NEWS.md

In easyRasch2: Psychometric Analysis with Rasch Measurement Theory

easyRasch2 1.3.0

New features

• New RMreliabilityCurve() plots conditional SEM, test information or conditional reliability across the latent scale, with the respondent distribution behind it. benchmark shades the region reaching a given reliability and reports the percentage of respondents inside it.

• New RMpersonChange() tests, per respondent, whether a person location moved between two occasions by more than measurement error allows. null selects the estimand, "measurement" (default) or "retest". Critical values come from the exact null by default, needing no simulation, and are well below 1.96 on short scales. retest_sd_tip reports how much occasion noise each flagged result tolerates. Single-respondent use needs item_params from an external calibration.

• New RMretestSD() estimates the per-occasion SD of occasion-to-occasion fluctuation from a test-retest study, for use as RMpersonChange(retest_sd =). Negative variance estimates are reported, not floored.

• RMtargeting() gains category_labels, row_gap, viridis_option, viridis_begin and viridis_end.

Breaking changes

• RMtargeting() now draws response-category bands in its bottom panel, with threshold estimates and confidence intervals as a coloured error-bar row beneath. Categories that are never most likely, and threshold reversals, are marked in red. Pass panel = "thresholds" for the previous dot-and-whisker panel. Estimates are unchanged.

• RMreliability()'s marginal reliability changes formula, and the row is renamed "Marginal (curve mean)". It is now the latent-density-weighted mean of sigma^2 / (sigma^2 + SEM(theta)^2) rather than Green's subtractive coefficient, which could fall below zero and was floored there. Results move upward, more so on short scales (phq9 .862 to .886, eRm::raschdat1[, 1:20] .695 to .769). The superseded value is the marginal_green attribute of RMreliabilityCurve(output = "dataframe").

Bug fixes

• RMdimCFACutoff() and RMdimCFA() failed when an item name was not a syntactic R name, such as Item 1. The CFA is now fitted under placeholder names and the loadings mapped back. Results for syntactic names are unchanged.

easyRasch2 1.2.0

Breaking changes

• RMitemInfit(), RMlocdepQ3() and RMlocdepGamma() now flag on the multiplicity-corrected p-value rather than on the simulated interval. p_value defaults to NULL, meaning TRUE when cutoff is the full cutoff object and FALSE otherwise, so calls that pass no cutoff or a bare cutoff table are unchanged. Flagging against an interval tests every item or pair at once and sets a family-wise error rate of 1 - width^m, which the corrected p-value targets directly instead. Tables gain the p_ and padj_ columns. Flagged items and pairs change. Pass p_value = FALSE for the old behaviour. Follows Johansson (2026), https://doi.org/10.31234/osf.io/7pqz4_v2.

• All three cutoff functions default to hdci_width = 0.95, was 0.999 in RMitemInfitCutoff() and 0.99 in RMlocdepQ3Cutoff() and RMlocdepGammaCutoff(), and the two local dependence cutoff functions default to iterations = 400, was 500 and 250. The interval is now a description of where a fitting statistic is expected to fall rather than a decision rule, and 400 is the count that width needs. Cutoff values change. RMitemInfitCutoffMI() keeps hdci_width = 0.999, since RMitemInfitMI() has no corrected-p-value path and the interval is still its decision rule there.

• RMlocdepGamma() now tests each item pair once, on the larger of its two conditioning directions. The p-value and the flag previously came from the canonical direction alone and were repeated in both tables, so the direction-2 table showed a coefficient beside a p-value computed from a different one, sometimes of the opposite sign. A new gamma_pair column carries the tested statistic and gamma still carries each direction's own coefficient. RMlocdepGammaCutoff() and RMlocdepGammaPlot() describe that maximum. Cutoff values, p-values and flags all change.

Bug fixes

• RMdimMartinLof() sampled dichotomous null patterns from the wrong distribution (since 1.0.0), which inflated the null distribution, gave a Type-I error near zero against a nominal 0.05 and badly reduced power. Polytomous data was unaffected. Dichotomous Martin-Löf p-values from 1.0.0 and 1.1.x should be recomputed.

• parallel = TRUE and parallel = FALSE now give identical results for the same seed, the per-iteration runners now pinning the generator that mirai workers otherwise change. Parallel results change, to agree with the sequential ones. Affects RMdifGammaCutoff(), RMdimCFACutoff(), RMdimMartinLof(), RMdimResidualPCACutoff(), RMitemInfitCutoff(), RMitemInfitCutoffMI(), RMitemRestscoreBoot(), RMlocdepGammaCutoff(), RMlocdepQ3Cutoff() and RMreliability().

• RMpersonFit(parallel = TRUE) was not reproducible from seed at all, its workers having had no per-task seed. Respondents are now seeded individually. Results change in both paths.

• RMreliability() with the default seed = NULL now reproduces from a session-level set.seed(), as the other simulation functions already did. The RMU row and the bootstrap intervals previously differed between two otherwise identical calls. Results for an explicit seed are unchanged.

• RMdimMartinLof() and RMdimMartinLofResiduals() no longer drop respondents for missingness on items outside partition. The 30-case minimum is likewise counted over the partitioned items only.

