A B C D E F G I K L M N O P Q R S U V W
| mfrmr-package | mfrmr: Many-Facet Ordered-Response Modeling in R |
| AIC.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| AIC.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| analyze_dff | Differential facet functioning analysis |
| analyze_dif | Differential facet functioning analysis |
| analyze_facet_equivalence | Analyze practical equivalence within a facet |
| analyze_hierarchical_structure | Analyze the hierarchical structure of a rating design |
| analyze_residual_pca | Run exploratory residual PCA summaries |
| anchor_review | Extract canonical review components |
| anchor_to_baseline | Fit new data anchored to a baseline calibration |
| apa_table | Build an APA-oriented table handoff using base R structures |
| apply_empirical_bayes_shrinkage | Apply empirical-Bayes shrinkage to fitted non-person facet estimates |
| as.data.frame.mfrm_facets_results | Read FACETS results for standalone visualization |
| as.data.frame.mfrm_fit | Convert mfrm_fit to a tidy data.frame |
| assess_mfrm_recovery | Assess whether recovery-simulation results are ready to use |
| as_flextable | Generic for converting objects to a 'flextable' |
| as_flextable.apa_table | Convert an 'apa_table' to a 'flextable' |
| as_ggplot | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.default | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_curve_intervals | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_design_evaluation | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_design_evaluation_plot_data | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_gpcm_bootstrap | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_plot_data | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_results | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_signal_detection | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_signal_detection_plot_data | Convert draw-free mfrmr plot data to ggplot2 |
| as_ggplot.mfrm_slope_intervals | Convert draw-free mfrmr plot data to ggplot2 |
| as_kable | Generic for converting objects to a 'knitr::kable' |
| as_kable.apa_table | Convert an 'apa_table' to a 'knitr::kable()' object |
| automated_scoring_table | Automated-scoring compatibility interface for score comparisons |
| bias_count_table | Build a bias-cell count report |
| bias_interaction_report | Build a bias-interaction plot-data bundle (FACETS Table 13: ranked bias list) |
| bias_iteration_report | Build a bias-iteration report (FACETS Table 9: iteration / convergence trace) |
| bias_pairwise_report | Build a conditional bias pairwise-contrast report (Table 14 style) |
| BIC.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| BIC.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| bootstrap_mfrm_gpcm | Parametric bootstrap for GPCM slopes or a matched PCM comparison |
| build_apa_outputs | Build APA text outputs from model results |
| build_conquest_overlap_bundle | Build a scoped ConQuest-overlap bundle |
| build_equating_chain | Build a screened linking chain across ordered calibrations |
| build_fixed_reports | Build legacy-compatible fixed-width text reports |
| build_linking_review | Build a linking-review synthesis object |
| build_mfrm_manifest | Build a reproducibility manifest for an MFRM analysis |
| build_mfrm_network_review | Build an MFRM network review |
| build_mfrm_replay_script | Build a package-native replay script for an MFRM analysis |
| build_mfrm_resampling_spec | Build an observed-data resampling specification |
| build_mfrm_sim_spec | Build an explicit simulation specification for MFRM design studies |
| build_misfit_casebook | Build a case-level misfit review bundle |
| build_model_choice_review | Build a model-choice review across RSM, PCM, and GPCM fits |
| build_peer_review_design_review | Review a peer-assessment assignment design |
| build_peer_review_sim_spec | Build a peer-review simulation specification |
| build_summary_table_bundle | Build a manuscript-oriented table bundle from 'summary()' outputs |
| build_visual_summaries | Build warning and narrative summaries for visual outputs |
| build_weighting_review | Build a weighting-policy review between Rasch-family and GPCM fits |
| category_curves_report | Build a category curve export bundle (preferred alias) |
| category_structure_report | Build a category structure report (preferred alias) |
| classification_table | Evaluate classification accuracy, consistency and conditional precision |
| coef.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| coef.mfrm_comparison_fit | Extract coefficients, covariance, likelihood and observation counts |
| coef.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| coef.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| coef.mfrm_network_ergm | Fit an ERGM to a declared binary rater network |
| compare_mfrm | Compare two or more fitted MFRM models |
| compatibility_alias_table | List retained compatibility aliases and preferred names |
| compute_facet_design_effect | Compute descriptive design-effect approximations for each facet |
| compute_facet_icc | Compute intra-class correlations for each facet |
| compute_information | Compute design-weighted precision curves for ordered many-facet fits |
| compute_person_fit_indices | Person fit indices: lz and Snijders-corrected lz* |
| confint.mfrm_cml_fit | Confidence intervals for native fit coefficients and GPCM slopes |
| confint.mfrm_comparison_fit | Confidence intervals for native fit coefficients and GPCM slopes |
| confint.mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| confint.mfrm_fair_average | Intervals for common-plan Person expected scores |
| confint.mfrm_fit | Confidence intervals for native fit coefficients and GPCM slopes |
| confint.mfrm_gpcm_bootstrap | Parametric bootstrap for GPCM slopes or a matched PCM comparison |
| confint.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| confint.mfrm_multivariate_d_study | Confidence intervals for dependability and SEM in D-study plans |
| confint.mfrm_multivariate_gstudy | Sampling uncertainty in G-study variance and covariance components |
| confint.mfrm_network_ergm | Fit an ERGM to a declared binary rater network |
| confint.mfrm_posterior_summary | Read an exported Bayesian measurement summary |
| confint.mfrm_random_rater | Population-SD profiles and explicit shared-rater model intervals |
| confint.mfrm_random_rater_intervals | Bootstrap prediction intervals for observed random raters |
| confint.mfrm_testlet | Explicit normal-approximation intervals for testlet calibration |
| data_quality_report | Build a data quality summary report (preferred alias) |
| describe_mfrm_data | Check rating data before fitting an MFRM |
| detect_anchor_drift | Detect anchor drift across multiple calibrations |
| detect_facet_nesting | Detect nesting structure between facets |
| diagnose_mfrm | Compute diagnostics for an 'mfrm_fit' object |
| dif_interaction_table | Compute interaction table between a facet and a grouping variable |
| dif_report | Generate a differential-functioning interpretation report |
| displacement_table | Compute displacement diagnostics for facet levels |
| draw_mfrm_resamples | Draw observed-data MFRM resamples |
| ej2021_combined | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_combined_itercal | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_data | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_study1 | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_study1_itercal | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_study2 | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| ej2021_study2_itercal | Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021) |
| estimate_all_bias | Estimate bias across multiple facet pairs |
| estimate_bias | Estimate bias and interaction screening terms |
| estimation_iteration_report | Build an estimation-iteration report (preferred alias) |
| evaluate_mfrm_design | Evaluate MFRM design conditions by repeated simulation |
| evaluate_mfrm_diagnostic_screening | Evaluate legacy and strict marginal diagnostic screening under controlled misfit scenarios |
| evaluate_mfrm_recovery | Simulate parameter recovery by repeated generation and refitting |
| evaluate_mfrm_signal_detection | Evaluate DIF power and bias-screening behavior under known simulated signals |
| export_mfrm | Export fitted MFRM tables to CSV files |
| export_mfrm_bundle | Export a fit-level analysis archive |
| export_mfrm_model | Export a measurement model to Stan or brms |
| export_mfrm_results | Export an mfrm_results analysis archive |
| export_mfrm_tables | Export fitted MFRM tables to CSV files |
| export_summary_appendix | Export manuscript appendix tables from documented summary surfaces |
| extract_mfrm_calibration | Create and use a portable fixed calibration |
| extract_mfrm_sim_spec | Derive a simulation specification from a fitted MFRM object |
| facets_chisq_table | Build facet variability diagnostics with fixed/random reference tests |
| facets_feature_coverage | FACETS Feature Coverage Matrix |
| facets_fit_df_guide | Guide FACETS-style fit df and ZSTD standardization |
| facets_fit_review | Review fit standardization against FACETS-style ZSTD conventions |
| facets_output_contract_review | Build a FACETS output-contract review |
| facets_output_file_bundle | Build native graph and observation-score output files |
| facets_positioning_guide | FACETS Positioning Guide |
| facets_term_crosswalk | FACETS-to-mfrmr term crosswalk |
| facets_visual_contract | FACETS-facing visual contract |
| facet_quality_dashboard | Facet-quality dashboard for facet-level screening |
| facet_small_sample_review | Review rating counts for each facet level |
