagnostics Map", "concept": [ "GPCM boundaries", "confidence intervals", "reporting workflow", "visual diagnostics" ], "topics": [ "mfrmr_visual_diagnostics" ] }, { "page": "mfrmr_workflow_methods", "title": "mfrmr Workflow and Method Map", "topics": [ "mfrmr_workflow_methods" ] }, { "page": "mml_quadrature_sensitivity", "title": "Review MML sensitivity to the quadrature grid", "topics": [ "gpcm_mml_quadrature_sensitivity", "mml_quadrature_sensitivity" ] }, { "page": "normalize_conquest_overlap_exports", "title": "Normalize the CSV exports generated by the ConQuest overlap template", "topics": [ "normalize_conquest_overlap_exports" ] }, { "page": "normalize_conquest_overlap_files", "title": "Normalize extracted ConQuest overlap files to the 'mfrmr' review contract", "topics": [ "normalize_conquest_overlap_files" ] }, { "page": "normalize_conquest_overlap_tables", "title": "Normalize extracted ConQuest overlap tables to the 'mfrmr' review contract", "topics": [ "normalize_conquest_overlap_tables" ] }, { "page": "ordinal_logistic_table", "title": "Compare ordered responses using cumulative logistic models", "topics": [ "ordinal_logistic_table" ] }, { "page": "pairwise_icc_table", "title": "Compute observed-score ICCs for each rater pair", "topics": [ "pairwise_icc_table" ] }, { "page": "pairwise_kappa_table", "title": "Compute pairwise weighted Cohen's kappa", "topics": [ "pairwise_kappa_table" ] }, { "page": "plot_data", "title": "Extract reusable data from an mfrmr plot object", "topics": [ "plot_data" ] }, { "page": "plot_data_components", "title": "List reusable components in mfrmr plot data", "topics": [ "plot_data_components" ] }, { "page": "plot.mfrm_bundle", "title": "Plot report/table bundles with base R defaults", "topics": [ "plot.mfrm_bundle" ] }, { "page": "plot.mfrm_clusters", "title": "Plot exploratory groups and imputation sensitivity", "topics": [ "plot.mfrm_clusters", "plot.mfrm_imputed_clusters" ] }, { "page": "plot.mfrm_d_study", "title": "Plot design comparisons from a main-effects D-study", "topics": [ "plot.mfrm_d_study" ] }, { "page": "plot.mfrm_design_evaluation", "title": "Plot a design-simulation study", "topics": [ "plot.mfrm_design_evaluation" ] }, { "page": "plot.mfrm_diagnostic_screening", "title": "Plot a diagnostic-screening simulation study", "topics": [ "plot.mfrm_diagnostic_screening" ] }, { "page": "plot.mfrm_dif", "title": "Plot differential facet functioning", "topics": [ "plot.mfrm_dif" ] }, { "page": "plot.mfrm_dstudy_intervals", "title": "Compare D-study confidence interval methods", "topics": [ "plot.mfrm_dstudy_intervals" ] }, { "page": "plot.mfrm_facet_nesting", "title": "Plot the pairwise nesting index matrix", "topics": [ "plot.mfrm_facet_nesting" ] }, { "page": "plot.mfrm_fit", "title": "Plot fitted MFRM results with base R", "concept": [ "confidence intervals", "shrinkage", "visual diagnostics" ], "topics": [ "plot.mfrm_fit" ] }, { "page": "plot.mfrm_multivariate_d_study", "title": "Compare measurement-condition counts in a multivariate D-study", "topics": [ "plot.mfrm_multivariate_d_study" ] }, { "page": "plot.mfrm_multivariate_gstudy", "title": "Inspect sources of observed-score variation", "topics": [ "plot.mfrm_multivariate_gstudy" ] }, { "page": "plot.mfrm_recovery_simulation", "title": "Plot parameter-recovery simulation results", "topics": [ "plot.mfrm_recovery_simulation" ] }, { "page": "plot.mfrm_slope_intervals", "title": "Display saved GPCM intervals and bootstrap results", "topics": [ "plot.mfrm_gpcm_bootstrap", "plot.mfrm_slope_intervals" ] }, { "page": "pool_mfrm_imputed", "title": "Pool common facet targets across imputed rating analyses", "topics": [ "plot.mfrm_pooled", "pool_mfrm_imputed", "print.mfrm_pooled", "summary.mfrm_pooled" ] }, { "page": "precision_review_report", "title": "Build a precision review report", "topics": [ "precision_review_report" ] }, { "page": "predict_mfrm_population", "title": "Forecast population-level MFRM operating characteristics for one future design", "topics": [ "predict_mfrm_population" ] }, { "page": "predict_mfrm_units", "title": "Score future or partially observed units under the fitted scoring basis", "topics": [ "predict_mfrm_units", "summary.mfrm_unit_prediction" ] }, { "page": "predict.mfrm_random_rater", "title": "Predict category probabilities for observed or replacement raters", "topics": [ "predict.mfrm_random_rater" ] }, { "page": "predict.mfrm_testlet", "title": "Score Persons using a fixed testlet calibration", "topics": [ "plot.mfrm_testlet_scores", "predict.mfrm_testlet", "print.mfrm_testlet_scores", "summary.mfrm_testlet_scores" ] }, { "page": "print.mfrm_apa_text", "title": "Print APA narrative text with preserved line breaks", "topics": [ "print.mfrm_apa_text" ] }, { "page": "q3_statistic", "title": "mfrmr standardized/aggregated-residual Q3-style screen", "topics": [ "q3_statistic" ] }, { "page": "rater_centrality_table", "title": "Describe rater centrality using RSM residuals and rater-specific PCM steps", "topics": [ "rater_centrality_table" ] }, { "page": "rater_halo_network_analysis", "title": "Analyze rater-by-criterion halo-effect networks", "topics": [ "rater_halo_network_analysis" ] }, { "page": "rater_network_analysis", "title": "Analyze rater agreement, disagreement, and severity-direction networks", "topics": [ "rater_network_analysis" ] }, { "page": "rating_scale_table", "title": "Build a rating-scale diagnostics report", "topics": [ "rating_scale_table" ] }, { "page": "read_facets_fit_table", "title": "Read a FACETS fit table for fit review", "topics": [ "import_facets_fit_table", "read_facets_fit_table" ] }, { "page": "read_facets_results", "title": "Read FACETS results for standalone visualization", "topics": [ "as.data.frame.mfrm_facets_results", "plot.mfrm_facets_results", "print.mfrm_facets_results", "read_facets_results", "summary.mfrm_facets_results" ] }, { "page": "read_mfrm_posterior", "title": "Read an exported Bayesian measurement summary", "topics": [ "confint.mfrm_posterior_summary", "plot.mfrm_posterior_summary", "print.mfrm_posterior_summary", "read_mfrm_posterior", "summary.mfrm_posterior_summary" ] }, { "page": "recode_missing_codes", "title": "Recode common missing-value sentinels to 'NA'", "topics": [ "recode_missing_codes" ] }, { "page": "recommend_mfrm_design", "title": "Recommend a design condition from simulation results", "topics": [ "plot.mfrm_design_recommendation", "print.mfrm_design_recommendation", "print.summary.mfrm_design_recommendation", "recommend_mfrm_design", "summary.mfrm_design_recommendation" ] }, { "page": "reference_case_benchmark", "title": "Benchmark packaged reference cases", "topics": [ "reference_case_benchmark" ] }, { "page": "reference_case_review", "title": "Build a package-native reference review for report completeness", "topics": [ "reference_case_review" ] }, { "page": "reporting_checklist", "title": "Build an auto-filled MFRM reporting checklist", "topics": [ "reporting_checklist", "summary.mfrm_reporting_checklist" ] }, { "page": "response_time_review", "title": "Review response-time patterns outside the MFRM likelihood", "concept": [ "quality control", "response time", "visual diagnostics" ], "topics": [ "plot.mfrm_response_time_review", "response_time_review", "summary.mfrm_response_time_review" ] }, { "page": "review_accessors", "title": "Extract canonical review components", "topics": [ "anchor_review", "precision_review", "review_accessors" ] }, { "page": "review_conquest_overlap", "title": "Review a ConQuest comparison within the documented 'mfrmr' overlap scope", "topics": [ "review_conquest_overlap" ] }, { "page": "review_mfrm_anchors", "title": "Review and normalize anchor/group-anchor tables", "topics": [ "plot.mfrm_anchor_review", "review_mfrm_anchors", "summary.mfrm_anchor_review" ] }, { "page": "review_peer_design", "title": "Review a peer-assessment assignment design", "topics": [ "build_peer_review_design_review", "review_peer_design", "summary.mfrm_peer_review_design_review" ] }, { "page": "run_mfrm_facets", "title": "Run a legacy-compatible estimation workflow