Multiple Imputation by Super Learning R package misl version 1.0.0 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. Software 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), knitr, ranger (>= 0.16.0), rmarkdown, testthat (>= 3.0.0), xgboost (>= 1.7.0) Justin Manjourides <j.manjourides@northeastern.edu> Comprehensive R Archive Network (CRAN) Justin Manjourides [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=misl/LICENSE) 2026-03-30 application/tgz https://CRAN.R-project.org/package=misl doi:10.32614/CRAN.package.misl