easyRasch2-package: easyRasch2: Psychometric Analysis with Rasch Measurement...
In easyRasch2: Psychometric Analysis with Rasch Measurement Theory
easyRasch2-package R Documentation
easyRasch2: Psychometric Analysis with Rasch Measurement Theory
Description
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) \Sexpr[results=rd]{tools:::Rd_expr_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, ordinal CFA fit indices, and partial-gamma DIF and local-dependence coefficients, optionally augmented with multiplicity-corrected bootstrap p-values. Outputs are knitr::kable() tables and 'ggplot2' figures suitable for direct inclusion in 'Quarto' and 'R Markdown' reports.
Author(s)
Maintainer: Magnus Johansson pgmj@pm.me (ORCID)
Authors:
Magnus Johansson pgmj@pm.me (ORCID)
Other contributors:
Nicklas Korsell (PCM simulation code) [contributor]
Mirka Henninger (ORCID) (MH / partial-gamma effect-size and ETS-classification algorithms in dif_tree.R, adapted under MIT licence from the raschtreeMH and effecttree packages) [contributor]
Jan Radek (ORCID) (partial-gamma effect-size and ETS-classification algorithms in dif_tree.R, adapted under MIT licence from the effecttree package) [contributor]
See Also
Useful links:
easyRasch2 documentation built on Sept. 13, 2026, 1:07 a.m.
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| easyRasch2-package | R Documentation |
easyRasch2: Psychometric Analysis with Rasch Measurement Theory
Description
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) \Sexpr[results=rd]{tools:::Rd_expr_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, ordinal CFA fit indices, and partial-gamma DIF and local-dependence coefficients, optionally augmented with multiplicity-corrected bootstrap p-values. Outputs are knitr::kable() tables and 'ggplot2' figures suitable for direct inclusion in 'Quarto' and 'R Markdown' reports.
Author(s)
Maintainer: Magnus Johansson pgmj@pm.me (ORCID)
Authors:
Magnus Johansson pgmj@pm.me (ORCID)
Other contributors:
Nicklas Korsell (PCM simulation code) [contributor]
Mirka Henninger (ORCID) (MH / partial-gamma effect-size and ETS-classification algorithms in dif_tree.R, adapted under MIT licence from the raschtreeMH and effecttree packages) [contributor]
Jan Radek (ORCID) (partial-gamma effect-size and ETS-classification algorithms in dif_tree.R, adapted under MIT licence from the effecttree package) [contributor]
See Also
Useful links:
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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