Other changes

• The reminder about iteration counts is now two-tier, reworded from calibration to reproducibility, and applies to item fit and both local dependence functions. Below 400 iterations a once-per-session message reports that the Westfall-Young correction is mildly liberal, and between 400 and 1000 the table caption notes that error rates are calibrated but decisions remain somewhat seed-dependent. A separate once-per-session message reports the family-wise error rate implied by the interval whenever flagging is interval-based.

• RMlocdepQ3() and RMlocdepGamma() warn when correction is "fdr_bh" or "fdr_by" and the bootstrap is too small for the procedure to reject anything. Over 36 item pairs Benjamini-Hochberg needs 720 iterations against 19 for the default Westfall-Young.

• RMlocdepGammaCutoff() is roughly 14 times faster, partial gamma now being computed by a vectorised internal rather than by iarm::partgam_LD(), which also derives an asymptotic standard error and confidence interval that a simulated null does not use.

• The two conditional samplers are now one recursion over the nested gamma functions, computed once per call rather than per simulated person, and about four times faster on a six-item scale. A given seed no longer reproduces the p-values of earlier versions.

• Captions now report simulated datasets that had to be discarded, so the iteration count a result rests on can be read against the count requested.

• The three cutoff objects gain requested_iterations, and RMdimMartinLof() gains sample_n_total and sample_has_na.

• RMitemICCPlot()'s caption now names the class-interval grouping that was actually used: the method, the number of groups formed, how many of those contain respondents when that is fewer, and whether the requested grouping had to fall back to score level.

• RMitemInfitPlot() draws its outer interval at simfit$hdci_width instead of a fixed percentile range, so the plot and the table describe the same interval.

• RMpersonFit(parallel = TRUE, n_cores = NULL) now reads getOption("mc.cores") before falling back to two workers, matching the cutoff functions. Results are unaffected by the worker count.

• New help topic ?easyRasch2-reproducibility covers what seed guarantees, what the default seed = NULL inherits from a session-level set.seed(), and the pinned generator's side effects.

• Both Martin-Löf functions have been validated against the pml SAS macro (Christensen, 2004), kindly shared by Karl Bang Christensen. The statistic, the conditional log-likelihoods, the expected counts and the residuals reproduce the macro to numerical precision.

easyRasch2 1.1.1

Bug fixes

• RMtargeting() no longer fails when the maximum possible raw score is odd. The default bins was the maximum observed raw score divided by two, which is fractional for an odd maximum (e.g. 13.5 for a nine-item 0-3 instrument); ggplot2 4.0 rejects a non-whole bins in geom_histogram(), which produced an Error in seq.default(): 'to' must be a finite number when the empty panel was scaled. The default is now rounded up.

New features

• RMitemInfit() gains a statistic argument ("infit", the default, or "outfit") that selects which conditional fit statistic the table reports. RMitemInfitCutoff() already simulated both, but only infit could be displayed and tested. With statistic = "outfit" the cutoff bounds come from outfit_low/outfit_high, the bootstrap p-values from the simulated outfit distribution, and the columns are named Outfit_MSQ, Outfit_low, Outfit_high, p_outfit, padj_outfit. The multiplicity correction runs across items within the selected statistic, so testing both and reporting whichever flags is a larger family than either call corrects for. The default is unchanged.

Other changes

• RMitemInfitCutoff() changes its default iterations setting from 250 to 400. This is based on a simulation study, available at https://github.com/pgmj/rasch_fwer, and is intended for use with the FWER corrected p-values in RMitemInfit(), which will be the new default method in a future release. While 400 iterations is the new recommended lowest level for all sample sizes, a final analysis should use 1000-2000 iterations.

easyRasch2 1.1.0

Bug fixes

• Respondents with no responses at all (all-NA rows) no longer crash the functions that fit a CML model on data retaining missing values. psychotools::pcmodel() errors on all-NA rows with an opaque "invalid argument type" (and psychotools::raschmodel() segfaults), so such rows are now dropped up front — with the standard "N respondent(s) with no responses dropped." message — in RMlocdepQ3Cutoff(), RMitemInfit(), RMitemRestscore(), RMitemRestscoreBoot(), RMreliability(), and the observed-data overlays of RMlocdepQ3Plot() and RMitemInfitPlot(). RMdifLR() now drops such rows too (jointly with dif_var) instead of failing with eRm::LRtest()'s error. Functions that already used complete cases or called .drop_empty_respondents() are unaffected.

• RMlocdepQ3Cutoff()'s sample_n now counts the respondents actually used (all-NA rows excluded, incomplete responses still retained) and sample_n_total the raw input rows, matching RMlocdepQ3() and the other *Cutoff() objects; previously the two were documented as always equal. Captions in the affected functions keep reporting the raw total in the n = X of Y respondents form.