wrapper", "topics": [ "mfrmRFacets", "plot.mfrm_facets_run", "run_mfrm_facets" ] }, { "page": "run_qc_pipeline", "title": "Run automated quality control pipeline", "topics": [ "plot.mfrm_qc_pipeline", "run_qc_pipeline" ] }, { "page": "sample_mfrm_plausible_values", "title": "Sample approximate plausible values under fitted posterior scoring", "topics": [ "sample_mfrm_plausible_values", "summary.mfrm_plausible_values" ] }, { "page": "score_comparison_table", "title": "Compare aligned scores against reference ratings", "topics": [ "score_comparison_table" ] }, { "page": "score_mfrm_persons", "title": "Estimate abilities for people already included in a fitted RSM", "topics": [ "plot.mfrm_person_scores", "print.mfrm_person_scores", "score_mfrm_persons", "summary.mfrm_person_scores" ] }, { "page": "score_mfrm_random_rater", "title": "Score Persons while integrating shared random raters", "topics": [ "plot.mfrm_random_rater_scores", "print.mfrm_random_rater_scores", "score_mfrm_random_rater", "summary.mfrm_random_rater_scores" ] }, { "page": "shrinkage_report", "title": "Extract the shrinkage report from a fitted mfrm_fit", "topics": [ "shrinkage_report" ] }, { "page": "simulate_mfrm_data", "title": "Simulate long-format ordered many-facet data for design studies", "topics": [ "simulate_mfrm_data" ] }, { "page": "simulate_mfrm_recovery", "title": "Simulate parameter recovery by repeated generation and refitting", "topics": [ "evaluate_mfrm_recovery", "simulate_mfrm_recovery" ] }, { "page": "specifications_report", "title": "Build a specification summary report (preferred alias)", "topics": [ "specifications_report" ] }, { "page": "subset_connectivity_report", "title": "Build a subset connectivity report (preferred alias)", "topics": [ "subset_connectivity_report" ] }, { "page": "summary.mfrm_bundle", "title": "Summarize report/table bundles in a user-friendly format", "topics": [ "summary.mfrm_bundle" ] }, { "page": "summary.mfrm_design_evaluation", "title": "Summarize a design-simulation study", "topics": [ "summary.mfrm_design_evaluation" ] }, { "page": "summary.mfrm_diagnostic_screening", "title": "Summarize a diagnostic-screening simulation study", "topics": [ "summary.mfrm_diagnostic_screening" ] }, { "page": "summary.mfrm_diagnostics", "title": "Summarize an 'mfrm_diagnostics' object in a user-friendly format", "topics": [ "summary.mfrm_diagnostics" ] }, { "page": "summary.mfrm_facets_run", "title": "Summarize a legacy-compatible workflow run", "topics": [ "summary.mfrm_facets_run" ] }, { "page": "summary.mfrm_fit", "title": "Summarize an 'mfrm_fit' object in a user-friendly format", "topics": [ "summary.mfrm_fit" ] }, { "page": "summary.mfrm_jeffreys_fit", "title": "Inspect a Jeffreys-penalized fit", "topics": [ "coef.mfrm_jeffreys_fit", "confint.mfrm_jeffreys_fit", "fitted.mfrm_jeffreys_fit", "logLik.mfrm_jeffreys_fit", "print.mfrm_jeffreys_fit", "summary.mfrm_jeffreys_fit", "vcov.mfrm_jeffreys_fit" ] }, { "page": "summary.mfrm_multivariate_d_study", "title": "Summarize assessment plans with their uncertainty", "topics": [ "print.summary.mfrm_multivariate_d_study", "summary.mfrm_multivariate_d_study" ] }, { "page": "summary.mfrm_population_prediction", "title": "Summarize a population-level design forecast", "topics": [ "summary.mfrm_population_prediction" ] }, { "page": "summary.mfrm_signal_detection", "title": "Summarize a DIF/bias screening simulation", "topics": [ "summary.mfrm_signal_detection" ] }, { "page": "unexpected_after_bias_table", "title": "Build an unexpected-after-adjustment screening report", "topics": [ "unexpected_after_bias_table" ] }, { "page": "unexpected_response_table", "title": "Build an unexpected-response screening report", "topics": [ "unexpected_response_table" ] }, { "page": "vcov.mfrm_multivariate_gstudy", "title": "Sampling covariance and SEs of G-study component estimates", "topics": [ "vcov.mfrm_multivariate_gstudy" ] }, { "page": "visual_reporting_template", "title": "Figure-reporting template for visual diagnostics", "concept": [ "figure