Package index

Primary workflow

Start here for routine fit, first-screen results, reports, and exports.

load_mfrmr_data(): Load a packaged ordered-response MFRM dataset
list_mfrmr_data(): List packaged ordered-response MFRM datasets
mfrmr_example_data mfrmr_example_operational mfrmr_example_core mfrmr_example_bias: Synthetic many-facet rating examples
mfrmr_example_operational_design: Planned assignment roster for the operational example
mfrmr_gtheory_data mfrmr_example_gtheory_crossed mfrmr_example_gtheory_nested: Synthetic crossed and nested data for learning G theory
mfrmr_agreement_data mfrmr_example_agreement: Synthetic ratings illustrating agreement, offsets and missing scores
describe_mfrm_data() summary(<mfrm_data_description>) plot(<mfrm_data_description>): Check rating data before fitting an MFRM
fit_mfrm() summary(<mfrm_cml_fit>) print(<mfrm_cml_fit>) print(<mfrm_comparison_fit>) summary(<mfrm_comparison_fit>): Fit many-facet ordered-response models with a flexible number of facets
fit_mfrm_technical_details: Technical Reference for fit_mfrm() Specifications, Controls, and Estimators
summary(<mfrm_jeffreys_fit>) print(<mfrm_jeffreys_fit>) coef(<mfrm_jeffreys_fit>) fitted(<mfrm_jeffreys_fit>) vcov(<mfrm_jeffreys_fit>) confint(<mfrm_jeffreys_fit>) logLik(<mfrm_jeffreys_fit>): Inspect a Jeffreys-penalized fit
coef(<mfrm_fit>) coef(<mfrm_cml_fit>) coef(<mfrm_comparison_fit>) vcov(<mfrm_fit>) vcov(<mfrm_comparison_fit>) nobs(<mfrm_fit>) nobs(<mfrm_cml_fit>) nobs(<mfrm_comparison_fit>) logLik(<mfrm_fit>) logLik(<mfrm_comparison_fit>) AIC(<mfrm_fit>) BIC(<mfrm_fit>) AIC(<mfrm_cml_fit>) BIC(<mfrm_cml_fit>) vcov(<mfrm_cml_fit>) logLik(<mfrm_cml_fit>): Extract coefficients, covariance, likelihood and observation counts
fitted(<mfrm_fit>) residuals(<mfrm_fit>) predict(<mfrm_fit>) fitted(<mfrm_cml_fit>) fitted(<mfrm_comparison_fit>) residuals(<mfrm_cml_fit>) residuals(<mfrm_comparison_fit>) predict(<mfrm_cml_fit>) predict(<mfrm_comparison_fit>): Extract fitted rating values and residuals
confint(<mfrm_fit>) confint(<mfrm_cml_fit>) confint(<mfrm_comparison_fit>): Confidence intervals for native fit coefficients and GPCM slopes
diagnose_mfrm() plot(<mfrm_diagnostics>): Compute diagnostics for an mfrm_fit object
mfrm_results(): Build comprehensive first-screen MFRM results
mfrm_report(): Build reports from measurement, G/D-study or residual results
export_mfrm_results(): Export an mfrm_results analysis archive
mfrmr_output_guide(): Choose an mfrmr output helper by user goal
mfrmr_workflow_methods: mfrmr Workflow and Method Map
mfrmr_methods_reference: What mfrmr Calculates and Where to Read More

Rating design and linking networks

Review who rated what, crossing/nesting and observed linking vulnerabilities. Start with observed ratings or a planned roster; inspect graph links before fitting, then check model identification.

analyze_hierarchical_structure(): Analyze the hierarchical structure of a rating design
detect_facet_nesting(): Detect nesting structure between facets
facet_small_sample_review() plot(<mfrm_facet_sample_review>): Review rating counts for each facet level
subset_connectivity_report(): Build a subset connectivity report (preferred alias)
mfrm_network_analysis(): Analyze the MFRM design network
build_mfrm_network_review() summary(<mfrm_network_review>): Build an MFRM network review
build_peer_review_sim_spec(): Build a peer-review simulation specification
review_peer_design() summary(<mfrm_peer_review_design_review>): Review a peer-assessment assignment design

Missing assigned scores

Review response imputations, fit a common MFRM and pool eligible facet targets.

