Changes in version 2.0.0 (2026-04-08)                  

New features

  - misl() now accepts any parsnip-compatible model spec directly via
    the *_method arguments, allowing any learner in the tidymodels
    ecosystem to be used without requiring a package update. Named
    strings and parsnip specs can be freely mixed in the same list.

  - New ord_method argument in misl() for ordered categorical (ordinal)
    outcomes. Ordered factors are automatically detected and routed to
    ord_method.

  - New built-in learner "polr" (proportional odds logistic regression
    via MASS::polr()) for ordinal outcomes. Note that "polr" cannot
    currently be stacked with other ordinal learners — when supplied
    alongside other learners in ord_method, "polr" will be used as the
    sole ordinal learner and others will be ignored with a warning. Full
    stacking support for ordinal outcomes is planned for a future
    release.

  - New plot_misl_trace() function for visualising convergence of
    imputed values across iterations, with one line per imputed dataset.

  - list_learners() now accepts "ordinal" as a valid outcome_type filter
    and includes the new ordinal support column.

Bug fixes

  - cv_folds argument is now correctly passed through to the internal
    .fit_super_learner() function. Previously the value was accepted by
    misl() but ignored, with 5 folds always used regardless of the
    user's setting.

  - The preprocessing recipe is now built once from the full observed
    data and shared across both the bootstrap and full fits for
    continuous outcomes. Previously separate recipes were built for each
    fit, meaning step_zv() and step_nzv() could drop different
    predictors between the two fits and produce inconsistent PMM donor
    selection.

  - Two-level factors (e.g. "Yes"/"No") are now correctly identified as
    binomial outcomes rather than categorical. Previously these were
    routed to cat_method, which caused incorrect imputation and errors
    with learners that require a numeric binary outcome.

  - Trace statistics (mean, sd) are no longer computed for factor-coded
    binary columns, preventing errors from calling mean() and var() on
    factor vectors.

  - Stacking now gracefully recovers when individual learners fail
    during cross-validation resampling. Previously a single learner
    failure would crash the entire imputation. Failed learners are now
    skipped with a warning, and if all learners fail the first learner
    is used as a fallback.

                 Changes in version 1.0.0 (2026-03-30)                  

  - Initial CRAN submission.