The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

mfrmr 0.2.4

Unreleased. This release connects rating analysis, assessment planning and reporting. You can move from a fitted model or rating plan to figures, tables and an editable report while keeping its assumptions visible.

What is easier now?

• Turn an analysis into a report. mfrm_report() now brings generalizability and decision studies (G/D studies), anchor/linking reviews, individual or batch bias screens and model-choice reviews into the shared reporting workflow. Editable templates help you write the Methods and Results, and exports keep the underlying tables and analysis. You can record what high scores mean alongside the results. See reporting examples.
• Compare plans before collecting more ratings. Examine how specified rater/task counts affect the precision of a score, weighted composite or difference. G/D-study reports connect sources of variation to plan tables, figures and supported intervals. Simulation recommendations now show their target, criteria and candidate outcomes in summaries, plots and reports. See assessment planning.
• Find rating patterns that need attention. Check assignment links before fitting, examine observed agreement, and prepare supported rater feedback. Network and residual analyses connect to tables, plots and reports; their exploratory findings prompt investigation, not automatic rater exclusion. See agreement and networks and reporting.

Continue a default single-family GPCM analysis in Stan while retaining its estimated normal population and supported fixed anchors. export_mfrm_model() writes the code, data and parameter maps with explicit priors. RSM/PCM and fixed-population single-family GPCM also support brms; two-family GPCM supports Stan. read_mfrm_posterior() brings saved summaries, including population mean/SD, back to figures and reports with credible intervals and sampling review. Population regression remains unsupported. See model export.

Supported MML models can show fitting progress and automatically increase numerical integration accuracy up to a user-set maximum. Two-family GPCM retains experimental inference limits. The GPCM guide and detailed changes explain these features, calibration reuse, external-software comparisons and numerical improvements.

The visual gallery adds ggplot2 examples with interactive Plotly views, static figures and the underlying values for rater feedback, assessment planning and score probabilities.

Source installation requires cpp11 0.5.4 or later for compatible C++ headers.

Complete worked examples remain in the help pages. Short primary workflows run normally; supplementary examples show how to execute them explicitly with example("topic", run.dontrun = TRUE), including their printed results. Related short summary and plot methods are documented with their generating function; their existing help aliases remain.

Model setup, residual PCA, Q3, residual plots, halo analyses and agreement summaries avoid calculations that their requested results do not use. The same input checks, numerical settings and interpretation limits apply. Simulation specifications and design reviews also avoid repeated calculations, with unchanged design tables, recommendation rules and numerical settings. Testlet fitting and scoring avoid redundant calculations. Scoring also avoids work for design patterns unused by the requested Persons. Quadrature, continuous posterior integration and accuracy checks are unchanged. RSM and PCM marginal maximum likelihood calculations reuse response probabilities for repeated rating conditions on a shared quadrature grid. Integration and convergence settings are unchanged. Model comparisons reject stale or nonstationary MML solutions before expensive uncertainty calculations, while retaining the existing eligibility criteria.

Before reusing an older analysis

Installing 0.2.4 does not update saved calculations. Corrections affect some fit statistics, bias/fair-score calculations, uncertainty and comparisons. Keep your original inputs, fitted objects and settings, then use the updating guide to identify which results need rebuilding. help("mfrmr_workflow_methods") explains the applicable checks; not every correction requires refitting.

Simulation files also check the package implementation and numerical environment before loading or resuming a run. An implementation change can make an older file incompatible even when its stored numerical values remain readable. Retain the original package installation and environment to continue that run; use a new file path when recomputing after an update. The default file_refit = "never" rejects mismatches without replacing the saved file.

Use plot() on result objects instead of retired standalone drawing helpers; help("mfrmr_plot_migration") lists replacements. Model comparisons now reject likelihood-ratio tests when the larger model omits terms from the smaller one; recreate affected comparisons from the existing fits.

The detailed change record is also installed at system.file("NEWS-0.2.4-details.md", package = "mfrmr").

mfrmr 0.2.3.1

mfrmr 0.2.3.1 was a focused CRAN maintenance release. It did not change the public R API or fitted-model contracts introduced in 0.2.3.

