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misl

Multiple Imputation by Super Learning

v1.0.0 · Mar 30, 2026 · MIT + file LICENSE

Description

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.

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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Check History

OK 7 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 31, 2026

Dependency Network

Dependencies Reverse dependencies dplyr future.apply parsnip (>= 1.2.0) recipes rsample stacks (>= 1.0.0) tibble tidyr tune (>= 1.2.0) workflows misl

Version History

new 1.0.0 Mar 30, 2026