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"Title": "Fit Bounded Continuous Item Response Theory Models to Data",
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"Description": "Bounded continuous data are encountered in many areas of\ntest application. Examples include visual analogue scales used\nin the measurement of personality, mood, depression, and\nquality of life; item response times from tests with item\ndeadlines; confidence ratings; and pain intensity ratings.\nUsing this package, item response theory (IRT) models suitable\nfor bounded continuous item scores can be fitted to data within\na Bayesian framework. The package draws on posterior sampling\nfacilities provided by R-package 'rstan' (Stan Development\nTeam, 2025). Available models include the\nBeta IRT model by Noel and Dauvier\n(2007), the continuous response\nmodel by Samejima (1973), the unbounded\nnormal model by Mellenbergh\n(1994), and the Simplex IRT\nmodel by Flores et al. (2020).\nAll models can be fitted with or without zero-one inflation\n(Molenaar et al., 2022). Model\nfit comparisons can be conducted using the Watanabe-Akaike\ninformation criterion (WAIC), leave-one-out cross-validation\ninformation citerion (LOOIC) and the fully marginalized\nlikelihood (i.e., Bayes factors).",
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