• Corrected the compiled-header configuration used under link-time optimization so all C++ translation units use R’s configured Rboolean definition.
• Removed expired FACETS/Winsteps hyperlinks while retaining the substantive model distinctions and bibliographic references.

mfrmr 0.2.3

mfrmr 0.2.3 improves the reliability and interpretation of the existing many-facet workflows, with particular emphasis on the bounded GPCM introduced before this release. No existing exported function has been removed.

Bounded GPCM

• Clarified the supported GPCM as an aligned, single-owner relative-slope model. A slope multiplies the complete adjacent-category predictor, including the ability, facet, interaction, and owned-step terms. This is distinct from loading-only GPCM variants and multiplicative generalized MFRM families.
• Corrected the default scale identification for GPCM marginal maximum likelihood fits. When no population formula is supplied, an intercept-only population model now provides the latent location and scale while relative slopes use their documented geometric-mean constraint.
• Added parameter-level status for free GPCM slopes. Standard errors and confidence intervals are reported as ordinary inferential results only when the fitted solution and the parameter both satisfy the relevant readiness checks. Optimizer- and Hessian-based quantities remain available as clearly labelled diagnostic evidence when formal inference is not supported.
• Improved handling of extreme response patterns. Free JML Person estimates remain infinite for all-minimum or all-maximum responses; finite optimizer values are computational traces. Optional adjusted-score tables and plot placements do not replace the fitted estimates. Prior-regularized marginal EAP estimates remain available but do not override a blocked source fit.
• Added gpcm_mml_quadrature_sensitivity() for explicit same-data comparison of a fitted GPCM-MML result across quadrature grids. It reports changes in the marginal likelihood, relative slopes, raw observed-information quantities, population scale, and fitted probabilities without silently refitting during summary() or print(). Results support summary(), print(), as.data.frame(), and apa_table().

Estimation and readiness

• Added pre-fit checks for category and step support and strengthened constrained-estimability checks for sparse many-facet designs. Unsupported step contrasts and structurally unidentified comparisons now stop or fail closed before they can be presented as ordinary estimates.
• Strengthened numerical and boundary reporting for JML and GPCM fits. Optimizer convergence, terminal gradients, local rank, curvature, boundary evidence, and formal inference readiness are retained as separate concepts.
• Corrected the alignment of MML Person EAP estimates and posterior standard deviations when observations or Person rows are filtered during fitting.
• Fit-level readiness is propagated to Person results, plots, model-choice reviews, reports, and exported tables. A finite value from a blocked fit is no longer presented as an unrestricted inferential estimate.

Summaries, diagnostics, and plots

• print() and summary() for fitted models now begin with a plain-language interpretation status, the availability of formal inference, the main reason for any hold, and a suggested next action.
• Formal-inference reporting now distinguishes satisfied fit-readiness requirements from the separate precision contract. A fit-only summary does not claim formal inference until matching diagnostics support the standard-error, confidence-interval, and reliability basis.
• GPCM uncertainty labels are consistent across fitted objects, summaries, and plots. Intervals calculated for observation-table Wright or facet displays are labelled screening_only and are not described as confidence intervals.
• PCM/GPCM model-choice reviews retain comparison warnings and keep information criteria, automatic preferences, and likelihood-ratio results unavailable when the compared fits do not share an eligible basis. Model-choice warnings can also be included in exported summary appendices.
• Differential-facet-functioning refits now replay the baseline response family, scoring range, estimator, weighting, and numerical settings. They fail closed for model structures that do not yet have a valid subgroup replay and linking contract. min_obs remains a computability guard rather than a claim about power or adequate sample size.
• Residual-PCA results now carry machine-readable exploratory-use guards. Returned components and warnings are descriptive diagnostics and do not automatically support dimensionality or subscore decisions.
• Corrected Person-involving bias-screen collection for explicitly requested facet pairs and expanded machine-readable FACETS feature-coverage guidance.
• FACETS table import retains the reported numeric text and displayed decimal precision so rounded output is not silently treated as hidden full-precision data.
• The Infit-versus-measure pathway now places Infit on the horizontal axis and the fitted measure on the vertical axis by default. GPCM category curves retain estimated step-facet slopes and now state explicitly that additive facet effects and interactions are fixed at zero. Multiple GPCM curve groups are shown in separate panels with category legends instead of as unlabelled overlaid traces.
• Native Wright maps label every retained facet level using displaced text and leader lines. Step thresholds are displayed as a vertical ladder with the score transition and fitted logit in each label.