review_mfrm_imputations() print(<mfrm_response_imputations>) summary(<mfrm_response_imputations>): Review multiple imputations of scores on assigned ratings
fit_mfrm_imputed() print(<mfrm_imputed_fits>) summary(<mfrm_imputed_fits>): Fit the same MFRM to every completed rating data set
pool_mfrm_imputed() print(<mfrm_pooled>) summary(<mfrm_pooled>) plot(<mfrm_pooled>): Pool common facet targets across imputed rating analyses

G-theory assessment planning

Separate observed-score variation, project specified future plans, and compare eligible prespecified plans. Start with the G-theory planning article.

mfrm_g_study() print(<mfrm_multivariate_gstudy>) summary(<mfrm_multivariate_gstudy>): G-study for one or several observed scores
confint(<mfrm_multivariate_gstudy>): Sampling uncertainty in G-study variance and covariance components
vcov(<mfrm_multivariate_gstudy>): Sampling covariance and SEs of G-study component estimates
plot(<mfrm_multivariate_gstudy>): Inspect sources of observed-score variation
mfrm_d_study() print(<mfrm_multivariate_d_study>): Plan measurement conditions with a D-study
summary(<mfrm_multivariate_d_study>) print(<summary.mfrm_multivariate_d_study>): Summarize assessment plans with their uncertainty
confint(<mfrm_multivariate_d_study>): Confidence intervals for dependability and SEM in D-study plans
plot(<mfrm_dstudy_intervals>): Compare D-study confidence interval methods
plot(<mfrm_multivariate_d_study>): Compare measurement-condition counts in a multivariate D-study
mfrm_multivariate_d_compare() summary(<mfrm_multivariate_d_comparison>) print(<mfrm_multivariate_d_comparison>) plot(<mfrm_multivariate_d_comparison>): Compare prespecified multivariate D-study plans
mfrm_generalizability(): Main-effects generalizability approximation for an MFRM design
mfrm_d_study(<mfrm_generalizability>) mfrm_d_study(<mfrm_fit>) summary(<mfrm_d_study>): Main-effects D-study approximation
plot(<mfrm_d_study>): Plot design comparisons from a main-effects D-study

Review fixed-facet estimates and contrasts, and experimental conditional-normal population intervals, under their respective assumptions.

mfrm_facet_intervals() print(<mfrm_facet_intervals>) summary(<mfrm_facet_intervals>) confint(<mfrm_facet_intervals>) vcov(<mfrm_facet_intervals>) plot(<mfrm_facet_intervals>): Pointwise intervals for fixed facet estimates and contrasts
mfrm_population_intervals() plot(<mfrm_population_intervals>) print(<mfrm_population_intervals>) summary(<mfrm_population_intervals>) vcov(<mfrm_population_intervals>): Pointwise intervals for a conditional-normal population model

Response probabilities and residuals

Describe observed ratings using each supported model’s probability definition, without automatic fit cutoffs.

mfrm_response_diagnostics() plot(<mfrm_response_diagnostics>) summary(<mfrm_response_diagnostics>) print(<mfrm_response_diagnostics>): Describe fitted response probabilities and residuals

Shared random raters

Estimate a rater population and distinguish observed-rater feedback from replacement-rater probabilities.

fit_mfrm_random_rater() plot(<mfrm_random_rater>) summary(<mfrm_random_rater>) print(<mfrm_random_rater>): Fit a rating-scale model with shared random rater severity
score_mfrm_random_rater() summary(<mfrm_random_rater_scores>) print(<mfrm_random_rater_scores>) plot(<mfrm_random_rater_scores>): Score Persons while integrating shared random raters
mfrmr_model_maps: Wright and Pathway maps for extended RSMs
confint(<mfrm_random_rater>): Population-SD profiles and explicit shared-rater model intervals
predict(<mfrm_random_rater>): Predict category probabilities for observed or replacement raters
mfrm_random_rater_intervals() plot(<mfrm_random_rater_intervals>) confint(<mfrm_random_rater_intervals>) summary(<mfrm_random_rater_intervals>) print(<mfrm_random_rater_intervals>): Bootstrap prediction intervals for observed random raters

Person-specific testlets

Model local dependence within a Person and score conditionally on fitted calibration.

fit_mfrm_testlet() plot(<mfrm_testlet>) summary(<mfrm_testlet>) print(<mfrm_testlet>): Fit a rating-scale model with dependence within Person-specific testlets
confint(<mfrm_testlet>): Explicit normal-approximation intervals for testlet calibration
predict(<mfrm_testlet>) summary(<mfrm_testlet_scores>) print(<mfrm_testlet_scores>) plot(<mfrm_testlet_scores>): Score Persons using a fixed testlet calibration

