rasch 1.12.0

Models and inference

• wright_map() sends fitted person and item estimates to WrightMap. It supports several person distributions and the item-panel layout introduced in WrightMap 1.5, including the person-group and item-set structure of EFRM fits.

• rasch_explanatory() fits the linear logistic test model and linear partial credit model from continuous, categorical or ordinal item- or threshold-level predictors. Formulae may include selected interactions. explanatory_test() compares the restrictions with a free calibration using the Kent adjustment; explanatory_diagnostics() and relax_explanatory() support fixed item and threshold departures. Refitted departures propagate to item and person estimates and are retained through item deletion, DIF splitting, superitem construction and response-dependence resolution. Keyed option responses remain available after item deletion, splitting and fixed-departure refits.

• btl_explanatory() applies a fixed explanatory design to object locations in dichotomous or ordered comparative judgements. It supports the same model comparison, Holm-adjusted diagnostics and fixed departures while retaining the nominated ordered-response threshold structure.

• A worked case study on the documentation site uses the verbal aggression data to develop and check an explanatory partial credit model.

• explanatory_test() now places the Kent-calibrated probability in both p and p_kent. The unscaled composite-likelihood probability is named p_naive so it cannot be mistaken for the inferential result. The table also reports calibration R-squared, with an adjusted counterpart whose null expectation is near zero, against the free threshold or object calibration.

• Pairwise conditional calibrations now use the remaining Newton move as a second convergence check. This prevents numerical false refusals at large sample sizes without changing the estimates.

• Holm adjustment for item-fit statistics now excludes items whose tests are unavailable. Their probabilities remain NA.

• Sparse-unit safeguards now use the sampling units that inform each test. MFRM interaction tests use the least-supported facet level; EFRM unit tests require adequate persons on every group or set link; BTL-EFRM judge bootstraps require adequate effective judges in every panel or link; and frame-invariance tests exclude weak frame calibrations.

• BTL-EFRM judge bootstraps now distinguish refit errors from non-convergence and report the underlying worker error when parallel refits fail.

• rasch_mfrm() supports several facets and an optional item-by-facet interaction. Omnibus and cell follow-up tests use the fitted joint covariance.

• rasch_efrm() supports crossed person-group factors and reports their GLS factorial decomposition. Set-unit linking uses a finite-grid semiparametric likelihood, with a separate nuisance distribution for each observed person group. Hybrid standard errors retain the joint uncertainty of the within-frame calibration and set link; full person-bootstrap inference remains available. The convergence flag covers both estimation stages, and non-converged links are excluded from bootstrap covariance calculations. EFRM data require one response row per person.

• The repeated semiparametric linking calculations in the EFRM bootstrap now use a compiled numerical kernel. Bootstrap replicates can also be distributed over a reproducible, cross-platform worker cluster. The Shiny application runs EFRM fits in a background process, defaults to four workers where the system permits, records the bootstrap seed, reports progress and permits the fit to be cancelled without retaining a partial result.

• BTL-EFRM judge bootstraps likewise default to four workers where available. A fixed seed gives the same result for any worker count. The application runs these fits in the background and supports progress reporting and cancellation.

• frame_invariance() compares item locations and discrimination across separately calibrated frames. The conditional method tests locations and reports discrimination descriptively. The person-within-frame bootstrap provides inference for both, with one combined Holm family.

• MFRM and EFRM summaries report item estimates separately from the item-by-facet or item-by-frame response cells used in estimation. Coefficient alpha is not reported for the expanded response-cell matrix. EFRM DIF tests pool residual evidence by item and exclude the person factors that define the frames.

• btl(), btl_dif() and btl_efrm() add ordered paired comparisons, judge-clustered inference, judge-factor DIF, linked object sets and judge panels. Paired-comparison diagnostics now include equating, transitivity, residual dimensions, design information and adaptive pair selection.

• Carry-over probabilities are withheld below 30 judges. btl_equate() uses Welch–Satterthwaite degrees of freedom when fitted calibrations have a finite number of judge clusters. Conditional BTL-EFRM unit probabilities are withheld; the application defaults to the judge bootstrap. BTL-EFRM judge bootstraps use finite-judge references, whereas its independent-outcome parametric bootstrap uses normal and chi-square references.

Differential item functioning

• Confirmatory multiplicity defaults are now consistently Holm familywise adjustments across item fit, DIF, equating and the application. BH remains available where false-discovery-rate screening is explicitly requested. BTL DIF uses HC3 covariance for unequal judge workloads and withholds omnibus probabilities below eight judges or eight effective judges in a factor cell.