Reporting and reproducibility

• Reproducibility manifests describe inputs through semantic summaries such as class, dimensions, fields, and missingness instead of environment-sensitive serialization hashes. The digest package is no longer required by the distributed package.
• Reporting, APA tables, model summaries, and plots use the same readiness and estimate-use vocabulary. Added fields may affect code that assumes an exact number of rows or columns in a summary component; selecting fields by name is recommended.
• APA output is described as an APA/JARS-informed drafting aid rather than a compliance certificate. The reporting guide now lists study-level fields that the fitted model cannot supply, and weighted-method prose distinguishes row-level likelihood weighting from replicated Person response patterns.
• External programs are not required to install or use mfrmr. Comparisons with FACETS, ConQuest, TAM, or immer remain model- and estimator-specific and are not general interchangeability claims.

Scope

• This release does not add unrestricted GPCM, loading-only or multiplicative generalized MFRM estimation, native multidimensional estimation, or new public multivariate G-theory and D-study functionality.

mfrmr 0.2.2

• Standardized the package’s canonical joint-maximum-likelihood label as "JML" across fitted objects, engine state, manifests, and replay scripts. method = "JMLE" remains accepted only as a backward-compatible input alias and now resolves immediately to "JML".

• Revised first-contact guides and result guidance to use reader-facing wording while retaining documented API and status vocabulary.

• Clarified that maxit is a prespecified computational ceiling rather than a result-selection control. Iteration-limited fits now direct users to keep the specification fixed, follow a prespecified ceiling sequence, and withhold interpretation until the numerical-readiness criteria are satisfied.

• Replaced blanket \dontrun{} and @examplesIf interactive() guards with checkable examples or \donttest{} blocks. Only the two workflows that need separately generated ConQuest files remain \dontrun{}, and only the local Shiny viewer remains interactive-only. This makes most examples executable while keeping workflows that require separately generated files clearly marked.

• Corrected bounded-GPCM score-side delta-method uncertainty to use the expected-score derivative ScoreSlope * Var. ScoreSideLogitSE remains the logit-side component SE, while ScoreSideSE and its interval columns now apply ScoreSlope * Var * ScoreSideLogitSE on the expected-score scale.

• Refit DFF/DIF contrasts are now explicitly exploratory: separate-subgroup plug-in standard errors are labeled as conditional on baseline anchors and as omitting baseline-anchor uncertainty and cross-refit covariance. Refit rows no longer receive ETS A/B/C, formal-inference, or primary-reporting eligibility.

• Bias summaries and multi-pair bias collections now use ScreenPositive as the primary label and expose explicit screening-only eligibility metadata. Historical Significant names remain as compatibility aliases.

• import_erm_fit() now reads the current eRm Person Parameter / Std.Error schema as well as historical estimate labels, preserves usable person IDs, and rejects ambiguous or misaligned schemas instead of silently returning empty or recycled person rows.

• q3_statistic() and its print method now identify the result as mfrmr’s standardized, Person-by-level aggregated-residual Q3-style screen. Legacy YenFlag names remain for compatibility, while fixed 0.20/0.30 rules are explicitly described as uncalibrated heuristics rather than raw-residual Yen Q3 critical values.

• as_kable.apa_table(format = "pipe") now appends an APA note once after the complete Markdown table. Previously the vectorized append could repeat the same note after every rendered table line.

• Added the package hex sticker.

• Tightened the FACETS positioning contract against the current 64-bit 4.5.1 software target: coverage rows describe package-native surfaces, not external numerical equivalence, and mixed models, multiple scales, threshold anchoring, and fixed-calibration scoring remain outside 0.2.2.

• Corrected the interrater_agreement_table() documentation: ExpectedExact is computed from fitted category-probability vectors, not marginal-frequency chance agreement.

