misl: Multiple Imputation by Super Learning

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:	1.0.0
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), knitr, ranger (≥ 0.16.0), rmarkdown, testthat (≥ 3.0.0), xgboost (≥ 1.7.0)
Published:	2026-03-30
DOI:	10.32614/CRAN.package.misl
Author:	Justin Manjourides [aut, cre]
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

Documentation:

Reference manual:	misl.html , misl.pdf
Vignettes:	Introduction to misl (source, R code)

Downloads:

Package source:	misl_1.0.0.tar.gz
Windows binaries:	r-devel: misl_1.0.0.zip, r-release: misl_1.0.0.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): misl_1.0.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): misl_1.0.0.tgz, r-oldrel (x86_64): misl_1.0.0.tgz

Linking:

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