• RMdimMartinLof() / RMdimMartinLofResiduals(): corrected the category weights in the Monte Carlo null sampler and the expected-count computation (present in 1.0.0). The item parameters were passed to psychotools::elementary_symmetric_functions() with the wrong sign convention (it weights categories by exp(-par)), so the gamma functions used sign-inverted weights while the sampler's numerator used the correct ones. Consequences of the bug: simulated null datasets did not follow the fitted model, the Monte Carlo null distribution (and hence the p-value) depended systematically on the column order of the data, and the residual table's expected counts were wrong. The fixed sampler reproduces the exact conditional pattern distribution (verified against brute-force enumeration), and expected counts match enumeration exactly. Martin-Löf p-values and residuals change relative to 1.0.0 — typically the corrected null distribution sits lower, so rejections become somewhat clearer. In addition, items are now processed in a fixed (alphabetical) internal order, so the same seed reproduces the same p-value regardless of how the data's columns are arranged.

• RMreliability() is now fully reproducible with the same seed: the RMU estimate previously varied slightly between identical calls because mirt's Metropolis-Hastings plausible-value sampler leaves the R random-number stream in a nondeterministic state (the draws themselves are reproducible), which perturbed the RMU split-half column assignments downstream. The function now re-seeds before the RMU iterations.

Changes

• RMdimMartinLof() now returns p_value_floor (1 / (actual_iterations + 1)), the smallest attainable Monte Carlo p-value; the documentation explains that a p-value equal to the floor means no simulated statistic reached the observed one and should be read as "p < floor" (increase iterations for finer resolution). The vignette's Martin-Löf example now sets a seed and discusses this.
• RMdifTree() gains output = "list", returning both the tidy effect-size table ($table) and the augmented tree object ($tree) from a single fit (used by the jamovi module).

• RMpersonFit() gains output = "list", returning both the per-person data.frame ($fit) and the named list of person-fit maps ($plots) from a single computation — avoiding a second resampling run when both views are needed (used by the jamovi module).

• output = "dataframe" now returns unrounded values across the package; rounding is a presentation concern and now happens only when rendering the kable (whose displayed precision is unchanged). Affected functions: RMitemInfit(), RMitemInfitMI(), RMitemRestscore(), RMitemRestscoreBoot(), RMlocdepQ3() (the $pairs table), RMdifGamma(), RMlocdepGamma(), RMdimResidualPCA(), RMdimCFA() (both $fit and $loadings), RMreliability(), RMdifLR(), RMdifTree() (including the stability summary attribute), RMdimMartinLof() (the wle_correlation element), RMdimMartinLofResiduals(), RMpersonParameters() (dataframe and file/CSV output), RMpersonFit(), and RMitemParameters() (dataframe and file/CSV output; previously rounded to 4 decimals, which also made the wide format's mean location differ from the mean of its rounded threshold columns in the 5th decimal). RMscoreSE() already followed this convention.

• Consequently, Flagged labels and sorting are now always computed on the exact (unrounded) values. Items or pairs sitting exactly on a rounded boundary could in principle change flag relative to earlier releases; displayed tables are otherwise identical.
• Deliberately unchanged: the infit simulation workers still round the per-iteration statistics to 3 decimals before the cutoff computation, so simulation-based expected ranges reproduce earlier releases exactly.
• Minor layout changes for RMtargeting() regarding number of bins if not set by user. Earlier it was sum score + 1, now it is sum score divided by 2. RMitemICCPlot() gets slightly larger points and thicker error bars.

Improvements

• RMitemICCPlot() gains two grouping methods for the observed means, alongside the existing quantile grouping (method = "cut" is now a legacy alias for method = "quantile"): method = "width" for equal-width intervals on the total-score scale, and method = "manual" with the new score_breaks argument -- the total scores at which a new group starts (the concept of RASCHplot's lower.groups, without the leading zero). The documentation of the grouping rules and of the error_band (the model-implied interval for the observed mean per total score) was substantially expanded.
• RMplotTile() gains a text_size argument for the cell labels (default 4, matching RMitemCatProb()), and cells with zero responses are now visually distinct: they are taken off the colour scale and shown unfilled with a light grey outline (zero_fill = "white", set to NULL for the previous behaviour), so the absence of data reads as absence rather than as the darkest end of the fill scale, where n = 0 was nearly indistinguishable from n = 1. The caption notes the convention when empty cells are present.
• RMlocdepGamma()'s output = "dataframe" tables now include the se, lower, and upper (95% Wald CI) columns for each pair, matching RMdifGamma(); the formatted kable output is unchanged.

easyRasch2 1.0.0

A large consolidation release. The headline change is that the whole package now runs on a single estimation engine — conditional maximum likelihood (CML) item parameters via psychotools and Warm's weighted-likelihood (WLE) person locations — replacing the previous mix of eRm (MLE) and mirt (MML/EAP). On top of that: new CFA loading diagnostics, three new functions for person+item parameters and person fit, bootstrap p-values with multiplicity correction, and a round of naming/output consistency fixes. After this release eRm is used only by RMdifLR() (Andersen's LR test) and mirt only for the RMU plausible values (RMitemInfitPlot()'s observed-fit overlay moved from an eRm fit to psychotools::pcmodel(), matching RMitemInfitCutoff(); values agree to ~1e-6). Accordingly, eRm has moved from Imports to Suggests: RMdifLR() errors with an install hint when it is absent.

Many items below shift reported numbers slightly: the underlying statistics are unchanged, only the estimation engine (WLE locations are finite at extreme scores, so extreme-score cases are now retained rather than dropped).