• Clarified that exact Person-by-facet duplicate rows are retained but place Data readiness under review; legitimate repeated ratings should carry a distinguishing event or occasion facet.

• Design, signal-detection, and population-prediction summaries now expose a deterministic named-facet review as structural_design_review. The review reports design balance, coverage, connectivity, and readiness without implying Monte Carlo performance or arbitrary-facet simulation support.

Estimation performance

• A code-zero solution whose terminal gradient still requires review now triggers a bounded warm-started polish ladder when the portable tolerance setting is at least as strict as the public default. Each stage records its optimizer, portable setting, native L-BFGS-B controls when applicable, objective, terminal gradient, maximum parameter change, evaluations, and elapsed time; the best non-worsening stage is retained rather than assuming that stricter controls improve every fit monotonically.
• Direct, hybrid, and EM MML engines now apply the same terminal-gradient check to InferenceReady. EM relative log-likelihood convergence remains visible as an engine-specific stopping condition but no longer overrides the common numerical-readiness contract.
• fit_mfrm() now shares likelihood and analytical-gradient work at an identical parameter vector. MML direct and EM paths reuse quadrature probabilities and posterior quantities, while JML reuses category probabilities and stable observed log probabilities.
• The compiled cpp11 probability kernels are now the default for supported RSM/PCM MML work, with automatic pure-R fallback. Set options(mfrmr.use_cpp11_backend = FALSE) for an explicit reference-path comparison; GPCM continues to use its validated R kernel.
• optimizer = "auto" selects limited-memory L-BFGS-B for MML and for large JML parameter vectors; "BFGS" and "L-BFGS-B" remain explicit choices. The requested and actual methods are recorded for summaries, exports, and replay. The portable reltol setting is mapped to L-BFGS-B factr and pgtol; actual stage controls are recorded alongside the requested and selected-stage settings.
• Repeated likelihood calculations reuse work within each fit without enlarging saved fit objects or sharing cached values across parallel fits.
• Measurement-graph component detection now avoids repeated row-wise lookups while preserving the established subset labels and ordering. This reduces first-fit overhead for larger long-format rating designs.

Summary workflow

• Fit summaries now separate Numerical, Data, Design, Stability, Diagnostics, and Reporting readiness. Disconnected measurement graphs and boundary-constant or single-level facet support remain explicit reporting holds even when numerical optimization succeeds.
• Wright, FACETS-style Wright, pathway, and related fit plots carry additive fit-readiness metadata. Review-only displays remain available for diagnosis but warn, mark their returned subtitle and drawn title, and do not silently promote availability to interpretability.
• plot_apa_figure_one() now emits one consolidated readiness warning per call, retains the readiness table and interpretation note on the composite, and visibly labels a non-ready result as a manuscript-oriented draft for review rather than a finished publication figure.
• Native and FACETS-style Wright maps now share a robust automatic range when boundary-separated facet levels are diagnosed. Exact estimates and CI bounds remain in the returned tables; ruler-end triangles, clipping metadata, and plot footers prevent truncated intervals from being read as complete, while the native returned legend uses the same keys as the rendered legend.
• summary(fit) now supports profile = "fit", "facets", and "reporting". The default fit profile remains fast and does not compute diagnostics. The opt-in FACETS profile organizes fitted measures, fit, precision, categories, steps, and plot routes in a familiar reading order; it does not imply that FACETS was run or that its estimates are numerically equivalent.
• FACETS and reporting profiles can reuse a matching mfrm_diagnostics object. The returned summary records provenance and section availability, and compute = "never" prevents automatic diagnostic computation.
• Bias/DIF, residual PCA, and anchor-drift or linking analyses remain explicit follow-up decisions because their interpretation depends on the study design.
• detail = "brief" gives a selective console view without person identifiers. Full structured results remain available through the returned object.
• The concise summary presents the visual workflow in order: the required native Wright map with facet uncertainty and labelled step locations, the optional FACETS-style Wright ruler, and the optional Infit pathway. Person rows in the pathway remain opt-in.