| facet_statistics_report | Build a facet statistics report (preferred alias) |
| fair_average_table | Build an adjusted-score reference table bundle |
| fitted.mfrm_cml_fit | Extract fitted rating values and residuals |
| fitted.mfrm_comparison_fit | Extract fitted rating values and residuals |
| fitted.mfrm_fit | Extract fitted rating values and residuals |
| fitted.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| fit_measures_table | Build a FACETS-style fit-measures review table |
| fit_mfrm | Fit many-facet ordered-response models with a flexible number of facets |
| fit_mfrm_imputed | Fit the same MFRM to every completed rating data set |
| fit_mfrm_random_rater | Fit a rating-scale model with shared random rater severity |
| fit_mfrm_testlet | Fit a rating-scale model with dependence within Person-specific testlets |
| fit_rater_accuracy | Estimate criterion-referenced rater accuracy from exact matches |
| fit_rater_network | Fit an ERGM to a declared binary rater network |
| freeze_mfrm_calibration | Create and use a portable fixed calibration |
| gpcm_capability_matrix | GPCM workflow and score availability |
| gpcm_mml_quadrature_sensitivity | Review MML sensitivity to the quadrature grid |
| gpcm_runtime_guard_coverage | GPCM workflow and score availability |
| gpcm_score_side_contract | GPCM workflow and score availability |
| gwet_ac2_table | Compute Gwet's AC2 or unweighted AC1 |
| import_erm_fit | Import an 'eRm' fit to an mfrmr-compatible bundle |
| import_facets_fit_table | Read a FACETS fit table for fit review |
| import_mirt_fit | Import an 'mirt' fit to an mfrmr-compatible bundle |
| import_tam_fit | Import a 'TAM' fit to an mfrmr-compatible bundle |
| interaction_effect_table | Extract model-estimated facet interaction effects |
| interrater_agreement_table | Build an inter-rater agreement report |
| krippendorff_alpha_table | Compute Krippendorff's alpha across raters |
| launch_mfrmr_viewer | Launch a local Shiny viewer for an mfrm_results object |
| list_mfrmr_data | List packaged ordered-response MFRM datasets |
| load_mfrmr_data | Load a packaged ordered-response MFRM dataset |
| load_mfrm_calibration | Create and use a portable fixed calibration |
| logLik.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| logLik.mfrm_comparison_fit | Extract coefficients, covariance, likelihood and observation counts |
| logLik.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| logLik.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| make_anchor_table | Build a candidate direct-anchor table from fitted estimates |
| mantel_haenszel_table | Compare binary responses between matched groups |
| measurable_summary_table | Build a measurable-data summary |
| mfrmr | mfrmr: Many-Facet Ordered-Response Modeling in R |
| mfrmRFacets | Run a legacy-compatible estimation workflow wrapper |
| mfrmr_agreement_data | Synthetic ratings illustrating agreement, offsets and missing scores |
| mfrmr_compatibility_layer | mfrmr Compatibility Layer Map |
| mfrmr_example_agreement | Synthetic ratings illustrating agreement, offsets and missing scores |
| mfrmr_example_bias | Synthetic many-facet rating examples |
| mfrmr_example_core | Synthetic many-facet rating examples |
| mfrmr_example_data | Synthetic many-facet rating examples |
| mfrmr_example_gtheory_crossed | Synthetic crossed and nested data for learning G theory |
| mfrmr_example_gtheory_nested | Synthetic crossed and nested data for learning G theory |
| mfrmr_example_operational | Synthetic many-facet rating examples |
| mfrmr_example_operational_design | Planned assignment roster for the operational example |
| mfrmr_gtheory_data | Synthetic crossed and nested data for learning G theory |
| mfrmr_interval_guide | Confidence-interval and uncertainty route guide |
| mfrmr_linking_and_dff | mfrmr Linking and DFF Guide |
| mfrmr_methods_reference | What mfrmr Calculates and Where to Read More |
| mfrmr_model_maps | Wright and Pathway maps for extended RSMs |
| mfrmr_output_guide | Choose an mfrmr output helper by user goal |
| mfrmr_plot_migration | Plotting after the 0.2.4 API consolidation |
| mfrmr_reporting_and_apa | mfrmr Reporting and APA Guide |
| mfrmr_reports_and_tables | mfrmr Reports and Tables Map |
| mfrmr_visual_diagnostics | mfrmr Visual Diagnostics Map |
| mfrmr_workflow_methods | mfrmr Workflow and Method Map |
| mfrm_calibration_capabilities | Portable fixed-calibration capabilities |
| mfrm_calibration_methods | Inspect a portable fixed calibration |
| mfrm_calibration_score_methods | Review and plot portable fixed-calibration scores |
| mfrm_calibration_workflow | Create and use a portable fixed calibration |
| mfrm_cluster | Group mixed external features using Gower distances and PAM |
| mfrm_cluster_compare | Compare exploratory groups across settings and clustering methods |