Unified CML/WLE estimation engine

• Observed item statistics — RMitemRestscore(), RMitemInfit(), RMitemCatProb(), RMitemHierarchy(), RMtargeting() — moved to CML (psychotools) + WLE. The conditional iarm statistics are engine-invariant; only minor things move on the common grand-mean-zero scale (Relative_location via the WLE person mean; threshold SEs ~few %; polytomous ICC curve position; the targeting person distribution). RMdifLR() deliberately stays on eRm::LRtest().
• Parameter / score functions — the shared .rasch_fit_cml() behind RMitemParameters(), RMpersonParameters(), RMscoreSE() now fits by CML via psychotools. Locations, person estimates and score SEs are unchanged (match eRm to ~5e-5); only RMitemParameters()'s threshold SEs shift slightly (psychotools vcov vs the eRm delta method, ~few %).
• RMscoreSE() — the WLE method now reports the information-based SE 1/sqrt(I(theta)) (matching RMpersonParameters(), catR, TAM); point estimates unchanged, SEs differ (larger) at the score extremes, and the WLE solver/centring is now shared with RMpersonParameters().
• RMreliability() — empirical reliability is replaced by a native marginal reliability (Green 1984; the "Empirical" row is now "Marginal"), While mirt::marginal_rxx() uses N(0,1), this integrates over the estimated latent variance, so it is correct on the Rasch logit scale; and the PSI is now the WLE-based separation reliability (1 - mean(SEM^2)/Var(theta), excluding extreme scorers), replacing eRm::SepRel() + mirt. The bootstrap is now fully native (no mirt per resample). PSI can differ noticeably for scales with many extreme scorers; alpha and RMU are unchanged.
• Simulation-based tools now both generate and refit on the CML/WLE engine: RMlocdepQ3()/RMlocdepQ3Cutoff(), RMitemInfitCutoff(), RMitemInfitMI()/RMitemInfitCutoffMI(), RMitemRestscoreBoot(), RMdimResidualPCA()/RMdimResidualPCACutoff(), RMdimCFACutoff(), RMdimMartinLof(), RMdifGammaCutoff(), RMlocdepGammaCutoff(). The eRm-based internal extract_item_thresholds() helper is removed. Conditional / scale-invariant statistics (infit/outfit, item-restscore classification, the Martin-Löf Monte-Carlo p-value, pooled MI infit MSQ/SE) are unchanged; residual-based and simulated quantities shift slightly — Q3 values/cut-offs/flags (the statistic is unchanged; observed-vs-old-MML correlation is typically >0.95), PCA eigenvalues / variance partition / cut-off, and the gamma cut-offs.
• RMlocdepQ3()/RMlocdepQ3Cutoff() keep the previous engine available via estimator = "MML" (default "CML"); the estimator is stored in the cutoff object and reused by RMlocdepQ3()/RMlocdepQ3Plot() (a mismatch is overridden with a warning). RMitemInfitMI()'s pooled-SE caveat (Müller 2020) is now documented.

New functions

• RMpersonFit() — per-respondent conditional infit/outfit MSQ and the standardized log-likelihood lz, computed per response pattern (handles partial missingness, no biased person estimate in the residual). Significance is by Monte-Carlo resampling under the fitted model, not the unreliable asymptotic null (Sinharay 2016; Müller 2020). Output as table, data.frame, or a named list of person-fit maps; zstd = TRUE adds the Wilson-Hilferty transform (comparability only, not for inference); flag = "underfit" restricts flagging to the validity-relevant direction.
• RMitemParameters() — item difficulty (dichotomous) or Andrich-threshold (polytomous) parameters in long/wide format, with optional SEs and Wald CIs.
• RMpersonParameters() — per-respondent theta and SEM computed on each response pattern (handles partial missingness), via Warm's WLE (default) or EAP under a normal prior (SD estimated by marginal ML unless fixed).
• Both RMitemParameters() and RMpersonParameters() support output = "file" to write the result table to a CSV at filename (the data.frame is also returned invisibly).

CFA dimensionality: loadings + three-function split

CFA now also reports per-item standardized factor loadings (observed vs a simulated expected range), restructured to match the other simulation tools:

• RMdimCFACutoff() is now simulate-only — returns the simulation object (null distributions + cutoffs for fit indices and loadings); it no longer computes the observed fit, and output = "kable" errors with a pointer to RMdimCFA(). (Breaking.)
• RMdimCFA() (new) takes data + a required cutoff object and returns a list of two tables, $fit (CFI/RMSEA/SRMR) and $loadings (observed loading vs the two-sided expected range), as kables (default) or data.frames.
• RMdimCFAPlot(cutoff_res, data) now returns a list of two ggplots ($loadings, in the RMitemInfitCutoffPlot() style; and $fit, the faceted fit-index distributions) and requires data. (Breaking: previously a single ggplot.)