Examples and teaching data

• example_operational adds a reproducible 48-person teaching dataset with a connected two-rater assignment, moderate workload imbalance, and six planned omissions. It is the primary applied tutorial dataset; example_core remains an explicitly idealized complete-crossing example, and example_bias remains the planted-effect diagnostic example.
• mfrmr_example_operational_design declares the 288 planned assignment cells separately from the 282 observed scores. describe_mfrm_data() can compare an explicit expected_design with observed cells, report planned omissions and unexpected observations, review Person-facet graph components, summarize sparse links and duplicate cells, and keep person labels out of its default compact output. Without a roster, structural missingness is reported as not assessed rather than inferred from a hypothetical complete crossing.
• list_mfrmr_data(details = TRUE) now explains the design and intended role of every bundled synthetic dataset. The compact examples can be regenerated with fixed seeds. Combined-study objects now explain that relabeling prevents identifier collisions but does not establish a common scale without an explicit anchor/linking design.
• Precomputed vignette tables now follow the same successful operational MML route as the displayed workflow and record their source dataset, schema, MD5 checksum, and package version.

Safer first analyses

• The public default remains reltol = 1e-9 for the initial optimizer stage; bounded polishing is invoked only when reltol <= 1e-9 and code zero precedes the terminal-gradient check. The fitted object records requested and selected-stage controls for replay. Model specification, design, identification, and inferential assumptions remain separate review questions.

• Non-finite scores or weights, blank person/facet identifiers, and fractional maxit or quad_points values now fail before expensive optimization with a focused correction. Duplicate Person-by-facet cells warn once per fit, report both affected rows and duplicate cells, and propagate a Data review state downstream.

• missing_codes = TRUE now applies the conventional sentinel set to scores while preserving person and facet IDs. An explicit character vector remains an explicit request to apply those codes across all selected model columns; the review records the scope used for each column.

• On-the-fly ConQuest overlap examples now use the same 1e-9 tolerance. Their bundle summaries, settings, written README files, and compact console summaries report the actual mfrmr fit controls, MML engine, terminal gradient, convergence state, and inference readiness. A fit requiring convergence review is clearly withheld from the external comparison step.

• fit_mfrm() now gives focused guidance for common undeclared missing-value codes and records score-category recoding in the fitted object. It also distinguishes an explicitly silent anchor policy from policies that report anchor review information.

• Partial-credit fits infer the step facet only when a familiar item-like role is unambiguous. Otherwise, the warning shows how to set step_facet explicitly. Rating-scale fits report that step_facet and slope_facet are not used.

• Direct data-frame input to mfrm_results() is limited to data with recognizable measurement roles. Ambiguous columns now lead to an explicit fit_mfrm(..., method = "MML") instruction instead of a guessed analysis.

• describe_mfrm_data() computes agreement automatically only when a rater-like facet is present. Agreement output names the facet actually used and avoids presenting a generic facet as a rater.

• Latent regression rejects a non-person-centered parameterization that would confound the population intercept with the measurement scale. ## Interpretation and compatibility boundaries

• Optimizer code zero is no longer treated as sufficient evidence of a clean solution when the terminal gradient remains large. Summaries label this state as requiring review and explain the diagnostic basis.

• facets_feature_coverage() and gpcm_capability_matrix() now present concise user-facing capability, limitation, and recommended-route information. Only documented user-facing columns are returned.

• FACETS-style plots reproduce a reading convention, not FACETS numerical estimation. ConQuest comparison helpers cover documented unidimensional MML overlap and do not automate ConQuest or claim general numerical equivalence.

• The generated ConQuest overlap command now states quadrature MML explicitly and requests four machine-readable CSV outputs. normalize_conquest_overlap_exports() reads those files, reconstructs the sum-constrained item location, trims fixed-width person identifiers, and prepares them for review_conquest_overlap().

• The documented binary, item-only, one-covariate MML handoff was compared in a matched 31-node run with ConQuest 5.47.5 Demonstration Version. The result supports that narrow handoff and is not a claim of general numerical equivalence.

• export_mfrm_results() now labels every preset as a potentially identifying analysis archive, warns before writing unless the risk is explicitly acknowledged, and records privacy status in its summary, HTML index, and written-files manifest. Fit-level export_mfrm_bundle() archives follow the same warning and metadata contract, and the lower-level export_mfrm_tables() CSV writer now records per-file handling metadata. ConQuest overlap bundles likewise warn on file export and include an artifact-level privacy inventory for response, covariate, and case-EAP files.

