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.