Bootstrap p-values and multiplicity correction

Six simulation-based diagnostics gain optional bootstrap p-values computed against their simulated null distributions (p_value = TRUE; the default FALSE leaves existing output unchanged). Shared mechanics: each statistic is studentised by its bootstrap mean/SD; marginal Monte-Carlo p-values have floor 1/(B+1); correction offers Westfall-Young studentised-max step-down FWER (default), Benjamini-Hochberg or Benjamini-Yekutieli FDR, or "none"; Flagged then reflects padj < alpha while the simulated effect-size bands stay in the tables. The full *Cutoff() object is required, and >= 1000 cutoff iterations are recommended (warning below that). (Ferreira 2024; Westfall & Young 1993.) Per function:

• RMdifGamma(): two-sided per item. The asymptotic BH-adjusted p-value and star columns from iarm are dropped in this mode (one p-value family per table); p_gamma / padj_gamma replace them.
• RMdimCFA(): one-sided in the unfavourable direction for CFI / RMSEA / SRMR, two-sided for the per-item loadings, corrected as two separate families.
• RMdimResidualPCA(): a single one-sided test of the first-contrast eigenvalue, so no multiplicity correction is involved.
• RMitemInfit(): two-sided per item.
• RMlocdepGamma(): one-sided per pair for excess positive LD (matching RMlocdepQ3()), computed once per pair in the canonical direction (rest score = total − Item2, the simulated direction) and repeated in the direction-2 table; correction runs over the full pair family before any n_pairs display filter; the iarm BH columns are dropped as in RMdifGamma().
• RMlocdepQ3(): one-sided per pair, folded into the new per-pair $pairs table returned alongside $matrix (observed Q3 vs simulated band, directional flag, sorted by departure from the per-pair median, optional n_pairs cap).

RMitemInfit(), RMlocdepQ3(), and the newly extended functions share a "Multiple comparisons" help section.

Output and argument consistency

• All table and plot captions now report the estimation sample size in a common form: n = X of Y respondents (<policy>), where of Y appears only when respondents were excluded and the policy note (complete cases / incomplete responses retained / missing values imputed) only when the input contained missing values — complete data reads simply n = X respondents. Terminology is "respondents" throughout (previously a mix of "persons" / "complete cases"). Sample size is newly reported by RMitemParameters(), RMpersonParameters(), RMitemHierarchy(), RMitemCatProb(), RMscoreSE(), RMlocdepQ3(), RMdimResidualPCA(), RMpersonFit()'s kable, and the descriptive plots (RMplotBar(), RMplotStackedbar(), RMplotTile()); all other captions are reworded to the common form. The bootstrap-null plots (RMitemInfitPlot(), RMdifGammaPlot(), RMlocdepGammaPlot(), RMlocdepQ3Plot(), RMdimCFAPlot()) report the simulation sample the same way, suffixed per dataset; their *Cutoff() objects store sample_n_total / sample_has_na, with a graceful fallback for cutoff objects from older versions. Respondents with no responses (all-NA rows) are dropped with a one-time message rather than triggering a CML fitting error, and RMplotTile() likewise messages when rows with NA in group are dropped (previously silent; item-level NAs are retained in the descriptive plots).
• RMdifGammaCutoff() no longer prints an iarm::partgam_DIF() result table on every iteration in sequential (parallel = FALSE) runs; the silencing sink is now also restored safely on iteration failure (hardened in RMlocdepGammaCutoff() too).
• RMdifLR() captions now report the Andersen LR p-value as a plain number (exact to three decimals, p < 0.001 below that) instead of the format.pval() scientific notation (<1e-04), and the test statistic is shown as χ² instead of chi^2.
• Naming aligned across functions (no deprecation aliases): RMdimResidualPCA() plot output is now output = "ggplot" ("loadings" kept as a backward-compatible alias); RMtargeting() output = "figure" → "patchwork" (matching RMitemICCPlot()); RMitemRestscore() p.adj → p_adj; RMitemInfitPlot() output → statistic.
• RMitemInfitCutoffPlot() renamed to RMitemInfitPlot(), matching the <base>Plot form of the other bootstrap-cutoff plot functions (RMdifGammaPlot(), RMlocdepGammaPlot(), RMlocdepQ3Plot(), RMdimCFAPlot()). Because the old name shipped in the 0.8.0 CRAN release, it is kept as a deprecated alias that warns and forwards (unlike the 0.8.0 renames, which dropped old names outright). Separately, RMdimCFAPlot()'s first argument cutoff_res was renamed to simfit to match the other plot functions (no alias).
• RMdifLR() now defaults to output = "kable" (was "ggplot"), matching every other function that offers both a kable and a ggplot output (RMscoreSE(), RMdimMartinLof(), RMpersonFit(), RMpersonParameters(), RMdimResidualPCA()). Pass output = "ggplot" for the previous default.
• The Flagged column now labels misfit direction ("overfit" / "underfit" / "") instead of logical TRUE/FALSE in RMitemInfit() / RMitemInfitMI() (column type changes from logical to character). RMitemRestscore() gains a Flagged column and drops the redundant significance-star and Location columns (trimmed headers + explanatory caption). Note the value direction differs: a high infit is underfit, a high restscore correlation is overfit.