First-use workflow

• The recommended workflow is now data -> fit_mfrm() -> fit summary -> required Wright map -> focused diagnostics -> mfrm_report() or export_mfrm_results().
• summary(mfrm_results(...), view = "brief") and summary(mfrm_report(...), view = "reader") provide stable, concise views over the corresponding structured objects.
• export_mfrm_results(preset = "starter") writes a reader-first result folder with an index, required Wright-map image, selected tables, report files, replay code, and a reproducibility manifest.
• The README, workflow vignette, and help pages now begin with the same compact analysis route and direct specialist questions to focused follow-up helpers.

Wright maps and fit pathways

• Wright maps retain the native renderer as the default. Native maps can show facet SE or confidence-interval whiskers alongside fitted step locations with show_ci = TRUE, while fitted coordinates remain unchanged.
• renderer = "facets" adds an opt-in FACETS Table 6-style visual grammar: a shared logit ruler, person-frequency asterisks, signed facet columns, all fitted facet levels, horizontal score-transition lines, and optional rubric labels. The renderer reproduces a display convention, not FACETS estimation or numerical output.
• Both Wright renderers return tidy draw-free data for custom graphics. The native top_n display remains compact; the FACETS-style data retain every fitted location.
• plot(..., type = "fit_pathway") adds a separate fit-oriented display with Infit or Outfit on the x-axis and measure logits on the y-axis. Screening bands, measure intervals, and optional ZSTD companions are explicit.
• Person rows can be added to the fit pathway with bounded selection and independent person/facet label controls. The existing expected-score type = "pathway" is unchanged.

Reporting and migration support

• facets_term_crosswalk() and facets_visual_contract() document the correspondence between FACETS terminology and mfrmr outputs while keeping visual compatibility separate from numerical equivalence.
• plot_data(), plot_data_components(), and as_ggplot() make plot coordinates, annotations, reference lines, and guidance available for custom R graphics.
• Plot helpers consistently support preset = "monochrome" for print-friendly figures.
• export_mfrm_bundle(..., include = "html") provides a fit-level HTML/CSV/replay bundle without first creating an mfrm_results object.
• Model-comparison output can be routed through build_model_choice_review() and build_summary_table_bundle(), with explicit guidance for equal-weighting RSM/PCM models, bounded GPCM sensitivity analyses, and latent-regression reporting.

mfrmr 0.2.1

Results, reports, and export

• mfrm_results() adds a comprehensive first-screen object for an existing fit, a run_mfrm_facets() result, or a long-format data frame. It gathers diagnostics, available tables, plot routes, status information, next actions, and reproducible code without replacing the lower-level helpers.
• mfrm_results(include = ...) supports purpose presets for publication, FACETS migration, validation, bias, local misfit, linking, network review, and bounded GPCM review.
• mfrm_report() converts an mfrm_results object into a navigable reporting plan. Its first screen, report index, template index, evidence boundaries, cautious wording, and next actions keep detailed tables available without turning diagnostics into pass/fail decisions.
• export_mfrm_results() writes selected result tables, report files, draw-free plot data, images, replay code, RDS output, and a written-files manifest. export_mfrm_bundle() remains the broader fit-centered archive.
• launch_mfrmr_viewer() provides an optional Shiny reader over an existing mfrm_results object. It displays stored results and does not refit the model or change diagnostics.
• mfrmr_output_guide("public") maps the shortest fit, results, report, viewer, export, and specialist routes. Additional guides cover FACETS, ConQuest, binary data, simulation, linking, response time, and R-first visualization.