Person scoring and conditional decisions

Score Persons, report posterior ability-band probabilities, or evaluate future fixed-plan classification accuracy, consistency and conditional precision.

score_mfrm_persons() summary(<mfrm_person_scores>) print(<mfrm_person_scores>) plot(<mfrm_person_scores>): Estimate abilities for people already included in a fitted RSM
predict_mfrm_units() summary(<mfrm_unit_prediction>): Score future or partially observed units under the fitted scoring basis
classification_table(): Evaluate classification accuracy, consistency and conditional precision
sample_mfrm_plausible_values() summary(<mfrm_plausible_values>): Sample approximate plausible values under fitted posterior scoring

Portable fixed calibration

Create, validate, freeze, persist, and apply eligible RSM/PCM or GPCM MML/JML calibrations; review and export later score batches.

extract_mfrm_calibration() review_mfrm_calibration() update(<mfrm_calibration>) save_mfrm_calibration() load_mfrm_calibration() score_mfrm_calibration(): Create and use a portable fixed calibration
summary(<mfrm_calibration>) print(<mfrm_calibration>) print(<summary.mfrm_calibration>): Inspect a portable fixed calibration
summary(<mfrm_calibration_score>) print(<mfrm_calibration_score>) print(<summary.mfrm_calibration_score>) plot(<mfrm_calibration_score>): Review and plot portable fixed-calibration scores

GPCM slopes and model comparison

Relative-slope intervals, model comparisons, and the scope of GPCM workflows.

compare_mfrm() summary(<mfrm_extended_comparison>) print(<mfrm_extended_comparison>) plot(<mfrm_extended_comparison>): Compare two or more fitted MFRM models
mfrmr_gpcm_scope: GPCM workflow and score availability
bootstrap_mfrm_gpcm() confint(<mfrm_gpcm_bootstrap>) summary(<mfrm_gpcm_bootstrap>) print(<mfrm_gpcm_bootstrap>): Parametric bootstrap for GPCM slopes or a matched PCM comparison
mfrm_curve_intervals() summary(<mfrm_curve_intervals>) print(<mfrm_curve_intervals>) plot(<mfrm_curve_intervals>): Uncertainty in GPCM category probabilities and information curves
plot(<mfrm_slope_intervals>) plot(<mfrm_gpcm_bootstrap>): Display saved GPCM intervals and bootstrap results
build_model_choice_review() summary(<mfrm_model_choice_review>): Build a model-choice review across RSM, PCM, and GPCM fits
build_weighting_review() summary(<mfrm_weighting_review>): Build a weighting-policy review between Rasch-family and GPCM fits
mml_quadrature_sensitivity(): Review MML sensitivity to the quadrature grid
compute_information() plot(<mfrm_information>): Compute design-weighted precision curves for ordered many-facet fits
category_structure_report(): Build a category structure report (preferred alias)
category_curves_report(): Build a category curve export bundle (preferred alias)

Diagnostics and quality control

Fit review, residual diagnostics, precision, agreement, and QC dashboards.

fit_measures_table(): Build a FACETS-style fit-measures review table
facet_quality_dashboard() summary(<mfrm_facet_dashboard>) plot(<mfrm_facet_dashboard>): Facet-quality dashboard for facet-level screening
precision_review_report(): Build a precision review report
measurable_summary_table(): Build a measurable-data summary
rating_scale_table(): Build a rating-scale diagnostics report
interrater_agreement_table(): Build an inter-rater agreement report
pairwise_icc_table(): Compute observed-score ICCs for each rater pair
krippendorff_alpha_table(): Compute Krippendorff's alpha across raters
pairwise_kappa_table(): Compute pairwise weighted Cohen's kappa
gwet_ac2_table(): Compute Gwet's AC2 or unweighted AC1
score_comparison_table(): Compare aligned scores against reference ratings
automated_scoring_table(): Automated-scoring compatibility interface for score comparisons
rater_centrality_table(): Describe rater centrality using RSM residuals and rater-specific PCM steps
rater_network_analysis(): Analyze rater agreement, disagreement, and severity-direction networks
rater_halo_network_analysis(): Analyze rater-by-criterion halo-effect networks
fit_rater_network() summary(<mfrm_network_ergm>) coef(<mfrm_network_ergm>) vcov(<mfrm_network_ergm>) confint(<mfrm_network_ergm>) plot(<mfrm_network_ergm>): Fit an ERGM to a declared binary rater network
fit_rater_accuracy(): Estimate criterion-referenced rater accuracy from exact matches
unexpected_response_table(): Build an unexpected-response screening report
displacement_table(): Compute displacement diagnostics for facet levels
analyze_residual_pca() plot(<mfrm_residual_pca>): Run exploratory residual PCA summaries
mfrm_residual_network() summary(<mfrm_residual_network>) print(<mfrm_residual_network>) plot(<mfrm_residual_network>): Explore residual associations and groups of facet levels
q3_statistic(): mfrmr standardized/aggregated-residual Q3-style screen
compute_person_fit_indices() summary(<mfrm_person_fit_indices>): Person fit indices: lz and Snijders-corrected lz*
run_qc_pipeline() plot(<mfrm_qc_pipeline>): Run automated quality control pipeline
mfrmr_visual_diagnostics: mfrmr Visual Diagnostics Map
mfrmr_plot_migration: Plotting after the 0.2.4 API consolidation
plot(<mfrm_dif>): Plot differential facet functioning

Reporting, tables, and exports

Connect the study question to draft tables, figures, interpretation and saved evidence; see the reporting article for optional sections and model-specific routes.

reporting_checklist() summary(<mfrm_reporting_checklist>): Build an auto-filled MFRM reporting checklist
build_apa_outputs() summary(<mfrm_apa_outputs>): Build APA text outputs from model results
build_visual_summaries(): Build warning and narrative summaries for visual outputs
build_summary_table_bundle() summary(<mfrm_summary_table_bundle>) plot(<mfrm_summary_table_bundle>): Build a manuscript-oriented table bundle from summary() outputs
export_summary_appendix(): Export manuscript appendix tables from documented summary surfaces
build_mfrm_manifest(): Build a reproducibility manifest for an MFRM analysis
build_mfrm_replay_script(): Build a package-native replay script for an MFRM analysis
export_mfrm_bundle(): Export a fit-level analysis archive
export_mfrm_model(): Export a measurement model to Stan or brms
read_mfrm_posterior() plot(<mfrm_posterior_summary>) print(<mfrm_posterior_summary>) summary(<mfrm_posterior_summary>) confint(<mfrm_posterior_summary>) as.data.frame(<mfrm_posterior_summary>): Read an exported Bayesian measurement summary and optional joint draws
predict(<mfrm_posterior_summary>) print(<mfrm_posterior_scores>) summary(<mfrm_posterior_scores>) print(<summary.mfrm_posterior_scores>) confint(<mfrm_posterior_scores>) as.data.frame(<mfrm_posterior_scores>) plot(<mfrm_posterior_scores>): Score new Persons under a saved external Bayesian calibration
apa_table() summary(<apa_table>) plot(<apa_table>): Build an APA-oriented table handoff using base R structures
as_kable(): Generic for converting objects to a knitr::kable
as_flextable(): Generic for converting objects to a flextable
as_ggplot(): Convert draw-free mfrmr plot data to ggplot2
plot_data(): Extract reusable data from an mfrmr plot object
plot_data_components(): List reusable components in mfrmr plot data
mfrmr_reports_and_tables: mfrmr Reports and Tables Map
mfrmr_reporting_and_apa: mfrmr Reporting and APA Guide

Simulation, design, and recovery

Design review, GPCM recovery checks, and future-administration scenarios.

build_mfrm_sim_spec() print(<mfrm_sim_spec>): Build an explicit simulation specification for MFRM design studies
simulate_mfrm_data(): Simulate long-format ordered many-facet data for design studies
extract_mfrm_sim_spec(): Derive a simulation specification from a fitted MFRM object
simulate_mfrm_recovery(): Simulate parameter recovery by repeated generation and refitting
assess_mfrm_recovery() plot(<mfrm_recovery_assessment>): Assess whether recovery-simulation results are ready to use
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_signal_detection() plot(<mfrm_signal_detection>): Evaluate DIF power and bias-screening behavior under known simulated signals
mfrm_screening_performance() summary(<mfrm_screening_performance>) print(<mfrm_screening_performance>) plot(<mfrm_screening_performance>): Evaluate screening outcomes against a planned simulation roster
mfrm_screening_sensitivity() summary(<mfrm_screening_sensitivity>) print(<mfrm_screening_sensitivity>) plot(<mfrm_screening_sensitivity>): Compare fit-screening thresholds against known simulation truth
predict_mfrm_population(): Forecast population-level MFRM operating characteristics for one future design
build_mfrm_resampling_spec(): Build an observed-data resampling specification
draw_mfrm_resamples(): Draw observed-data MFRM resamples