Other changes and fixes

• RMitemInfitMI() and RMitemInfitCutoffMI() now accept mids objects whose items were imputed as ordered factors (as required by mice's polr method): the completed data are coerced back to numeric responses internally, rather than erroring on the factor columns.
• RMitemInfitCutoff() and RMlocdepQ3Cutoff() gain an experimental dgp argument — "resample" (default; resample WLE locations and simulate, a marginal null) vs "conditional" (simulate each pattern from the exact Rasch conditional given the observed total score, a matched conditional null) — stored in the result. See dev/q3_dgp_comparison.R / dev/infit_dgp_comparison.R.
• Q3 plot and table outputs now share a $matrix / $pairs structure. RMlocdepQ3Plot() returns a list of two plots: $pairs (the per-pair simulated-vs-observed dot-interval, previously the single return value) and $matrix (a lower-triangle diverging-RdYlBu Q3 tile heatmap with above-cutoff pairs outlined, adapted from RASCHplot::ggQ3star(); needs data, else NULL).
• RMitemICCPlot() is reimplemented on the CML engine, replacing the iarm::ICCplot() wrapper. The conditional item curve and its variance now come from psychotools CML thresholds + the exact conditional distribution given the total score. It draws confidence intervals on the observed class-interval means (ci, default on) and an optional model band (error_band); in DIF mode it shows per-group CIs and annotates each panel with the partial-gamma DIF magnitude (iarm::partgam_DIF, as in RMdifGamma()), with the full table attached as attr(., "dif_gamma"). New arguments ci, error_band, conf_level, min_n (per-cell floor, 8), items; method / class_intervals / dif_var / output are unchanged. The conditional-ICC approach follows Buchardt, Christensen & Jensen (2023) and their RASCHplot package.
• RMlocdepGamma(): corrected the $direction2 caption / @return wording to "total - Item2" (with a note that direction 2 lists pairs in reverse order); computations unchanged (labelling fix).
• RMdifTree(): fixed a "variable lengths differ" error when a single covariate was passed by index or expression (e.g. covariates = phq9[, 10]). The derived non-syntactic column name is now matched as a literal column rather than re-evaluated as an R expression against the original (pre-NA-drop) data.
• RMdifTree(stability = TRUE) no longer silences the console in front-ends (e.g. RStudio) that redirect the message stream. The internal stderr capture used to muffle resample-fit noise reset the message sink to the default, clobbering the front-end's sink; it now only redirects when no foreign message sink is active.
• RMitemRestscore(p_adj = "none") now returns the unadjusted p-values instead of NA with a "NAs introduced by coercion" warning. With no adjustment iarm::item_restscore() omits the adjusted-p column, so the p-value is now selected by name rather than by a fixed column position (which had picked up the significance-stars column). The table header reads "p-value" in this case.

easyRasch2 0.8.0

Initial CRAN release!

Visual consistency across all plot output

• Every RM* function that returns a ggplot now applies three shared internal theme helpers:
• er2_axis_margins() --- extra breathing room around the x and y axis titles.
• er2_plot_caption() --- left-aligned 9 pt plot caption. When the optional ggtext package is installed (new in Suggests), the caption renders via ggtext::element_markdown() so the APA-conventional italic "Note." prefix can sit alongside roman body text. Without ggtext, it falls back to a plain element_text(face = "italic") and a plain "Note. " prefix --- no markdown asterisks ever leak through to the rendered text.
• er2_caption() --- builds the caption string with the "Note." prefix and wraps long captions at 90 characters via strwrap() so they no longer run off the right edge of the plot. The body text is wrapped first then prefixed, so the prefix is never broken by a line break.
• All three helpers are internal (@noRd) and not exported.

New function

• RMitemCatProb() plots model-implied category-response probability curves per item, similar to eRm::plotICC() or mirt trace plots but with a ggplot2 / viridis output. Each item gets its own facet panel; one curve per response category is coloured from low to high using a continuous viridis palette. Polytomous items are fit with eRm::PCM(); dichotomous items fall back to eRm::RM() (recovering the standard two-category logistic ICC). Optional descriptive item_labels and category_labels make the output report-ready.
• Optional label_curves = "path" mode (requires the optional geomtextpath package, in Suggests) writes each category's label along its own curve in the classic IRT trace-plot style. Single-item only --- pass the full multi-item dataset and use the item argument to choose which item's curves to plot. The model is still fit on all items (CML threshold estimation needs the full dataset); only the rendering is filtered. Each category's label is positioned at that curve's modal theta (the peak of its bell for middle categories; the relevant edge for the monotone extreme categories), clamped a small margin in from the plot edges to prevent clipping.

Function renaming for consistency

This release renames 22 exported functions under a consistent domain-prefix → method → variant-suffix scheme so that autocompletion on a prefix (RMdif, RMlocdep, RMitem, RMdim, RMplot) surfaces every related function. No semantic changes; only names.

A complete mapping is on the new ?easyRasch2-renaming help page. The short version:

| Domain | Prefix | Examples | |-----------------|--------------|----------| | DIF | RMdif | RMdifGamma(), RMdifGammaCutoff(), RMdifGammaPlot(), RMdifLR(), RMdifTree() | | Local dependence| RMlocdep | RMlocdepQ3(), RMlocdepQ3Cutoff(), RMlocdepQ3Plot(), RMlocdepGamma(), RMlocdepGammaCutoff(), RMlocdepGammaPlot() | | Item statistics | RMitem | RMitemInfit(), RMitemInfitCutoff(), RMitemInfitCutoffPlot(), RMitemInfitMI(), RMitemInfitCutoffMI(), RMitemRestscore(), RMitemRestscoreBoot(), RMitemICCPlot(), RMitemHierarchy() | | Dimensionality | RMdim | RMdimResidualPCA(), RMdimResidualPCACutoff(), RMdimCFACutoff(), RMdimCFAPlot(), RMdimMartinLof(), RMdimMartinLofResiduals() | | Descriptive plot| RMplot | RMplotTile(), RMplotBar(), RMplotStackedbar() |

Unchanged: RMreliability(), RMUreliability(), RMtargeting(), RMscoreSE().

