High-Performance Open-Source Archive
superlearner and sl imputers. These
construct a Super Learner-style ensemble by cross-validating candidate
imputers on observed cells, assigning non-negative loss-based weights,
and combining predictions inside the existing chained-imputation
loop.library, folds, and
metalearner hyperparameters for
superlearner.First public release candidate.
ncore to impute() for
completed-dataset-level parallel imputation through
functionals::fmap().mimar_imputation
diagnostics for convergence screening.mimar
a distinct visual identity while retaining the existing plot
themes.funcml dependency.
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