Linking, bias, and DFF screening

Anchor review, linking synthesis, residual-bias screening, and differential-functioning review.

make_anchor_table(): Build a candidate direct-anchor table from fitted estimates
review_mfrm_anchors() summary(<mfrm_anchor_review>) plot(<mfrm_anchor_review>): Review and normalize anchor/group-anchor tables
anchor_to_baseline() print(<mfrm_anchored_fit>) summary(<mfrm_anchored_fit>) print(<summary.mfrm_anchored_fit>): Fit new data anchored to a baseline calibration
detect_anchor_drift() print(<mfrm_anchor_drift>) summary(<mfrm_anchor_drift>) print(<summary.mfrm_anchor_drift>) plot(<mfrm_anchor_drift>): Detect anchor drift across multiple calibrations
build_equating_chain() print(<mfrm_equating_chain>) plot(<mfrm_equating_chain>) summary(<mfrm_equating_chain>) print(<summary.mfrm_equating_chain>): Build a screened linking chain across ordered calibrations
build_linking_review() summary(<mfrm_linking_review>): Build a linking-review synthesis object
estimate_bias() summary(<mfrm_bias>) plot(<mfrm_bias>): Estimate bias and interaction screening terms
estimate_all_bias() summary(<mfrm_bias_collection>) print(<mfrm_bias_collection>) print(<summary.mfrm_bias_collection>) plot(<mfrm_bias_collection>): Estimate bias across multiple facet pairs
analyze_dff(): Differential facet functioning analysis
mantel_haenszel_table(): Compare binary responses between matched groups
ordinal_logistic_table(): Compare ordered responses using cumulative logistic models
dif_interaction_table() plot(<mfrm_dif_interaction>): Compute interaction table between a facet and a grouping variable
dif_report(): Generate a differential-functioning interpretation report
summary(<mfrm_dif>) summary(<mfrm_dff>) print(<summary.mfrm_dif>) print(<summary.mfrm_dff>) print(<mfrm_dif>) print(<mfrm_dff>): Review differential-functioning results
mfrmr_linking_and_dff: mfrmr Linking and DFF Guide

FACETS handoff, external-table review, and related package import adapters.

facets_fit_review(): Review fit standardization against FACETS-style ZSTD conventions
facets_output_file_bundle(): Build native graph and observation-score output files
facets_output_contract_review(): Build a FACETS output-contract review
read_facets_results() summary(<mfrm_facets_results>) print(<mfrm_facets_results>) as.data.frame(<mfrm_facets_results>) plot(<mfrm_facets_results>): Read FACETS results for standalone visualization and fit review
facets_guide(): Choose a FACETS comparison guide
run_mfrm_facets() plot(<mfrm_facets_run>): Run a legacy-compatible estimation workflow wrapper
import_irt_fit() summary(<mfrm_imported_fit>) print(<summary.mfrm_imported_fit>): Import an external IRT fit (mirt, TAM, or eRm) to an mfrmr-compatible bundle
mfrmr_compatibility_layer: mfrmr Compatibility Layer Map
mfrmr_api_migration_030 mfrmr_api_migration_025 import_facets_fit_table read_facets_fit_table import_mirt_fit import_tam_fit import_erm_fit mfrmr_interval_guide visual_reporting_template gpcm_capability_matrix mfrm_calibration_capabilities anchor_review precision_review review_accessors: Fewer public entry points and unified ggplot2 graphics in 0.3.0

External features and exploratory groups

Review entity-level external attributes and describe exploratory groups.

mfrm_features() print(<mfrm_features>) summary(<mfrm_features>): Prepare external features for exploratory grouping
mfrm_cluster() print(<mfrm_clusters>) summary(<mfrm_clusters>): Group mixed external features using Gower distances and PAM
plot(<mfrm_hierarchical_clusters>): Hierarchical grouping of external features
mfrm_pca() print(<mfrm_pca>) summary(<mfrm_pca>) plot(<mfrm_pca>): Principal components of numeric external features
mfrm_cluster_kmeans: K-means groups from numeric features or retained principal components
mfrm_cluster_imputed() print(<mfrm_imputed_clusters>) summary(<mfrm_imputed_clusters>): Compare exploratory groups across external-feature imputations
mfrm_cluster_compare() print(<mfrm_cluster_comparison>) summary(<mfrm_cluster_comparison>): Compare exploratory groups across settings and clustering methods
plot(<mfrm_clusters>) plot(<mfrm_imputed_clusters>): Plot exploratory groups and imputation sensitivity

