mixedsubjectsirt
1.0.0Item Response Theory Calibration with a Mixed Subjects Design
Overview
Integrates large language model generated item responses into psychometric calibration studies through a mixed-subjects design for unidimensional two-parameter and one-parameter logistic item response theory models. Human pilot responses are augmented with model-generated responses using a prediction-powered inference estimator (Angelopoulos, Bates, Fannjiang, Jordan and Zrnic (2023) doi:10.1126/science.adi6000; Angelopoulos, Duchi and Zrnic (2023) doi:10.48550/arXiv.2311.01453) adapted to marginal maximum-likelihood estimation, following the mixed-subjects design of Broska, Howes and van Loon (2025) doi:10.1177/00491241251326865. The estimator is anchored to the human responses and is asymptotically unbiased for the human item parameters at any tuning weight; the weight on the synthetic responses is chosen to minimize propagated ability-score risk, down-weighting uninformative or biased generated responses. Louis-corrected sandwich standard errors, ability scoring, cross-fitted tuning, and scale linking are also provided.
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- OK2026-06-267 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Checks run against github.com/klintkanopka/mixedsubjectsirt on 2026-07-30.
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1 release. R releases are shown for context.
- 1.0.0Latest2026-06-25 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-25
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
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