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easyRaschBayes: Bayesian Rasch Analysis Using 'brms'
Reproduces classic Rasch psychometric analysis features using Bayesian item response theory models fitted with 'brms' following Bürkner (2021) <doi:10.18637/jss.v100.i05> and Bürkner (2020) <doi:10.3390/jintelligence8010005>. Supports both dichotomous and polytomous Rasch models. Features include posterior predictive item fit, conditional infit, item-restscore associations, person fit, differential item functioning, local dependence assessment via Q3 residual correlations, dimensionality assessment with residual principal components analysis, person-item targeting plots, item category probability curves, and reliability using relative measurement uncertainty following Bignardi et al. (2025) <doi:10.31234/osf.io/h54k8_v1>.
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Package details |
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| Author | Magnus Johansson [aut, cre] (ORCID: <https://orcid.org/0000-0003-1669-592X>), Giacomo Bignardi [ctb] (RMU reliability code) |
| Maintainer | Magnus Johansson <pgmj@pm.me> |
| License | GPL (>= 3) |
| Version | 0.2.0 |
| URL | https://github.com/pgmj/easyRaschBayes https://pgmj.github.io/easyRaschBayes/ |
| Package repository | View on CRAN |
| Installation |
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