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license: GPL-3.0-only
title: 'BoundIRT: Fit Bounded Continuous Item Response Theory Models to Data'
version: 0.8.0
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)<https://mc-stan.org/>.
  Available models include the Beta IRT model by Noel and Dauvier (2007)<https://doi.org/10.1177/0146621605287691>,
  the continuous response model by Samejima (1973)<https://doi.org/10.1007/BF03372160>,
  the unbounded normal model by Mellenbergh (1994)<https://doi.org/10.1207/s15327906mbr2903_2>,
  and the Simplex IRT model by Flores et al. (2020)<https://doi.org/10.1007/978-3-030-43469-4_8>.
  All models can be fitted with or without zero-one inflation (Molenaar et al., 2022)<https://doi.org/10.3102/10769986221108455>.
  Model fit comparisons can be conducted using the Watanabe-Akaike information criterion
  (WAIC), leave-one-out cross-validation information criterion (LOOIC), and the fully
  marginalized likelihood (i.e., Bayes factors).
authors:
- family-names: Molenaar
  given-names: Dylan
  email: d.molenaar@uva.nl
repository: https://dylanmolenaar-nl.r-universe.dev
commit: 4cea92b453361b3d85a884da7b18cbc48668341a
date-released: '2026-09-16'
contact:
- family-names: Molenaar
  given-names: Dylan
  email: d.molenaar@uva.nl