| mfrm_cluster_hierarchical | Hierarchical grouping of external features |
| mfrm_cluster_imputed | Compare exploratory groups across external-feature imputations |
| mfrm_cluster_kmeans | K-means groups from numeric features or retained principal components |
| mfrm_cluster_pam | Group mixed external features using Gower distances and PAM |
| mfrm_curve_intervals | Uncertainty in GPCM category probabilities and information curves |
| mfrm_dff_methods | Review differential-functioning results |
| mfrm_d_study | Plan measurement conditions with a D-study |
| mfrm_d_study.default | Plan measurement conditions with a D-study |
| mfrm_d_study.mfrm_fit | Main-effects D-study approximation |
| mfrm_d_study.mfrm_generalizability | Main-effects D-study approximation |
| mfrm_d_study.mfrm_multivariate_gstudy | Plan measurement conditions with a D-study |
| mfrm_d_study_main_effects | Main-effects D-study approximation |
| mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| mfrm_features | Prepare external features for exploratory grouping |
| mfrm_fit_extractors | Extract coefficients, covariance, likelihood and observation counts |
| mfrm_fit_responses | Extract fitted rating values and residuals |
| mfrm_generalizability | Main-effects generalizability approximation for an MFRM design |
| mfrm_g_study | G-study for one or several observed scores |
| mfrm_misfit_thresholds | MnSq misfit threshold pair used across mfrmr screening helpers |
| mfrm_multivariate_d_compare | Compare prespecified multivariate D-study plans |
| mfrm_multivariate_d_study | Plan measurement conditions with a D-study |
| mfrm_multivariate_gstudy | G-study for one or several observed scores |
| mfrm_network_analysis | Analyze the MFRM design network |
| mfrm_pca | Principal components of numeric external features |
| mfrm_population_intervals | Pointwise intervals for a conditional-normal population model |
| mfrm_random_rater_intervals | Bootstrap prediction intervals for observed random raters |
| mfrm_report | Build reports from measurement, G/D-study or residual results |
| mfrm_residual_network | Explore residual associations and groups of facet levels |
| mfrm_response_diagnostics | Describe fitted response probabilities and residuals |
| mfrm_response_imputations | Review multiple imputations of scores on assigned ratings |
| mfrm_results | Build comprehensive first-screen MFRM results |
| mfrm_results_interactive | Interactively choose data-frame columns before calling 'mfrm_results()' |
| mfrm_screening_performance | Evaluate screening outcomes against a planned simulation roster |
| mfrm_screening_sensitivity | Compare fit-screening thresholds against known simulation truth |
| mfrm_threshold_profiles | List literature-based warning threshold profiles |
| mml_quadrature_sensitivity | Review MML sensitivity to the quadrature grid |
| nobs.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| nobs.mfrm_comparison_fit | Extract coefficients, covariance, likelihood and observation counts |
| nobs.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| normalize_conquest_overlap_exports | Normalize the CSV exports generated by the ConQuest overlap template |
| normalize_conquest_overlap_files | Normalize extracted ConQuest overlap files to the 'mfrmr' review contract |
| normalize_conquest_overlap_tables | Normalize extracted ConQuest overlap tables to the 'mfrmr' review contract |
| ordinal_logistic_table | Compare ordered responses using cumulative logistic models |
| pairwise_icc_table | Compute observed-score ICCs for each rater pair |
| pairwise_kappa_table | Compute pairwise weighted Cohen's kappa |
| plot.apa_table | Build an APA-oriented table handoff using base R structures |
| plot.mfrm_anchor_drift | Detect anchor drift across multiple calibrations |
| plot.mfrm_anchor_review | Review and normalize anchor/group-anchor tables |
| plot.mfrm_bias | Estimate bias and interaction screening terms |
| plot.mfrm_bias_collection | Estimate bias across multiple facet pairs |
| plot.mfrm_bundle | Plot report/table bundles with base R defaults |
| plot.mfrm_calibration_score | Review and plot portable fixed-calibration scores |
| plot.mfrm_clusters | Plot exploratory groups and imputation sensitivity |
| plot.mfrm_curve_intervals | Uncertainty in GPCM category probabilities and information curves |
| plot.mfrm_data_description | Check rating data before fitting an MFRM |
| plot.mfrm_design_evaluation | Plot a design-simulation study |
| plot.mfrm_design_recommendation | Recommend a design condition from simulation results |
| plot.mfrm_diagnostics | Compute diagnostics for an 'mfrm_fit' object |
| plot.mfrm_diagnostic_screening | Plot a diagnostic-screening simulation study |
| plot.mfrm_dif | Plot differential facet functioning |
| plot.mfrm_dif_interaction | Compute interaction table between a facet and a grouping variable |
