opImputation: Optimal Selection of Imputation Methods for Pain-Related Numerical Data (original) (raw)
A model-agnostic framework for selecting dataset-specific imputation methods for missing values in numerical data related to pain. Lotsch J, Ultsch A (2025) "A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data" Canadian Journal of Pain (in minor revision).
| Version: | 0.6 |
|---|---|
| Depends: | R (≥ 3.5.0) |
| Imports: | parallel, Rfit, methods, stats, caret, ABCanalysis, ggplot2, future, future.apply, progressr, missForest, utils, mice, miceRanger, multiUS, Amelia, mi, reshape2, DataVisualizations, abind, cowplot, twosamples, ggh4x, ggrepel, tools |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-11-07 |
| DOI: | 10.32614/CRAN.package.opImputation |
| Author: | Jorn Lotsch  [aut, cre], Alfred Ultsch [aut] |
| Maintainer: | Jorn Lotsch <j.lotsch at em.uni-frankfurt.de> |
| License: | GPL-3 |
| URL: | https://github.com/JornLotsch/opImputation |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | opImputation results |
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