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mimar 0.8.0

• Added 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.
• Added library, folds, and metalearner hyperparameters for superlearner.
• Updated CRAN preparation files and vignette examples for the new release.

mimar 0.7

First public release candidate.

• Added ncore to impute() for completed-dataset-level parallel imputation through functionals::fmap().
• Added lightweight iteration traces to mimar_imputation diagnostics for convergence screening.
• Added diagnostic plot types for boxplots, bivariate observed/imputed comparisons, categorical proportions, and trace summaries.
• Updated density diagnostics to draw line-only overlays across imputations so multiple completed datasets remain visible.
• Refreshed the diagnostic plotting palette to give mimar a distinct visual identity while retaining the existing plot themes.
• Expanded the vignette with KNN-based diagnostic examples, parallel imputation notes, and interpretation guidance.

mimar 0.0.1

• Initial compact missing-data grammar.
• Added description, amputation, imputation, evaluation, pooling, and plotting.
• Added chained native and optional learner-backed imputation adapters without a funcml dependency.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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