NEWS.md
In misl: Multiple Imputation by Super Learning

misl 2.0.0

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.

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.