Item Response Theory Calibration with a Mixed Subjects Design R package mixedsubjectsirt version 1.0.0 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. Software Imports: mirt, rmutil Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0) Klint Kanopka <klint.kanopka@nyu.edu> Comprehensive R Archive Network (CRAN) Klint Kanopka [aut, cre] (ORCID: <https://orcid.org/0000-0003-3196-9538>) MIT + file LICENSE (https://CRAN.R-project.org/package=mixedsubjectsirt/LICENSE) 2026-06-25 application/tgz https://CRAN.R-project.org/package=mixedsubjectsirt doi:10.32614/CRAN.package.mixedsubjectsirt en-US