| plot.mfrm_dstudy_intervals | Compare D-study confidence interval methods |
| plot.mfrm_d_study | Plot design comparisons from a main-effects D-study |
| plot.mfrm_equating_chain | Build a screened linking chain across ordered calibrations |
| plot.mfrm_extended_comparison | Compare two or more fitted MFRM models |
| plot.mfrm_facets_results | Read FACETS results for standalone visualization |
| plot.mfrm_facets_run | Run a legacy-compatible estimation workflow wrapper |
| plot.mfrm_facet_dashboard | Facet-quality dashboard for facet-level screening |
| plot.mfrm_facet_equivalence | Analyze practical equivalence within a facet |
| plot.mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| plot.mfrm_facet_nesting | Plot the pairwise nesting index matrix |
| plot.mfrm_facet_sample_review | Review rating counts for each facet level |
| plot.mfrm_fair_intervals | Intervals for common-plan Person expected scores |
| plot.mfrm_fit | Plot fitted MFRM results with base R |
| plot.mfrm_gpcm_bootstrap | Display saved GPCM intervals and bootstrap results |
| plot.mfrm_hierarchical_clusters | Hierarchical grouping of external features |
| plot.mfrm_imputed_clusters | Plot exploratory groups and imputation sensitivity |
| plot.mfrm_information | Compute design-weighted precision curves for ordered many-facet fits |
| plot.mfrm_multivariate_d_comparison | Compare prespecified multivariate D-study plans |
| plot.mfrm_multivariate_d_study | Compare measurement-condition counts in a multivariate D-study |
| plot.mfrm_multivariate_gstudy | Inspect sources of observed-score variation |
| plot.mfrm_network_ergm | Fit an ERGM to a declared binary rater network |
| plot.mfrm_pca | Principal components of numeric external features |
| plot.mfrm_person_scores | Estimate abilities for people already included in a fitted RSM |
| plot.mfrm_pooled | Pool common facet targets across imputed rating analyses |
| plot.mfrm_population_intervals | Pointwise intervals for a conditional-normal population model |
| plot.mfrm_posterior_summary | Read an exported Bayesian measurement summary |
| plot.mfrm_qc_pipeline | Run automated quality control pipeline |
| plot.mfrm_random_rater | Fit a rating-scale model with shared random rater severity |
| plot.mfrm_random_rater_intervals | Bootstrap prediction intervals for observed random raters |
| plot.mfrm_random_rater_scores | Score Persons while integrating shared random raters |
| plot.mfrm_recovery_assessment | Assess whether recovery-simulation results are ready to use |
| plot.mfrm_recovery_simulation | Plot parameter-recovery simulation results |
| plot.mfrm_residual_network | Explore residual associations and groups of facet levels |
| plot.mfrm_residual_pca | Run exploratory residual PCA summaries |
| plot.mfrm_response_diagnostics | Describe fitted response probabilities and residuals |
| plot.mfrm_response_time_review | Review response-time patterns outside the MFRM likelihood |
| plot.mfrm_screening_performance | Evaluate screening outcomes against a planned simulation roster |
| plot.mfrm_screening_sensitivity | Compare fit-screening thresholds against known simulation truth |
| plot.mfrm_signal_detection | Evaluate DIF power and bias-screening behavior under known simulated signals |
| plot.mfrm_slope_intervals | Display saved GPCM intervals and bootstrap results |
| plot.mfrm_summary_table_bundle | Build a manuscript-oriented table bundle from 'summary()' outputs |
| plot.mfrm_testlet | Fit a rating-scale model with dependence within Person-specific testlets |
| plot.mfrm_testlet_scores | Score Persons using a fixed testlet calibration |
| plot_data | Extract reusable data from an mfrmr plot object |
| plot_data_components | List reusable components in mfrmr plot data |
| pool_mfrm_imputed | Pool common facet targets across imputed rating analyses |
| precision_review | Extract canonical review components |
| precision_review_report | Build a precision review report |
| predict.mfrm_cml_fit | Extract fitted rating values and residuals |
| predict.mfrm_comparison_fit | Extract fitted rating values and residuals |
| predict.mfrm_fit | Extract fitted rating values and residuals |
| predict.mfrm_random_rater | Predict category probabilities for observed or replacement raters |
| predict.mfrm_testlet | Score Persons using a fixed testlet calibration |
| predict_mfrm_population | Forecast population-level MFRM operating characteristics for one future design |
| predict_mfrm_units | Score future or partially observed units under the fitted scoring basis |
| print.mfrm_anchored_fit | Fit new data anchored to a baseline calibration |
| print.mfrm_anchor_drift | Detect anchor drift across multiple calibrations |
| print.mfrm_apa_text | Print APA narrative text with preserved line breaks |
| print.mfrm_bias_collection | Estimate bias across multiple facet pairs |
