missForest
Nonparametric Missing Value Imputation using Random Forest
The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.6.1 |
rolling linux/jammy R-4.5 | missForest_1.6.1.tar.gz |
289.7 KiB |
1.6.1 |
rolling linux/noble R-4.5 | missForest_1.6.1.tar.gz |
289.7 KiB |
1.6.1 |
rolling source/ R- | missForest_1.6.1.tar.gz |
262.3 KiB |
1.6.1 |
latest linux/jammy R-4.5 | missForest_1.6.1.tar.gz |
289.7 KiB |
1.6.1 |
latest linux/noble R-4.5 | missForest_1.6.1.tar.gz |
289.7 KiB |
1.6.1 |
latest source/ R- | missForest_1.6.1.tar.gz |
262.3 KiB |
1.6.1 |
2026-04-26 source/ R- | missForest_1.6.1.tar.gz |
262.3 KiB |
1.6.1 |
2026-04-23 source/ R- | missForest_1.6.1.tar.gz |
262.3 KiB |
1.6.1 |
2026-04-09 windows/windows R-4.5 | missForest_1.6.1.zip |
289.6 KiB |
1.5 |
2025-04-20 source/ R- | missForest_1.5.tar.gz |
309.2 KiB |