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Changelog

Source: NEWS.md

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.
      1. to assess stability of tree-based results.
    • Borrows from
    • 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.