| print.mfrm_calibration | Inspect a portable fixed calibration |
| print.mfrm_calibration_score | Review and plot portable fixed-calibration scores |
| print.mfrm_clusters | Group mixed external features using Gower distances and PAM |
| print.mfrm_cluster_comparison | Compare exploratory groups across settings and clustering methods |
| print.mfrm_cml_fit | Fit many-facet ordered-response models with a flexible number of facets |
| print.mfrm_comparison_fit | Fit many-facet ordered-response models with a flexible number of facets |
| print.mfrm_curve_intervals | Uncertainty in GPCM category probabilities and information curves |
| print.mfrm_design_recommendation | Recommend a design condition from simulation results |
| print.mfrm_dff | Review differential-functioning results |
| print.mfrm_dif | Review differential-functioning results |
| print.mfrm_equating_chain | Build a screened linking chain across ordered calibrations |
| print.mfrm_extended_comparison | Compare two or more fitted MFRM models |
| print.mfrm_facets_results | Read FACETS results for standalone visualization |
| print.mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| print.mfrm_fair_intervals | Intervals for common-plan Person expected scores |
| print.mfrm_features | Prepare external features for exploratory grouping |
| print.mfrm_gpcm_bootstrap | Parametric bootstrap for GPCM slopes or a matched PCM comparison |
| print.mfrm_imputed_clusters | Compare exploratory groups across external-feature imputations |
| print.mfrm_imputed_fits | Fit the same MFRM to every completed rating data set |
| print.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| print.mfrm_multivariate_d_comparison | Compare prespecified multivariate D-study plans |
| print.mfrm_multivariate_d_study | Plan measurement conditions with a D-study |
| print.mfrm_multivariate_gstudy | G-study for one or several observed scores |
| print.mfrm_pca | Principal components of numeric external features |
| print.mfrm_person_scores | Estimate abilities for people already included in a fitted RSM |
| print.mfrm_pooled | Pool common facet targets across imputed rating analyses |
| print.mfrm_population_intervals | Pointwise intervals for a conditional-normal population model |
| print.mfrm_posterior_summary | Read an exported Bayesian measurement summary |
| print.mfrm_random_rater | Fit a rating-scale model with shared random rater severity |
| print.mfrm_random_rater_intervals | Bootstrap prediction intervals for observed random raters |
| print.mfrm_random_rater_scores | Score Persons while integrating shared random raters |
| print.mfrm_residual_network | Explore residual associations and groups of facet levels |
| print.mfrm_response_diagnostics | Describe fitted response probabilities and residuals |
| print.mfrm_response_imputations | Review multiple imputations of scores on assigned ratings |
| print.mfrm_screening_performance | Evaluate screening outcomes against a planned simulation roster |
| print.mfrm_screening_sensitivity | Compare fit-screening thresholds against known simulation truth |
| print.mfrm_sim_spec | Build an explicit simulation specification for MFRM design studies |
| print.mfrm_testlet | Fit a rating-scale model with dependence within Person-specific testlets |
| print.mfrm_testlet_scores | Score Persons using a fixed testlet calibration |
| print.summary.mfrm_anchored_fit | Fit new data anchored to a baseline calibration |
| print.summary.mfrm_anchor_drift | Detect anchor drift across multiple calibrations |
| print.summary.mfrm_bias_collection | Estimate bias across multiple facet pairs |
| print.summary.mfrm_calibration | Inspect a portable fixed calibration |
| print.summary.mfrm_calibration_score | Review and plot portable fixed-calibration scores |
| print.summary.mfrm_design_recommendation | Recommend a design condition from simulation results |
| print.summary.mfrm_dff | Review differential-functioning results |
| print.summary.mfrm_dif | Review differential-functioning results |
| print.summary.mfrm_equating_chain | Build a screened linking chain across ordered calibrations |
| print.summary.mfrm_imported_fit | Import an 'mirt' fit to an mfrmr-compatible bundle |
| print.summary.mfrm_multivariate_d_study | Summarize assessment plans with their uncertainty |
| q3_statistic | mfrmr standardized/aggregated-residual Q3-style screen |
| rater_centrality_table | Describe rater centrality using RSM residuals and rater-specific PCM steps |
| rater_halo_network_analysis | Analyze rater-by-criterion halo-effect networks |
| rater_network_analysis | Analyze rater agreement, disagreement, and severity-direction networks |
| rating_scale_table | Build a rating-scale diagnostics report |
| read_facets_fit_table | Read a FACETS fit table for fit review |
| read_facets_results | Read FACETS results for standalone visualization |