• Item-fit documentation now distinguishes the principal item-trait test from the supplementary class-interval ANOVA and notes the limits of both in short administrations. HC3 was evaluated for item fit and was not adopted.

• dif_anova() fits several person factors jointly using Type II sums of squares. Repeated measurements use the person as the sampling unit and separate between- and within-person error strata. Multiplicity adjustment covers the complete family of uniform and non-uniform DIF tests rather than treating each term as a separate family; btl_dif() follows the same rule. Uniform between-person terms now use HC3 covariance. Class-interval interactions retain the residual-ANOVA reference used for non-uniform DIF.

• dif_contrasts() and dif_posthoc() provide planned and post-hoc logit contrasts, including simple effects and difference-in-differences for interactions. MFRM follow-ups pool the fitted facet cells of an underlying item; resolved EFRM follow-ups are withheld because an ordinary split would discard the frame units. The residual-mean Tukey table has been removed from dif_anova(); dif_posthoc() is the supported follow-up for multilevel terms.

• Repeated-measures DIF follow-ups use the full design-cell weights in their person-level tests. Reported resolved estimates and probabilities therefore address the same marginal contrast when nuisance factors are imbalanced.

• dif_size() reports resolved pairwise logit differences. Dichotomous items receive the itemwise ETS A/B/C classification. Polytomous items report the PCM signed expected-score area descriptively, without importing an incompatible score-metric classification.

• resolve_dif() splits confirmed DIF items iteratively while retaining a minimum anchor set. Automatic splitting is restricted to uniform DIF; non-uniform DIF remains visible for item review. MFRM residuals can be pooled to their source items, and EFRM factors that do not define frames can be tested.

• btl_dif() retains anchors and fitted dependence terms in its resolution refit. Resolved pairwise inference is withheld unless each factor cell has at least eight effective judges; pairwise degrees of freedom use the two cells’ effective counts, and the pairwise table reports the raw and effective support for both cells. BTL-EFRM fits require a frame-specific analysis rather than the equal-unit resolution model.

Diagnostics and model changes

• Identification checks now cover item, facet, frame and paired-comparison graphs, rank, separation and sparse categories. Unidentified estimates are refused; identified but weak estimates are marked or have inference withheld.
• dependence_magnitude() uses the joint covariance of resolved thresholds. Equating tests require independent calibrations and the covariance of banked locations.
• spread_test() applies the binomial least-upper-bound only to superitems formed entirely from dichotomous components. It now distinguishes a point estimate below the bound from adjusted one-sided evidence of dependence. Its significance level and multiplicity adjustment are available in the application. The component structure is retained through subsequent item splits and removals.
• The tailored-analysis bootstrap resamples complete persons, including all rows of a repeated-measures record.
• drop_items(), resolve_frames(), DIF splitting and superitem construction refit the active model and update downstream item and person estimates. Refit specifications retain anchors, keyed scoring, threshold constraints, factors and frame-linking controls; a non-converged downstream calibration is not returned as a completed analysis.
• Classical whole-test statistics and the Guttman scalogram are withheld when an item is represented by several facet or frame response cells. They remain available for a one-cell-per-item reduction.
• MFRM characteristic and information curves now combine facet conditions administered to the same person. Distinct rating designs receive separate curves rather than being added into a test no person received.
• Automatic model comparisons are available for the main model families. Structural changes are accompanied by before-and-after item and person summaries.

Application and documentation

• The Shiny application uses responsive control and result columns, compact explainers for outputs and options, scalable plots and downloadable tables. Plot controls sit below the plot, and related item curves may be overlaid.
• Analyses can be saved as .rasch projects and reopened. Reports can be produced as self-contained HTML, Word or PDF documents; the R code for each displayed result is available in the application.
• The application covers the extended model suite, including model comparison, DIF follow-ups, frame-invariance checks and refitted structural changes.
• The manuals and vignettes have been revised to state the fitted models, estimands, identification requirements and uncertainty methods directly.
• The shipped EFRM and BTL-EFRM case studies now use the current linking and uncertainty methods.
• plot_scree() and plot_btl_scree() label their component axes at whole components only, instead of overprinting the default axis.

rasch 1.11.7

• print() preserves the reference distribution used by saved BTL fits: current and transitional results are labelled t, while older results without cluster degrees of freedom retain their original z label.

