Bayesian Rasch Analysis Using 'brms' R package easyRaschBayes version 0.2.0 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>. Software Depends: R (>= 4.1.0) Imports: brms (>= 2.20.0), rlang (>= 1.0.0), dplyr (>= 1.1.0), tidyr (>= 1.3.0), tibble (>= 3.0.0), ggdist, stats, grDevices, ggplot2 (>= 3.4.0), forcats Suggests: ggrepel, patchwork, eRm, testthat (>= 3.0.0), knitr, rmarkdown Magnus Johansson <pgmj@pm.me> Comprehensive R Archive Network (CRAN) Magnus Johansson [aut, cre] (ORCID: <https://orcid.org/0000-0003-1669-592X>), Giacomo Bignardi [ctb] (RMU reliability code) GPL (>= 3) 2026-03-28 application/tgz https://CRAN.R-project.org/package=easyRaschBayes doi:10.32614/CRAN.package.easyRaschBayes