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Polkas merged 1 commit into from
Feb 25, 2026
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v0.9.0 #33

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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: miceFast
Title: Fast Imputations Using 'Rcpp' and 'Armadillo'
Version: 0.8.5.9000
Version: 0.9.0
Authors@R: person("Maciej", "Nasinski", email = "nasinski.maciej@gmail.com", role = c("aut", "cre"))
Description:
Fast imputations under the object-oriented programming paradigm.
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2 changes: 1 addition & 1 deletion NEWS.md
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# miceFast 0.8.5.9000
# miceFast 0.9.0

Kota Hattori, thank you for your feedback and for motivating me for this deep update.

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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -127,7 +127,7 @@ See the [Introduction vignette](https://polkas.github.io/miceFast/articles/miceF
- **Object-Oriented Interface** via `miceFast` objects (Rcpp modules).
- **Convenient Helpers**:
- `fill_NA()`: Single imputation (`lda`, `lm_pred`, `lm_bayes`, `lm_noise`).
- `fill_NA_N()`: Multiple imputations (`pmm`, `lm_bayes`, `lm_noise`).
- `fill_NA_N()`: Averaged multiple imputations (mean of N draws) (`pmm`, `lm_bayes`, `lm_noise`).
- `pool()`: Pool multiply imputed results using Rubin's rules.
- `VIF()`: Variance Inflation Factor calculations.
- `naive_fill_NA()`: Automatic naive imputations.
Expand All @@ -151,6 +151,7 @@ See the [Introduction vignette](https://polkas.github.io/miceFast/articles/miceF

## Practical Advice

- **Only need a filled-in dataset for exploration or ML?** A single imputation with `fill_NA()` or averaging draws with `fill_NA_N()` is fast and convenient. For any inferential statement use full MI with `pool()`.
- **Little missing data + MCAR?** Consider using `complete.cases()` — listwise deletion is unbiased under MCAR and may be sufficient when the fraction of incomplete rows is small.
- **For publication**, always run a **sensitivity analysis**: compare MI results against base methods (`complete.cases()`, mean imputation) and across different imputation models (`lm_bayes`, `lm_noise`, `pmm`). Vary the number of imputations. If conclusions change, investigate why. Report the imputation model, *m*, and any assumptions about the missing-data mechanism.
- See the [MI vignette](https://polkas.github.io/miceFast/articles/missing-data-and-imputation.html) for details on MCAR/MAR/MNAR mechanisms and a practical checklist.
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