Fit Bounded Continuous Item Response Theory Models to Data // 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), leave-one-out cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors).