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license: GPL-3.0-only
title: 'BoundIRT: Fit Bounded Continuous Item Response Theory Models to Data'
version: 0.5.0
doi: 10.32614/CRAN.package.BoundIRT
abstract: 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).
Available models include the Beta IRT model by Noel and Dauvier (2007),
the continuous response model by Samejima (1973),
the unbounded normal model by Mellenbergh (1994),
and the Simplex IRT model by Flores et al. (2020).
All models can be fitted with or without zero-one inflation (Molenaar et al., 2022).
Model fit comparisons can be conducted using the Watanabe-Akaike information criterion
(WAIC), leave-one-out cross-validation information citerion (LOOIC) and the fully
marginalized likelihood (i.e., Bayes factors).
authors:
- family-names: Molenaar
given-names: Dylan
email: d.molenaar@uva.nl
repository: https://cran.r-universe.dev
commit: 0781e5dcd853ce20f71814ead5ae0c0cd36554fa
date-released: '2026-05-05'
contact:
- family-names: Molenaar
given-names: Dylan
email: d.molenaar@uva.nl