captions", "reporting workflow", "visual diagnostics" ], "topics": [ "visual_reporting_template" ] }, { "page": "write_mfrm_residual_file", "title": "Write a standalone residual file", "topics": [ "write_mfrm_residual_file" ] }, { "page": "write_mfrm_subset_file", "title": "Write standalone subset-connectivity files", "topics": [ "write_mfrm_subset_file" ] } ], "_pkglogo": "https://github.com/cran/mfrmr/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/cran/mfrmr/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "dplyr", "generics", "glue", "GPArotation", "lattice", "lifecycle", "magrittr", "Matrix", "mnormt", "nlme", "pillar", "pkgconfig", "psych", "purrr", "R6", "rlang", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "withr" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "mfrmr-model-choice.Rmd", "filename": "mfrmr-model-choice.html", "title": "Choose a model and estimator for your assessment", "engine": "knitr::rmarkdown", "headings": [ "Start with the decision you need to make", "Choose what the response model needs to explain", "Choose how Person ability enters estimation", "Which combinations are currently implemented?", "Check the population scale before comparing results", "Why not start with the most complex model?", "Compare conclusions, not just fit statistics", "Continue to the analysis and reporting" ], "created": "2026-10-30 14:25:57", "modified": "2026-10-30 14:25:57", "commits": 1 }, { "source": "mfrmr-networks-and-reporting.Rmd", "filename": "mfrmr-networks-and-reporting.html", "title": "Connect rating designs, network diagnostics and reports", "engine": "knitr::rmarkdown", "headings": [ "Inspect the design before estimating a model", "Compare score networks with MFRM", "Model network configurations with ERGM", "Connect to a manuscript, not a coefficient inventory", "Export values and choose your own graphics", "References" ], "created": "2026-10-30 14:25:57", "modified": "2026-10-30 14:25:57", "commits": 1 }, { "source": "mfrmr-testlets.Rmd", "filename": "mfrmr-testlets.html", "title": "Dependent ratings within a person: testlet models", "engine": "knitr::rmarkdown", "headings": [ "Declare what shares an effect", "Fit and check the calibration", "Review fixed-rater feedback", "Score persons and interpret their intervals", "Missing assigned scores and sparse designs", "Reuse and save the result", "Connect to the common graphics and table workflow", "Compare fixed effects with an ordinary RSM", "Inspect posterior predictive residuals", "Compare predictions on the same definition", "Compare Person scores and inspect model-aware maps", "Build a report from saved results" ], "created": "2026-10-30 14:25:57", "modified": "2026-10-30 14:25:57", "commits": 1 }, { "source": "mfrmr-external-features.Rmd", "filename": "mfrmr-external-features.html", "title": "Exploring person, rater, and task attributes", "engine": "knitr::rmarkdown", "headings": [ "Choose attributes and their meaning", "Distinguish unrecorded and undefined values", "Fit an explicit imputation model", "Describe groups and compare choices", "Compare feature selections", "Interpret the retained profiles", "Read separation, feature profiles, and imputation sensitivity", "Custom distribution plots", "Compare PAM with a hierarchy", "How this relates to MFRM rater-type research", "Link separately grouped persons, raters, and tasks", "Distinguish sparse assignment from missing attributes", "Many features, k-means, and PCA", "Reopen an analysis without repeating imputation or clustering", "Plan for the size of the analysis" ], "created": "2026-10-30 14:25:57", "modified": "2026-10-30 14:25:57", "commits": 1 }, { "source": "mfrmr-gpcm-scope.Rmd", "filename": "mfrmr-gpcm-scope.html", "title": "GPCM scope and current limitations", "engine": "knitr::rmarkdown", "headings": [ "Follow the result through the existing API", "Does an unavailable interval mean interval estimation is impossible?", "An explicit corrected-JML workflow", "Score a new cohort using the corrected calibration", "Describe the observed ratings at