Changes in version 1.0.0 (2026-06-25)                  

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.