https://klintkanopka.r-universe.devRecent package updates in klintkanopkaR-universehttps://github.com/klintkanopka.pnghttps://klintkanopka.r-universe.devThu, 25 Jun 2026 17:38:01 GMTklint.kanopka@nyu.edu (Klint Kanopka)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.https://github.com/r-universe/klintkanopka/actions/runs/28226552438Thu, 25 Jun 2026 17:38:01 GMTmixedsubjectsirt1.0.0successhttps://klintkanopka.r-universe.devhttps://github.com/klintkanopka/mixedsubjectsirtweakly-informative-llm.Rmdweakly-informative-llm.htmlCalibrating with a Weakly-Informative, Biased LLM2026-06-11 03:50:582026-06-25 17:38:01lambda-tuning.Rmdlambda-tuning.htmlChoosing Lambda in Mixed-Subjects IRT2026-06-01 19:53:482026-06-25 17:38:01linking-comparison.Rmdlinking-comparison.htmlIRT Linking and Gradient Asymmetry: Diagnostic Guide2026-06-03 18:33:272026-06-25 17:38:01mixed-subjects-1pl.Rmdmixed-subjects-1pl.htmlMixed-Subjects 1PL Calibration2026-06-05 00:54:162026-06-11 03:50:58mixed-subjects-workflow.Rmdmixed-subjects-workflow.htmlMixed-Subjects IRT Calibration2026-06-01 19:09:422026-06-25 17:38:01lambda-tuning-item.Rmdlambda-tuning-item.htmlPer-Item Lambda (Experimental)2026-06-05 00:54:162026-06-25 17:38:01simulation-validation.Rmdsimulation-validation.htmlSimulation Validation of the Mixed-Subjects MML Estimator2026-06-09 14:29:522026-06-25 17:38:01understanding-ability-risk.Rmdunderstanding-ability-risk.htmlUnderstanding Ability-Risk Tuning2026-06-25 17:38:012026-06-25 17:38:01