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mixedsubjectsirt 1.0.0

Initial CRAN release.

• Mixed-subjects 2PL/1PL IRT calibration that augments human responses with LLM-generated responses through a PPI++ marginal-MML estimator (fit_mixed_subjects_mml() and relatives). The estimator is anchored to the human data and is asymptotically unbiased for the human item parameters at any tuning weight.
• Power tuning by ability-score risk (tune_lambda_ability_risk()), which selects the tuning weight by direct 1-D optimization of propagated ability-recovery risk (pass method = "grid" to scan a grid instead). Also included: a theoretical PPI++ score diagnostic (tune_lambda_ppi_score()), cross-fitted tuning (tune_lambda_ability_risk_crossfit(), the recommended workflow for reported analyses), and experimental per-item tuning (tune_lambda_ability_risk_item()). All non-experimental tuners use the marginal-MML estimator by default; the frozen expected-count estimator remains available via fit_fn but is discouraged.
• Louis-corrected marginal sandwich covariance through the vcov() S3 method (vcov_mixed_subjects_mml()), with ability scoring and item-parameter uncertainty propagation (score_theta(), ability_risk()).
• Vignettes covering the recommended workflow, lambda tuning, the 1PL model, per-item tuning, scale linking, and a simulation-validation study; an R-CMD-check GitHub Actions workflow.
• Currently predicted and generated data must be binary 0/1 responses in the high-level fitting and PPI-score functions; the low-level quadrature utilities accept fractional input.

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.