Breaking change. No deprecation aliases are shipped — existing scripts will need a search-and-replace. See ?easyRasch2-renaming for the full table.

easyRasch2 0.7.1

• New RMlocdepQ3plot() for visualising per-pair simulated Q3 distributions against observed Q3 values, mirroring the design of RMpgLDplot() and RMpgDIFplot(). Supports items and n_pairs filters; with data supplied, ranks n_pairs by |observed Q3 - median(simulated Q3 per pair)|.
• RMlocdepQ3cutoff() now retains per-pair iteration-level data: the returned list gains pair_results, pair_cutoffs, item_names, cutoff_method, and hdci_width. Existing scalar outputs (suggested_cutoff etc.) are unchanged. New arguments cutoff_method ("hdci" / "quantile") and hdci_width (default 0.99) control how per-pair credible intervals are computed.
• RMlocdepQ3() can now be called with the full list returned by RMlocdepQ3cutoff() (it auto-extracts $suggested_cutoff), matching the partial-gamma family convention. Numeric scalar still works.
• RMpartgamLD() and RMpgLDplot() gain an n_pairs argument to keep only the top-N pairs by |gamma| (LD) or by |observed - median(sim)| deviation (plot).
• RMpartgamLD(), RMpartgamDIF(), and RMitemrestscore() now use consistent column headers: Adj. p-value (BH) and a new p-value sign. star-string column. RMitemrestscore()'s former Absolute_difference column is now a signed Difference (observed − expected), so over- and underfit are visually distinguishable at a glance.

easyRasch2 0.7.0

• New RMbarplot() and RMstackedbarplot() join RMtileplot() for easy visualization of item response data distributions.
• New RMciccPlot() for conditional item characteristic curves plot, also includes DIF analysis for categorical DIF variables.
• New RMitemHierarchy(), which outputs a plot illustrating the item hierarchy with item thresholds and confidence intervals.
• Modified RMcfaCutoff() to only use .scaled metrics for RMSEA and CFI, for stability. See documentation for more details.
• Bug fix for RMmartinLof() with dichotomous items.
• Bug fix for RMitemHierarchy() making option item_labels work as intended.
• Tests written for all functions, preparing for CRAN submission in the nearish future.
• Drafted a package intro/vignette

easyRasch2 0.6.0

• New RMcfaCutoff() for testing unidimensionality against simulation-based cutoff values for model fit metrics.
• RMcfaPlot() shows figure with distribution of simulation results and observed model fit values.

easyRasch2 0.5.8

• New RMdifTree() for testing DIF with continuous variables, such as age in years, as well as combinations of DIF variables (DIF interactions).
• Also implements use of stablelearner, as recommended by Henninger et al. (2025) to assess stability of tree-based results.
• Borrows from
  • raschtreeMH (https://github.com/mirka-henninger/raschtreeMH)
  • effecttree (https://github.com/mirka-henninger/effecttree)
• See these two papers (and ?RMdifTree) for more details:
  • Henninger, M., Debelak, R., & Strobl, C. (2023). A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for DIF. Educational Psychological Measurement, 83, 181-212. doi:10.1177/00131644221077135
  • Henninger, M., Radek, J., Debelak, R., & Strobl, C. (2025). Partial credit trees meet the partial gamma coefficient for quantifying DIF and DSF in polytomous items. Behaviormetrika, 52, 221-257.

easyRasch2 0.5.7

• New RMdifLR() for testing DIF of categorical variables using Andersens's Likelihood Ratio test as implemented in package eRm
• Visualization of response data using a tile plot with RMtileplot().
• Optional group faceting, which is useful for DIF-analyses.
• Added Martin-Löf test of dimensionality RMmartinLof() based on Christensen & Kreiner (2007, doi: 10.1177/0146621605286204), for both dichotomous and polytomous (PCM) items.
• Also added post-processing function RMmartinLofresiduals().

easyRasch2 0.5.6

• Added RMresidualPCA() for evaluating patterns in the standardized residuals from the RM/PCM. Outputs either a table with eigenvalues and explained variance or a figure with standardized loadings on the first residual contrast and item locations.
• Added RMpcaCutoff() to determine a simulation-based critical value for the largest eigenvalue.

easyRasch2 0.5.5

• Added RMbootRestscore() for use with large sample sizes. See https://pgmj.github.io/rasch_itemfit/ for more details.
• Added RMscoreSE() that produces a transformation table (or figure) from ordinal sum scores to WLE (Weighted Likelihood Estimation) interval scores.
• Modified RMlocdepQ3cutoff() to improve speed.