| read_mfrm_posterior | Read an exported Bayesian measurement summary |
| recode_missing_codes | Recode common missing-value sentinels to 'NA' |
| recommend_mfrm_design | Recommend a design condition from simulation results |
| reference_case_benchmark | Benchmark packaged reference cases |
| reference_case_review | Build a package-native reference review for report completeness |
| reporting_checklist | Build an auto-filled MFRM reporting checklist |
| residuals.mfrm_cml_fit | Extract fitted rating values and residuals |
| residuals.mfrm_comparison_fit | Extract fitted rating values and residuals |
| residuals.mfrm_fit | Extract fitted rating values and residuals |
| response_time_review | Review response-time patterns outside the MFRM likelihood |
| retire_mfrm_calibration | Create and use a portable fixed calibration |
| review_accessors | Extract canonical review components |
| review_conquest_overlap | Review a ConQuest comparison within the documented 'mfrmr' overlap scope |
| review_mfrm_anchors | Review and normalize anchor/group-anchor tables |
| review_mfrm_calibration | Create and use a portable fixed calibration |
| review_mfrm_imputations | Review multiple imputations of scores on assigned ratings |
| review_peer_design | Review a peer-assessment assignment design |
| run_mfrm_facets | Run a legacy-compatible estimation workflow wrapper |
| run_qc_pipeline | Run automated quality control pipeline |
| sample_mfrm_plausible_values | Sample approximate plausible values under fitted posterior scoring |
| save_mfrm_calibration | Create and use a portable fixed calibration |
| score_comparison_table | Compare aligned scores against reference ratings |
| score_mfrm_calibration | Create and use a portable fixed calibration |
| score_mfrm_persons | Estimate abilities for people already included in a fitted RSM |
| score_mfrm_random_rater | Score Persons while integrating shared random raters |
| shrinkage_report | Extract the shrinkage report from a fitted mfrm_fit |
| simulate_mfrm_data | Simulate long-format ordered many-facet data for design studies |
| simulate_mfrm_recovery | Simulate parameter recovery by repeated generation and refitting |
| specifications_report | Build a specification summary report (preferred alias) |
| subset_connectivity_report | Build a subset connectivity report (preferred alias) |
| summary.apa_table | Build an APA-oriented table handoff using base R structures |
| summary.mfrm_anchored_fit | Fit new data anchored to a baseline calibration |
| summary.mfrm_anchor_drift | Detect anchor drift across multiple calibrations |
| summary.mfrm_anchor_review | Review and normalize anchor/group-anchor tables |
| summary.mfrm_apa_outputs | Build APA text outputs from model results |
| summary.mfrm_bias | Estimate bias and interaction screening terms |
| summary.mfrm_bias_collection | Estimate bias across multiple facet pairs |
| summary.mfrm_bundle | Summarize report/table bundles in a user-friendly format |
| summary.mfrm_calibration | Inspect a portable fixed calibration |
| summary.mfrm_calibration_score | Review and plot portable fixed-calibration scores |
| summary.mfrm_clusters | Group mixed external features using Gower distances and PAM |
| summary.mfrm_cluster_comparison | Compare exploratory groups across settings and clustering methods |
| summary.mfrm_cml_fit | Fit many-facet ordered-response models with a flexible number of facets |
| summary.mfrm_comparison_fit | Fit many-facet ordered-response models with a flexible number of facets |
| summary.mfrm_curve_intervals | Uncertainty in GPCM category probabilities and information curves |
| summary.mfrm_data_description | Check rating data before fitting an MFRM |
| summary.mfrm_design_evaluation | Summarize a design-simulation study |
| summary.mfrm_design_recommendation | Recommend a design condition from simulation results |
| summary.mfrm_dff | Review differential-functioning results |
| summary.mfrm_diagnostics | Summarize an 'mfrm_diagnostics' object in a user-friendly format |
| summary.mfrm_diagnostic_screening | Summarize a diagnostic-screening simulation study |
| summary.mfrm_dif | Review differential-functioning results |
| summary.mfrm_d_study | Main-effects D-study approximation |
| summary.mfrm_equating_chain | Build a screened linking chain across ordered calibrations |
| summary.mfrm_extended_comparison | Compare two or more fitted MFRM models |
| summary.mfrm_facets_results | Read FACETS results for standalone visualization |
| summary.mfrm_facets_run | Summarize a legacy-compatible workflow run |
| summary.mfrm_facet_dashboard | Facet-quality dashboard for facet-level screening |
| summary.mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| summary.mfrm_fair_intervals | Intervals for common-plan Person expected scores |