rasch 1.11.6

• BTL print methods read both current and earlier dependence-statistic columns. Current clustered statistics are labelled t, in accordance with their t reference distribution.

rasch 1.11.5

• Ordered paired-comparison margins must be numeric or an ordered factor; unordered categorical margins are rejected.
• Invalid graded responses and malformed secondary-dimension specifications now produce errors rather than being coerced or dropped.
• Clustered dependence and position statistics are labelled t.

rasch 1.11.4

• Score validation reads factor labels and rejects non-integer, non-numeric, or non-finite values.
• Ordered BTL responses require an ordered factor or integer scores.
• Clustered dependence and position tests use a t reference with G - 1 degrees of freedom.
• simulate_rasch() validates secondary-trait correlations and item sets.

rasch 1.11.3

• All estimators share the same integer-score validation.
• item_moments() uses a log-sum-exp calculation for wide category ranges.
• BTL object-separation reliability is withheld when the clustered covariance is rank deficient.
• btl_efrm() requires each judge to belong to one panel.
• Undefined reliability and omnibus statistics are reported as NA.
• Secondary-trait simulation retains its requested mean and standard deviation, and MLE score-table calculations use the correct common-unit score equation.

rasch 1.11.2

• The BTL dimensionality reference simulates count-weighted data at the unordered-pair level and returns the leading strength from each replicate.
• Judge-bootstrap EFRM inference requires more than one judge per panel and notes panels with fewer than five judges.
• BTL fits report when the number of judge clusters cannot support a full-rank clustered covariance.

rasch 1.11.1

• BTL dimensionality reference draws now reproduce count-weighted binomial or multinomial sampling.
• Pairwise chi-square degrees of freedom include position and dependence parameters; untestable designs return NA.
• Judge-clustered covariance uses the CR1 small-sample factor.
• btl_efrm(se_method = "judge_bootstrap") resamples judges within panels and refits both stages.
• The interpretation of btl_next_pairs() as a ranking rule is stated in its documentation.

rasch 1.11.0

• Threshold and item-location covariance is transformed consistently after recentering items with different maximum scores.
• Warm WLE uses the common-discrimination score equation in which the common discrimination cancels.
• Untestable item-trait statistics return NA, and the total test includes testable items only.
• Equating drift tests account for the estimated origin shift and joint covariance of common-item locations.
• Judge-clustered inference requires more than one judge and reports a caution for fewer than ten clusters.
• btl_dif() uses the judge as the sampling unit and count-weighted opponent bands.
• rasch() and btl() warn when estimation has not converged.

rasch 1.10.3

• rasch() reports unknown id, factors, and items columns as errors.
• Fractional scores are rejected rather than truncated.
• MFRM rows with missing design identifiers are omitted with a note.
• equate_tests() excludes common items without usable locations or standard errors from weighted linking and drift inference.
• report_html() escapes data-derived labels and notes.

rasch 1.10.2

• The Shiny application adds consistent hover labels to scalograms, residual heatmaps, equating plots, tailored analysis, and paired-comparison displays.

rasch 1.10.1

• Person- and item-fit plots in the Shiny application show identifiers, locations, and fit residuals on hover.

rasch 1.10.0

• compare_fits() adds composite-likelihood AIC and BIC based on the Godambe effective parameter count.
• Model comparison now covers BTL position, threshold, and dependence specifications. The Shiny comparison page supports these fits.
• Optional cross-package tests compare results with eRm, sirt, and psychotools.

rasch 1.9.3

• Pairwise estimation now checks that the observed item graph, together with any anchors, identifies a common scale.
• Thresholds adjoining critically sparse categories are marked weak. Their threshold and item-location standard errors are withheld.

rasch 1.9.2

• Case study: inst/casestudies/party_blocs_crisis.R applies btl_efrm() to the Tuebingen 2009 party-preference data.
• Sets without stable panel-ratio information are excluded from the unit reconciliation and refitted at the reconciled panel units.
• Boundary-unstable bootstrap parameters receive NA standard errors with the number of boundary replicates reported.
• BTL-EFRM convergence is assessed by the gradient per comparison.

rasch 1.9.1

• The Shiny Frames page supports BTL-EFRM panel and object-set definitions, unit tables, frame fit, and unit plots.

rasch 1.9.0

• btl_efrm() fits the paired-comparison extension of the extended frame of reference model, with panel units, object-set units, and set origins.
• Bootstrap standard errors refit both stages. Conditional standard errors remain available for descriptive work.
• plot_btl_units() and simulate_btl_efrm() support display and simulation.

