easyRaschBayes provides functions to reproduce classic Rasch analysis features using Bayesian item response theory (IRT) models fitted with brms. It supports both dichotomous Rasch models and polytomous partial credit models, and exposes the full posterior distribution for all output.
Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("pgmj/easyRaschBayes")| Function | Description |
|---|---|
dif_statistic() |
Differential Item Functioning (DIF) analysis |
fit_statistic_pcm() |
Posterior predictive item fit for polytomous models |
fit_statistic_rm() |
Posterior predictive item fit for dichotomous models |
infit_statistic() |
Conditional infit / outfit statistics |
item_restscore_statistic() |
Item–rest score associations with Goodman & Kruskal’s gamma |
plot_residual_pca() |
Residual PCA contrast plot for dimensionality assessment |
q3_statistic() |
Yen’s Q3 residual correlations for local dependence evaluation |
plot_ipf() |
Item category probability function curves |
plot_targeting() |
Person-item map (Wright map) |
RMUreliability() |
Reliability via Relative Measurement Uncertainty (RMU) |
library(brms)
library(easyRaschBayes)
# Fit a Bayesian Rasch model (dichotomous)
fit <- brm(
response ~ 1 + (1 | item) + (1 | id),
data = my_data,
family = bernoulli(),
chains = 4, cores = 4
)
# Item fit
infit_statistic(fit)
# Person-item map
plot_targeting(fit)
# Local dependence
q3_statistic(fit)Bürkner, P.-C. (2020). Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models. Journal of Intelligence, 8(1). doi:10.3390/jintelligence8010005
Bürkner, P.-C. (2021). Bayesian Item Response Modeling in R with brms and Stan. Journal of Statistical Software, 100, 1–54. https://doi.org/10.18637/jss.v100.i05
This started as a little side project to update the code posted by Bürkner (2020) to assess item fit. With the help of Claude Opus 4.6 the things expanded to try to reproduce the core Rasch analysis aspects. Most of the code in this package is produced by the LLM.
Magnus Johansson is a licensed psychologist with a PhD in behavior analysis. He works as a research specialist at Karolinska Institutet, Department of Clinical Neuroscience, Center for Psychiatry Research.
GPL (>= 3)