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mixedsubjectsirt: Item Response Theory Calibration with a Mixed Subjects Design

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.

Version: 1.0.0
Imports: mirt, rmutil
Suggests: ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-06-25
DOI: 10.32614/CRAN.package.mixedsubjectsirt (may not be active yet)
Author: Klint Kanopka ORCID iD [aut, cre]
Maintainer: Klint Kanopka <klint.kanopka at nyu.edu>
BugReports: https://github.com/klintkanopka/mixedsubjectsirt/issues
License: MIT + file LICENSE
URL: https://klintkanopka.com/mixedsubjectsirt/, https://github.com/klintkanopka/mixedsubjectsirt
NeedsCompilation: no
Language: en-US
Materials: README, NEWS
CRAN checks: mixedsubjectsirt results

Documentation:

Reference manual: mixedsubjectsirt.html , mixedsubjectsirt.pdf
Vignettes: Per-Item Lambda (Experimental) (source, R code)
Choosing Lambda in Mixed-Subjects IRT (source, R code)
IRT Linking and Gradient Asymmetry: Diagnostic Guide (source, R code)
Mixed-Subjects 1PL Calibration (source, R code)
Mixed-Subjects IRT Calibration (source, R code)
Simulation Validation of the Mixed-Subjects MML Estimator (source, R code)
Understanding Ability-Risk Tuning (source, R code)
Calibrating with a Weakly-Informative, Biased LLM (source, R code)

Downloads:

Package source: mixedsubjectsirt_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): mixedsubjectsirt_1.0.0.tgz, r-oldrel (arm64): mixedsubjectsirt_1.0.0.tgz, r-release (x86_64): mixedsubjectsirt_1.0.0.tgz, r-oldrel (x86_64): mixedsubjectsirt_1.0.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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