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BoundIRT
View on CRAN: Click
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Download and install BoundIRT package within the R console
Install from CRAN:
install.packages("BoundIRT")
Install from Github:
library("remotes")
install_github("cran/BoundIRT") Install by package version:
library("remotes")
install_version("BoundIRT", "0.0.1") Attach the package and use:
library("BoundIRT")
Maintained by
Dylan Molenaar
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First Published: 2026-04-20
Latest Update: 2026-04-20
Description:
Bounded continuous data are encountered in many areas of test application. Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025)<https://mc-stan.org/>. Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be fitted with or without zero-one inflation (Molenaar et al., 2022)<doi:10.3102/10769986221108455>. Model fit comparisons can be conducted using the Watanabe–Akaike information criterion (WAIC), the deviance information criterion (DIC), and the fully marginalized likelihood (i.e., Bayes factors).
How to cite:
Dylan Molenaar (2026). BoundIRT: Fit Bounded Continuous Item Response Theory Models to Data. R package version 0.0.1, https://cran.r-project.org/web/packages/BoundIRT. Accessed 12 Sep. 2026.
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