Performs multiple imputation of missing data using an ensemble
super learner built with the tidymodels framework. For each incomplete
column, a stacked ensemble of candidate learners is trained on a bootstrap
sample of the observed data and used to generate imputations via predictive
mean matching (continuous), probability draws (binary), or cumulative
probability draws (categorical). Supports parallelism across imputed
datasets via the future framework.
| Version: |
2.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr (≥ 1.1.0), future.apply (≥ 1.11.0), parsnip (≥
1.2.0), recipes (≥ 1.0.0), rsample (≥ 1.2.0), stacks (≥
1.0.0), stats, tibble (≥ 3.2.0), tidyr (≥ 1.3.0), tune (≥
1.2.0), utils, workflows (≥ 1.1.0) |
| Suggests: |
earth (≥ 5.3.0), future (≥ 1.33.0), ggforce, ggplot2, knitr, MASS, ranger (≥ 0.16.0), rmarkdown, scales, testthat (≥ 3.0.0), xgboost (≥ 1.7.0) |
| Published: |
2026-04-08 |
| DOI: |
10.32614/CRAN.package.misl |
| Author: |
Justin Manjourides
 [aut, cre],
Thomas Carpenito
[aut] |
| Maintainer: |
Justin Manjourides <j.manjourides at northeastern.edu> |
| BugReports: |
https://github.com/JustinManjourides/misl/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/JustinManjourides/misl |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
misl results |