easyRasch2 0.5.4

• RMtargeting() for Wright map style plots.
• New functions for handling missing data with conditional item infit MSQ by multiple imputation using package mice.
• RMinfitcutoff_mi() uses the mids object containing multiple imputated datasets (output by mice::mice()) to run simulations on each datasets and combines the results. Splits iterations across imputations (e.g. 250 total / 5 imputations = 50 each).
• RMiteminfit_mi() calculates and pools conditional infit from the imputated datasets and optionally uses the RMinfitcutoff_mi() output for cutoff values.
• Works with existing RMinfitcutoffPlot()

easyRasch2 0.5.3

• New functions RMpartgamLD() and RMpgLDcutoff() for evaluating local dependency of item pairs in both directions.
• Plot function RMpgLDplot(), similar to RMpgDIFplot()
• NOTE: The simulation-based cutoffs have not yet been evaluated in a systematic way. Default choices may not be sensible, please try different numbers of iterations and hdci_width.

easyRasch2 0.5.2

• New functions RMpartgamDIF() and RMpgDIFcutoff() for evaluating DIF of categorical external variables.
• Also with plot function RMpgDIFplot(), similar to RMinfitcutoffPlot()
• NOTE: The simulation-based cutoffs have not yet been evaluated in a systematic way. Default choices may not be sensible, please try different numbers of iterations and hdci_width.

easyRasch2 0.5.1

• New function RMinfitcutoffPlot() to illustrate distribution of simulated conditional item infit MSQ values together with the observed value.

easyRasch2 0.5.0

• RMinfitcutoff() gains cutoff_method and hdci_width parameters: By default (cutoff_method = "hdci", hdci_width = 0.999), per-item cutoff intervals are now computed using the Highest Density Interval via ggdist::hdci() (99.9% HDCI). Set cutoff_method = "quantile" to restore the previous behaviour (2.5th/97.5th percentiles). The ggdist package is only required when cutoff_method = "hdci" (added to Suggests). The returned list now also includes cutoff_method and hdci_width fields.

• RMiteminfit() caption updated: When cutoff is the return value of RMinfitcutoff(), the kable caption now states the cutoff method, e.g. "Cutoff values based on 250 simulation iterations (99.9% HDCI)." or "Cutoff values based on 250 simulation iterations (2.5th/97.5th percentile).".

easyRasch2 0.4.0

• RMiteminfit() gains optional cutoff parameter: Accepts the return value of RMinfitcutoff() (or its $item_cutoffs data.frame directly). When provided, per-item cutoff boundaries (Infit_low, Infit_high) and a logical Flagged column are added to both "dataframe" and "kable" output. The kable caption includes the number of simulation iterations when available.

• New RMinfitcutoff(): Simulation-based (parametric bootstrap) cutoff determination for [RMiteminfit()]. Supports both dichotomous and polytomous data. Optional parallel processing via mirai (falls back to sequential if not installed). Returns per-item 2.5th and 97.5th percentiles of simulated infit and outfit MSQ distributions ($item_cutoffs), together with the full iteration-level results ($results). Requires the iarm package (Suggests).

easyRasch2 0.3.0

• New RMiteminfit(): Computes conditional infit MSQ statistics for each item via iarm::out_infit(), enriched with item locations relative to the sample mean person location. Supports both dichotomous (Rasch model via eRm::RM()) and polytomous (Partial Credit Model via eRm::PCM()) data. Requires the iarm package (Suggests). Output options: "kable" (default, plain-text knitr::kable()) or "dataframe". Optional sort = "infit" sorts by infit MSQ descending. Only complete cases are used for conditional fit calculation.

• New RMitemrestscore(): Computes observed and model-expected item-restscore correlations via iarm::item_restscore(), enriched with absolute differences between observed and expected values, item average locations, and item locations relative to the sample mean person location. Supports both dichotomous (Rasch model via eRm::RM()) and polytomous (Partial Credit Model via eRm::PCM()) data. Requires the iarm package (Suggests). Output options: "kable" (default, plain-text knitr::kable()) or "dataframe". Optional sort = "diff" sorts by absolute difference descending.

easyRasch2 0.2.0

• RMlocdepQ3(): cutoff is now optional (default NULL). When omitted, the raw Q3 residual correlation matrix is returned without any dynamic cut-off applied. When provided, the dynamic cut-off (mean Q3 + cutoff) is shown in the kable caption as before. Supersedes the requirement to always supply a cutoff value (PRs #2 and #3).
• New RMlocdepQ3cutoff(): Simulation-based (parametric bootstrap) cutoff determination for RMlocdepQ3(). Supports both dichotomous and polytomous data. Optional parallel processing via mirai (falls back to sequential if not installed). The $suggested_cutoff is the 99th percentile of the simulated Q3 max–mean distribution.
• New internal helpers in R/utils-simulation.R: sim_poly_item(), sim_partial_score(), and extract_item_thresholds().
• Dependencies: eRm, psychotools (>= 0.7-3), parallel, and utils moved/added to Imports. mirai added to Suggests.

easyRasch2 0.1.0

• Initial package structure.
• Added RMlocdepQ3() for Yen's Q3 residual correlation analysis of local dependence.

easyRasch2 documentation built on Sept. 13, 2026, 1:07 a.m.