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mixedsubjectsirt  

Item Response Theory Calibration with a Mixed Subjects Design
View on CRAN: Click here


Download and install mixedsubjectsirt package within the R console
Install from CRAN:
install.packages("mixedsubjectsirt")

Install from Github:
library("remotes")
install_github("cran/mixedsubjectsirt")

Install by package version:
library("remotes")
install_version("mixedsubjectsirt", "1.0.0")



Attach the package and use:
library("mixedsubjectsirt")
Maintained by
Klint Kanopka
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-25
Latest Update: 2026-06-25
Description:
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.
How to cite:
Klint Kanopka (2026). mixedsubjectsirt: Item Response Theory Calibration with a Mixed Subjects Design. R package version 1.0.0, https://cran.r-project.org/web/packages/mixedsubjectsirt. Accessed 24 Oct. 2026.
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