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cff-version: 1.2.0
message: 'To cite package "mixedsubjectsirt" in publications use:'
type: software
license: MIT
title: 'mixedsubjectsirt: Item Response Theory Calibration with a Mixed Subjects Design'
version: 1.0.0
abstract: 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) <https://doi.org/10.1126/science.adi6000>;
  Angelopoulos, Duchi and Zrnic (2023) <https://doi.org/10.48550/arXiv.2311.01453>)
  adapted to marginal maximum-likelihood estimation, following the mixed-subjects
  design of Broska, Howes and van Loon (2025) <https://doi.org/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.
authors:
- family-names: Kanopka
  given-names: Klint
  email: klint.kanopka@nyu.edu
  orcid: https://orcid.org/0000-0003-3196-9538
repository: https://cran.r-universe.dev
repository-code: https://github.com/klintkanopka/mixedsubjectsirt
commit: 2e77546e86fd98746edc454a11c8d8112e7b2aa4
url: https://klintkanopka.com/mixedsubjectsirt/
date-released: '2026-06-25'
contact:
- family-names: Kanopka
  given-names: Klint
  email: klint.kanopka@nyu.edu
  orcid: https://orcid.org/0000-0003-3196-9538