the corrected estimates", "A provisional two-family workflow", "What the paired automatic-integration study supports", "Saved-input replay after aligning optimizer and interval checks", "Planning a practical two-family analysis", "Estimate the same response model in Stan", "Inspect observed ratings with posterior residuals", "Scoring new Persons with the fitted two-family calibration", "Model, estimation method and algorithm are different choices", "What happened to the name \"bounded GPCM\"?", "What changes if I use JML?", "What can I report under MML?", "Which uncertainty claims are supported?", "How uncertain is a relative slope?", "Choosing the interval for the question", "Examine one relative discrimination with a profile likelihood", "A fitted-model bootstrap alternative", "Why an information warning needs a parameter-specific review", "Uncertainty in probability and information curves", "What the additional interval checks found", "Checking the updated estimator on the affected design", "Carry the selected inference into reports and figures", "Check that the integration grid does not change the decision", "Why intervals, information criteria and tests need separate checks", "Relationship to generalized many-facet models", "When would two slope families help in practice?", "Start from an actual rating table", "What if the analysis could affect a championship?", "Can EM estimate two slope families?", "What does changing the ability SD test?", "Do two slopes handle testlets or local dependence?", "How does this relate to multivariate G theory?", "What can the related facets literature add?", "Which facet receives different slopes?", "Criterion discrimination with rater-specific category use", "An explanation for a high-school reader", "Before fitting: model-choice triage", "Comparing PCM and GPCM", "Save the comparison and carry its limitations into reporting", "Response-family boundary", "GPCM is not one estimator", "Why total-score conditioning does not remove ability", "Report wording templates", "Checking the support boundary", "Slope values, boundaries, and uncertainty", "What can and cannot be compared across programs", "Model structure: which effect does discrimination change?", "Exact overlap and what has been checked", "Inference: same estimates do not imply the same intervals", "Source-grounded recovery interpretation", "Supported routes and verification evidence", "What works with caveats", "What is intentionally restricted", "Recommended substitutes", "A worked example", "Estimator references", "Current boundary" ], "created": "2026-05-16 22:08:17", "modified": "2026-10-30 14:25:57", "commits": 5 }, { "source": "mfrmr-screening-performance.Rmd", "filename": "mfrmr-screening-performance.html", "title": "How often does a rater screen give the right warning?", "engine": "knitr::rmarkdown", "headings": [ "Keep failed and incomplete trials in the planned roster", "Connect simulated ratings to a declared screen", "Compare thresholds without refitting the models", "Distinguish low mean squares from ZSTD-only warnings", "Check the mathematical target before interpreting a threshold", "Design an accuracy study around the feedback decision", "Evidence and limits", "Stress-test thresholds, sample size and low-side warnings" ], "created": "2026-10-30 14:25:57", "modified": "2026-10-30 14:25:57", "commits": 1 }, { "source": "mfrmr-linking-and-dff.Rmd", "filename": "mfrmr-linking-and-dff.html", "title": "mfrmr Linking and DFF", "engine": "knitr::rmarkdown", "headings": [ "First example: reuse selected criterion locations", "Limits that matter before using real data", "Annual speech contests with common recordings", "Identify the performance, judge and rating occasion", "Choose what is shared and what is allowed to change", "Read the existing helper results for their stated purpose", "Which uncertainty is available with anchors?", "Reusing an analysis saved before the centering change", "Comparing an anchor with TAM or ConQuest", "Fit and diagnose the observed ratings", "1. Check