Advanced reviews and visualization

Specialist plots, S3 methods, network reviews, reliability, shrinkage, and compatibility helpers.

analyze_facet_equivalence() plot(<mfrm_facet_equivalence>): Analyze practical equivalence within a facet
apply_empirical_bayes_shrinkage(): Apply empirical-Bayes shrinkage to fitted non-person facet estimates
as.data.frame(<mfrm_fit>): Convert mfrm_fit to a tidy data.frame
as_flextable(<apa_table>): Convert an apa_table to a flextable
as_kable(<apa_table>): Convert an apa_table to a knitr::kable() object
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)
build_conquest_overlap_bundle(): Build a scoped ConQuest-overlap bundle
build_fixed_reports(): Build legacy-compatible fixed-width text reports
build_misfit_casebook() summary(<mfrm_misfit_casebook>): Build a case-level misfit review bundle
compatibility_alias_table(): List compatibility aliases, migration status 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
data_quality_report(): Build a data quality summary report (preferred alias)
ej2021_data ej2021_study1 ej2021_study2 ej2021_combined ej2021_study1_itercal ej2021_study2_itercal ej2021_combined_itercal: Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021)
estimation_iteration_report(): Build an estimation-iteration report (preferred alias)
export_mfrm_tables(): Export fitted MFRM tables to CSV files
facet_statistics_report(): Build a facet statistics report (preferred alias)
facets_chisq_table(): Build facet variability diagnostics with fixed/random reference tests
fair_average_table(): Build an adjusted-score reference table bundle
confint(<mfrm_fair_average>) summary(<mfrm_fair_intervals>) print(<mfrm_fair_intervals>) plot(<mfrm_fair_intervals>): Intervals for common-plan Person expected scores
interaction_effect_table(): Extract model-estimated facet interaction effects
mfrm_misfit_thresholds(): MnSq misfit threshold pair used across mfrmr screening helpers
mfrm_results_interactive(): Interactively choose data-frame columns before calling mfrm_results()
mfrm_threshold_profiles() summary(<mfrm_threshold_profiles>): List literature-based warning threshold profiles
mfrmr mfrmr-package: mfrmr: Many-Facet Ordered-Response Modeling in R
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
plot(<mfrm_bundle>): Plot report/table bundles with ggplot2
plot(<mfrm_design_evaluation>): Plot a design-simulation study
plot(<mfrm_diagnostic_screening>): Plot a diagnostic-screening simulation study
plot(<mfrm_facet_nesting>): Plot the pairwise nesting index matrix
plot(<mfrm_fit>): Plot fitted MFRM results with ggplot2
plot(<mfrm_recovery_simulation>): Plot parameter-recovery simulation results
print(<mfrm_apa_text>): Print APA narrative text with preserved line breaks
recode_missing_codes(): Recode common missing-value sentinels to NA
recommend_mfrm_design() summary(<mfrm_design_recommendation>) print(<summary.mfrm_design_recommendation>) print(<mfrm_design_recommendation>) plot(<mfrm_design_recommendation>): Review assessment plans against declared planning criteria
reference_case_benchmark(): Benchmark packaged reference cases
reference_case_review(): Build a package-native reference review for report completeness
review_conquest_overlap(): Review a ConQuest comparison within the documented mfrmr overlap scope
response_time_review() summary(<mfrm_response_time_review>) plot(<mfrm_response_time_review>): Review response-time patterns outside the MFRM likelihood
shrinkage_report(): Extract the shrinkage report from a fitted mfrm_fit
specifications_report(): Build a specification summary report (preferred alias)
summary(<mfrm_bundle>): Summarize report/table bundles in a user-friendly format
summary(<mfrm_design_evaluation>): Summarize a design-simulation study
summary(<mfrm_diagnostic_screening>): Summarize a diagnostic-screening simulation study
summary(<mfrm_diagnostics>): Summarize an mfrm_diagnostics object in a user-friendly format
summary(<mfrm_facets_run>): Summarize a legacy-compatible workflow run
summary(<mfrm_fit>): Summarize an mfrm_fit object in a user-friendly format
summary(<mfrm_population_prediction>): Summarize a population-level design forecast
summary(<mfrm_signal_detection>): Summarize a DIF/bias screening simulation
unexpected_after_bias_table(): Build an unexpected-after-adjustment screening report
write_mfrm_residual_file(): Write a standalone residual file
write_mfrm_subset_file(): Write standalone subset-connectivity files
launch_mfrmr_viewer(): Launch a local Shiny viewer for an mfrm_results object