| summary.mfrm_features | Prepare external features for exploratory grouping |
| summary.mfrm_fit | Summarize an 'mfrm_fit' object in a user-friendly format |
| summary.mfrm_gpcm_bootstrap | Parametric bootstrap for GPCM slopes or a matched PCM comparison |
| summary.mfrm_imported_fit | Import an 'mirt' fit to an mfrmr-compatible bundle |
| summary.mfrm_imputed_clusters | Compare exploratory groups across external-feature imputations |
| summary.mfrm_imputed_fits | Fit the same MFRM to every completed rating data set |
| summary.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| summary.mfrm_linking_review | Build a linking-review synthesis object |
| summary.mfrm_misfit_casebook | Build a case-level misfit review bundle |
| summary.mfrm_model_choice_review | Build a model-choice review across RSM, PCM, and GPCM fits |
| summary.mfrm_multivariate_d_comparison | Compare prespecified multivariate D-study plans |
| summary.mfrm_multivariate_d_study | Summarize assessment plans with their uncertainty |
| summary.mfrm_multivariate_gstudy | G-study for one or several observed scores |
| summary.mfrm_network_ergm | Fit an ERGM to a declared binary rater network |
| summary.mfrm_network_review | Build an MFRM network review |
| summary.mfrm_pca | Principal components of numeric external features |
| summary.mfrm_peer_review_design_review | Review a peer-assessment assignment design |
| summary.mfrm_person_fit_indices | Person fit indices: lz and Snijders-corrected lz* |
| summary.mfrm_person_scores | Estimate abilities for people already included in a fitted RSM |
| summary.mfrm_plausible_values | Sample approximate plausible values under fitted posterior scoring |
| summary.mfrm_pooled | Pool common facet targets across imputed rating analyses |
| summary.mfrm_population_intervals | Pointwise intervals for a conditional-normal population model |
| summary.mfrm_population_prediction | Summarize a population-level design forecast |
| summary.mfrm_posterior_summary | Read an exported Bayesian measurement summary |
| summary.mfrm_random_rater | Fit a rating-scale model with shared random rater severity |
| summary.mfrm_random_rater_intervals | Bootstrap prediction intervals for observed random raters |
| summary.mfrm_random_rater_scores | Score Persons while integrating shared random raters |
| summary.mfrm_reporting_checklist | Build an auto-filled MFRM reporting checklist |
| summary.mfrm_residual_network | Explore residual associations and groups of facet levels |
| summary.mfrm_response_diagnostics | Describe fitted response probabilities and residuals |
| summary.mfrm_response_imputations | Review multiple imputations of scores on assigned ratings |
| summary.mfrm_response_time_review | Review response-time patterns outside the MFRM likelihood |
| summary.mfrm_screening_performance | Evaluate screening outcomes against a planned simulation roster |
| summary.mfrm_screening_sensitivity | Compare fit-screening thresholds against known simulation truth |
| summary.mfrm_signal_detection | Summarize a DIF/bias screening simulation |
| summary.mfrm_summary_table_bundle | Build a manuscript-oriented table bundle from 'summary()' outputs |
| summary.mfrm_testlet | Fit a rating-scale model with dependence within Person-specific testlets |
| summary.mfrm_testlet_scores | Score Persons using a fixed testlet calibration |
| summary.mfrm_threshold_profiles | List literature-based warning threshold profiles |
| summary.mfrm_unit_prediction | Score future or partially observed units under the fitted scoring basis |
| summary.mfrm_weighting_review | Build a weighting-policy review between Rasch-family and GPCM fits |
| supersede_mfrm_calibration | Create and use a portable fixed calibration |
| unexpected_after_bias_table | Build an unexpected-after-adjustment screening report |
| unexpected_response_table | Build an unexpected-response screening report |
| validate_mfrm_calibration | Create and use a portable fixed calibration |
| vcov.mfrm_cml_fit | Extract coefficients, covariance, likelihood and observation counts |
| vcov.mfrm_comparison_fit | Extract coefficients, covariance, likelihood and observation counts |
| vcov.mfrm_facet_intervals | Pointwise intervals for fixed facet estimates and contrasts |
| vcov.mfrm_fit | Extract coefficients, covariance, likelihood and observation counts |
| vcov.mfrm_jeffreys_fit | Inspect a Jeffreys-penalized fit |
| vcov.mfrm_multivariate_gstudy | Sampling covariance and SEs of G-study component estimates |
| vcov.mfrm_network_ergm | Fit an ERGM to a declared binary rater network |
| vcov.mfrm_population_intervals | Pointwise intervals for a conditional-normal population model |
| visual_reporting_template | Figure-reporting template for visual diagnostics |
| write_mfrm_residual_file | Write a standalone residual file |
| write_mfrm_subset_file | Write standalone subset-connectivity files |