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opImputation: Optimal Selection of Imputation Methods for Pain-Related
Numerical Data
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 (may not be active yet) |
| 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 |
Documentation:
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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.