connectedness first", "2. Export anchor candidates", "Design the link, not only the percentage", "3. Compare group residuals", "4. Refit DFF when subgroup comparisons are defensible", "Review a particular school comparison before fitting", "Preserve that question alongside a residual screen", "Estimate the declared contrast in one joint calibration", "Estimate an identified comparison when the full model is deficient", "Obtain CML uncertainty for the identified comparison", "Standardize an MML interaction contrast by that fit's population SD", "Comparing with MH or ordinal logistic DIF", "Compare an explicitly shared response roster", "5. Cell-level follow-up", "6. Model-estimated facet interactions", "7. Multi-wave anchor review", "Recommended sequence", "Related help", "References", "Optional: explore rating assignments" ], "created": "2026-03-30 21:05:11", "modified": "2026-10-30 14:25:57", "commits": 5 }, { "source": "mfrmr-reporting-and-apa.Rmd", "filename": "mfrmr-reporting-and-apa.html", "title": "mfrmr Reporting and APA", "engine": "knitr::rmarkdown", "headings": [ "Choose report sections by question", "Start with an editable GPCM report", "1. Fit once and retain the analysis record", "Show what was planned, observed, and excluded", "2. Check uncertainty and fit before writing the answer", "3. Show estimates, uncertainty, and the answer", "Keep separation, agreement, and category functioning distinct", "If the question concerns observed versus adjusted scores", "If the score will determine pass or fail", "A short Methods and Results example", "4. Review gaps and generate draft text", "Manuscript coverage map", "5. Save the analysis and complete the references", "References" ], "created": "2026-03-30 21:05:11", "modified": "2026-10-30 14:25:57", "commits": 6 }, { "source": "mfrmr-visual-diagnostics.Rmd", "filename": "mfrmr-visual-diagnostics.html", "title": "mfrmr Visual Diagnostics", "engine": "knitr::rmarkdown", "headings": [ "Find a figure by purpose", "Latent regression: coefficients and specified comparisons", "Connect a figure to a claim", "Minimal setup", "1. Targeting and scale structure", "Establish what higher scores mean", "Wright map", "Fit pathway", "Category probabilities", "2. Local response and level issues", "Strict marginal follow-up", "3. Linking and coverage", "4. Residual structure and interaction screens", "Checking one substantive dimension without fitting a multidimensional model", "Shared Persons, sparse assignment and the choice of PCA columns", "A residual block is not automatically another substantive ability", "Residual networks, EGA and the assignment graph answer different questions", "5. Custom figures without losing the evidence boundary", "Save a figure with its values and notes", "Set an appearance once for several plots", "Use a common title argument", "Fair Scores and annotations outside the figure", "6. Secondary visual layer", "Save the agreement result and its review rules", "Response-time QC context", "Small-N shrinkage with uncertainty", "7. Attributes and assessment planning", "External-feature hierarchy", "D-study planning", "Portable score-batch review", "Recommended sequence", "Related help" ], "created": "2026-03-30 21:05:11", "modified": "2026-10-30 14:25:57", "commits": 7 }, { "source": "mfrmr-workflow.Rmd", "filename": "mfrmr-workflow.html", "title": "mfrmr Workflow", "engine": "knitr::rmarkdown", "headings": [ "Choose the result you need", "Put the network at the stage its edges describe", "Quick start", "Inspect individual estimates", "Check what needs review", "Use your own CSV", "1. Read and inspect the file", "2. Review the rows and the rubric", "3. Fit and read the results", "If a check stops you", "If criteria occupy separate columns", "Continue with diagnostics and reporting", "1. Check the data and estimation record", "2. Read precision and model fit before interpreting differences", "3. Identify what needs closer inspection", "4. Assemble the evidence for a report", "5. 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