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.