Interpretation and reporting accuracy

• APA output now describes mean-square fit relative to the selected screening band instead of labeling overall fit “acceptable” or “elevated.” Band position is presented as a review signal, not a validity decision.
• MML reports state that person measures are EAP estimates and that residual-based fit statistics are evaluated at those measures. Comparisons intended to match JMLE-based FACETS output should use method = "JML" and aligned settings.
• Small-df ZSTD values are withheld when the transformation is unstable. FACETS/Winsteps output may still show a value under different sparse-cell conventions; such pairs are labeled as availability or standardization differences rather than automatically as fit differences.
• MML person separation and reliability are based on EAP measures and posterior SDs. They are kept distinct from JMLE-based FACETS reliability and from observed inter-rater agreement.
• APA tables and narratives report the measure-CI basis and fitted sign convention when available. Separation reliability, agreement, fit, and validity remain separate reporting claims.
• precision_review_report(), fit_measures_table(), and facets_fit_review() expose the fit, ZSTD, df, separation, and uncertainty bases needed before drafting technical conclusions.

Focused review and planning

• Result and report objects can carry explicit bias, local-misfit/pathway, linking/anchor, precision, network, and response-time sections. Missing or unrequested sections remain visible as such.
• Recovery summaries expose reading_order, condition_review, and fit/separation operating characteristics. Bounded-GPCM slope_regime labels and extended sensitivity evidence remain separate from recovery metrics, convergence, and uncertainty availability; they are not automatic adequacy decisions.
• Resampling and simulation tools add person-clustered subsampling/bootstrap, sparse linked rating designs, connectedness summaries, and peer-assessment assignment checks. These are stability, design, or operating-characteristic diagnostics rather than calibrated tests or automatic decisions.

mfrmr 0.2.0

Scope and compatibility

• This version strengthens mathematical identification, uncertainty reporting, diagnostic tables, recovery tools, and draw-free visual output for RSM, PCM, and the documented bounded GPCM implementation.
• Breaking change: former exported *_audit* helper names, compatibility classes, and duplicate output fields were removed in favor of the canonical *_review* names. Stable accessors include anchor_review() and precision_review().
• facets_positioning_guide(), facets_feature_coverage(), and facets_output_contract_review() describe supported FACETS-style tables, migration routes, and known differences. mfrmr estimates remain package-native unless external FACETS output is supplied for comparison.
• mfrmr_output_guide("facets"), mfrmr_output_guide("conquest"), and mfrmr_output_guide("r") provide focused entry points for users moving from FACETS or ConQuest and for users who want reusable R plot data.
• write_mfrm_residual_file() and write_mfrm_subset_file() add standalone residual and connected-subset files for external review.

Estimation and fit statistics

• RSM, PCM, and bounded GPCM step profiles now use the correct sum-to-zero parameter count. MML structural covariance output provides uncertainty for non-person facets, steps, and bounded-GPCM slopes when the observed information is available.
• Measure tables record confidence level, interval method, eligibility, and interpretation basis. compare_mfrm() records the BIC sample-size basis, including weighted fits, and withholds unsupported likelihood-ratio tests with an explicit reason.
• Bounded-GPCM simulation and fitting use the same geometric-mean-one relative-slope identification. Expected scores, information, category curves, fair averages, and bias screening use slope-aware probabilities.
• fair_average_table(fair_se = TRUE) adds structural delta-method uncertainty where supported. estimate_bias() uses slope-aware information and can report conditional profile-likelihood screening quantities.
• diagnose_mfrm(fit_df_method = "engine" | "facets" | "both") exposes the package and FACETS-style df/ZSTD conventions separately. facets_fit_review() and read_facets_fit_table() support row-aligned comparison with existing FACETS tables without treating convention differences as estimation errors.
• compute_person_fit_indices() computes polytomous lz from observed category probabilities. Snijders-corrected lz_star is reported for compatible JML/fixed-effect person estimates and remains unavailable for MML/EAP scores. The incorrectly named ECI4 output was removed; use OutfitZSTD for the corresponding standardized chi-square quantity.

Diagnostics and visualization

• fit_measures_table() adds FACETS-style element fit tables, configurable threshold profiles, measure intervals, df-sensitivity summaries, and draw-free fit plots.
• data_quality_report() reports row retention, score-support gaps, zero/sparse category use by facet level, restricted response patterns, quality flags, original-to-internal score mapping, and dashboard plot data.
• analyze_residual_pca(parallel = TRUE) adds residual-permutation parallel analysis and dedicated plots. It remains exploratory dimensionality evidence.
• category_curves_report() adds category probabilities, cumulative probabilities, total information, category-specific information, boundary summaries, and overview/focused plots.
• plot_data() and plot_data_components() expose long-form data, annotations, styles, and settings from supported draw = FALSE plots; monochrome and interval guides support print-oriented reporting.
• Response-time QC, design connectedness, rater-effect networks, and halo screening receive dedicated summaries and plots. They remain descriptive evidence, not speed parameters, logit estimates, or automatic exclusions.
• mfrm_d_study() extends observed-score generalizability output to planned rater/facet-count comparisons. Its residual-scaling assumptions are reported explicitly; it is not a substitute for an unidentified interaction decomposition.

Recovery, model choice, and reporting

• evaluate_mfrm_recovery() and assess_mfrm_recovery() report parameter recovery, convergence, coverage, Monte Carlo precision, uncertainty availability, score support, and user-specified practical thresholds in separate summaries and plots.
• build_model_choice_review() combines fitted-model comparisons, model-role guidance, downstream support, cautious wording, and optional weighting review for RSM, PCM, and bounded GPCM candidates.
• build_summary_table_bundle() and export_summary_appendix() accept a broader set of fit, recovery, person-fit, precision, and comparison objects for report and appendix handoff.
• DIF plots add comparable scales, value labels, flag thresholds, confidence intervals, and interpretation metadata. Input validation for DFF/DIF helpers now fails earlier with clearer messages.
• Citation and interpretation corrections clarify mean-square screening ranges, Q3 residual conventions, sample-size guidance, ICC bands, shrinkage uncertainty, and the limits of pairwise bias SE approximations.

Bounded GPCM boundary

• gpcm_capability_matrix() is the authoritative support map. Supported, caveated, and unavailable routes include a recommended alternative and the evidence needed for broader use.
• Direct fitting, posterior scoring, information, category plots, recovery, fair averages, conditional bias screening, and selected reporting/planning helpers are available where marked.
• Full unrestricted discrimination structures, full FACETS score-side equivalence, posterior-predictive checks, and heavy Bayesian backends are outside this version’s supported scope.
• Structured mfrmr_gpcm_scope_error conditions identify the unsupported area and recommended route instead of returning a partial result.

Defaults and performance

• No defaults changed from 0.1.6: quad_points = 31, diagnostic_mode = "both", plot(fit) showing the Wright map, and keep_original = FALSE.
• Users upgrading directly from 0.1.5 should note that these defaults were introduced in 0.1.6.
• The cpp11 MML backend is used by default for supported RSM and PCM work; options(mfrmr.use_cpp11_backend = FALSE) selects the pure-R reference path. Unsupported kernels fall back automatically.

0.1.6

• Changed the default diagnostic mode from legacy-only to both legacy and strict-marginal diagnostics, increased MML quadrature points from 15 to 31, and made the Wright map the default plot(fit) output. The former overview remains available with type = "bundle".
• Added estimated facet interactions, empirical-Bayes shrinkage, hierarchical/sample-adequacy review, missing-code preprocessing, APA output adapters, confidence intervals across major plots, Q3 diagnostics, expanded person-fit indices, observed-score generalizability helpers, import adapters for mirt/TAM/eRm, resumable MML fits, and additional diagnostic plots.
• Improved fit summaries, replay scripts, input validation, examples, large-design diagnostics, and the printable cheatsheet.

0.1.5

• Simplified the first-use fit, diagnostic, and reporting workflow.
• Added MML latent regression with EAP scoring, the first bounded-GPCM fitting route, binary and non-consecutive score support, strict-marginal follow-up plots, report/appendix helpers, and clearer uncertainty and support boundaries.
• Added focused overlap and handoff guidance for FACETS, ConQuest, mirt, TAM, and eRm.

0.1.4 to 0.1.1

• Improved metadata, references, help-page examples, output documentation, and cross-platform portability while preserving the public analysis workflow.

0.1.0

• Introduced package-native many-facet RSM/PCM estimation with MML and JML, arbitrary facet counts, FACETS-style bias and fixed-width reports, APA-oriented summaries, residual-PCA diagnostics, visual summaries, anchoring helpers, and synthetic example data.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.