easyRasch2: Psychometric Analysis with Rasch Measurement Theory
Streamlines reproducible Rasch measurement theory analyses
for ordinal item-response data, combining estimation routines from
'eRm', 'psychotools', 'mirt', 'iarm', and 'lavaan' with consistent
diagnostic, plotting, and reporting layers. Covers the four basic
psychometric criteria summarised by Christensen et al. (2021)
<doi:10.1111/sms.13908> – unidimensionality, local independence,
ordered response category thresholds, and invariance across
subgroups – together with item fit, targeting, reliability,
category functioning, and descriptive item-response plots. A
distinguishing feature is the use of simulation-based critical
values to replace rule-of-thumb cutoffs for conditional infit mean-square,
Yen's Q3 local-dependence statistic, the largest residual-PCA eigenvalue,
and ordinal CFA fit indices. Outputs are knitr::kable() tables and
'ggplot2' figures suitable for direct inclusion in 'Quarto' and 'R Markdown'
reports.
| Version: |
0.8.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
eRm, knitr, mirt, psychotools (≥ 0.7-3), stats, utils, rlang |
| Suggests: |
difR, dplyr, geomtextpath, ggdist, ggtext, iarm, mirai, ggplot2 (≥ 3.4.0), partykit, psychotree, stablelearner, testthat (≥ 3.0.0), rmarkdown, patchwork, scales, mice, ggrepel, lavaan |
| Published: |
2026-06-08 |
| DOI: |
10.32614/CRAN.package.easyRasch2 (may not be active yet) |
| Author: |
Magnus Johansson 
[aut, cre],
Nicklas Korsell [ctb] (PCM simulation code),
Mirka Henninger
[ctb] (MH / partial-gamma effect-size and ETS-classification
algorithms in dif_tree.R, adapted under MIT licence from the
raschtreeMH and effecttree packages),
Jan Radek [ctb]
(partial-gamma effect-size and ETS-classification algorithms in
dif_tree.R, adapted under MIT licence from the effecttree package) |
| Maintainer: |
Magnus Johansson <pgmj at pm.me> |
| BugReports: |
https://github.com/pgmj/easyRasch2/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/pgmj/easyRasch2,
https://pgmj.github.io/easyRasch2/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
easyRasch2 results |
Documentation:
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