rasch 1.8.0

• btl() adds anchored estimation and a first-position effect.
• btl_equate() and plot_btl_equate() provide common-object linking and drift tests for paired-comparison calibrations.
• btl_information(), plot_btl_targeting(), and btl_next_pairs() provide design information and greedy next-pair selection.
• Count-weighted BTL sandwich covariance now reproduces expanded-data covariance. Equating uses each calibration’s stored covariance.
• The Shiny Targeting and Equating pages support paired comparisons.

rasch 1.7.1

• Rasch simulation layers now retain all previously specified DIF, dimensionality, response-style, dependence, and guessing terms.
• PCM simulation uses item-specific threshold structures and applies stricter validation to DIF, guessing, and disordered-threshold specifications.
• BTL dimensionality references include fitted within-judge dependence.
• MFRM simulation and recovery use the recorded item and rater parameters consistently.
• The Simulate page links recovery output to the current generated dataset.
• A simulation vignette and package logo were added.

rasch 1.7.0

• Simulation functions add population-distribution controls, response styles, speededness, and MFRM halo effects.
• sim_replicate(), sim_recovery(), and plot_recovery() support repeated simulation and parameter-recovery summaries.
• The Shiny Simulate page exposes the population controls and recovery output.

rasch 1.6.1

• The Shiny application adds a Simulate page for the four data layouts and their model-departure controls.

rasch 1.6.0

• simulate_efrm() gains n_categories for partial credit items within frames, with planted thresholds recorded in the truth attribute.
• simulate_rasch(), simulate_btl(), simulate_mfrm(), and simulate_efrm() generate data from the package’s model families and can introduce nominated departures. Generating parameters are stored in the returned data.

rasch 1.5.1

• plot_btl_judge_map() now displays individual matchups. judge_pair_surprise() returns the corresponding residuals.

rasch 1.5.0

• judge_surprise() and plot_btl_judge_map() compare a judge’s object-level preferences with the consensus object scale. The display is available from the Shiny Judge fit page.

rasch 1.4.0

• btl_transitivity() reports circular triads and Kendall’s consistency coefficient for suitable paired-comparison designs.
• btl_dimensionality() decomposes the skew-symmetric residual preference matrix and compares its leading component with a model-based reference.
• New plots display BTL transitivity, scree, and residual maps.

rasch 1.3.1

• The package was renamed from its development name, rmt, to rasch. Result classes use the rasch_ prefix.

rasch 1.3.0

• btl() adds count-weighted exposure and carry-over effects, separation handling, and plot_btl_dependence().
• btl_dif() carries fitted dependence effects into its residual analysis and handles aggregated comparison counts.
• Mixed-design DIF terms are evaluated in their corresponding error strata. Repeated-person logit contrasts are provided by dif_contrasts().
• Residual parallel analysis uses data simulated from the fitted model.
• The Shiny application retains the settings used by each DIF analysis and applies consistent fit flags.

rasch 1.2.0

• plot_pca_biplot() draws the item loadings on the first two residual principal components on equal axes.
• residual_correlations() now also returns the adjusted-Q3 star_matrix and plot_resid_cor() can draw raw Q3 or adjusted Q3*.
• The Shiny trait and local-dependence pages pair tables with their plots and allow the original data to be restored after restructuring.
• dif_anova() is now the single DIF analysis-of-variance function. One factor is analysed one-way; several factors are fitted jointly. It supports repeated-measures and mixed designs.
• resolve_dif() resolves DIF iteratively by item splitting.

rasch 1.0.0

First stable release.

Models

• rasch() fits dichotomous, partial credit, and rating scale models by pairwise conditional maximum likelihood, with Warm WLE person estimates.
• rasch_mfrm() fits additive and item-by-facet many-facet models.
• rasch_efrm() fits the extended frame of reference model.
• btl() fits dichotomous and ordered paired-comparison models.

Diagnostics

• Item and person fit, category functioning, targeting, reliability, test information, residual dimensionality, and local dependence.
• DIF analysis, item splitting, tailored analysis, common-item equating, and anchored calibration.
• BTL judge fit, invariance, and paired-comparison diagnostics.

Display and reporting

• Base-graphics functions cover item, person, threshold, targeting, information, residual, and paired-comparison displays.
• fit_summary_table() and targeting_table() return the headline statistics; save_outputs() and report_html() export results.
• run_app() launches the Shiny interface and shows the R call corresponding to each analysis.