Bayesian Estimation of Generalized Item Response Models with Flexible Tail Behavior
收藏DataCite Commons2026-04-10 更新2026-05-04 收录
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https://data.mendeley.com/datasets/z6hssz598j
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资源简介：
This dataset provides a comprehensive computational framework for the Bayesian Estimation of Generalized Item Response Models with Flexible Tail Behavior. It features highly optimized algorithms for both the Generalized 2-Parameter Logistic Model (GL2PLM) and the Generalized Graded Response Model (GLGRM). The codebase is structured to address two primary estimation paradigms: (1) Known Shape Parameter: providing benchmark recovery analysis when $p$ is a pre-specified constant; and (2) Unknown Shape Parameter.
提供机构：
Mendeley Data
创建时间：
2026-03-16
