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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.0 |
rolling linux/jammy R-4.5 | misl_2.0.0.tar.gz |
65.7 KiB |
2.0.0 |
rolling linux/noble R-4.5 | misl_2.0.0.tar.gz |
65.6 KiB |
2.0.0 |
rolling source/ R- | misl_2.0.0.tar.gz |
31.8 KiB |
2.0.0 |
latest linux/jammy R-4.5 | misl_2.0.0.tar.gz |
65.7 KiB |
2.0.0 |
latest linux/noble R-4.5 | misl_2.0.0.tar.gz |
65.6 KiB |
2.0.0 |
latest source/ R- | misl_2.0.0.tar.gz |
31.8 KiB |
2.0.0 |
2026-04-26 source/ R- | misl_2.0.0.tar.gz |
31.8 KiB |
2.0.0 |
2026-04-23 source/ R- | misl_2.0.0.tar.gz |
31.8 KiB |
2.0.0 |
2026-04-09 windows/windows R-4.5 | misl_2.0.0.zip |
69.7 KiB |