| gen.mcar | Generate missing (completely at random) cells in the dataset |
| imp.rfemp | Perform multiple imputation based on the empirical error distribution of random forests |
| imp.rfnode.cond | Perform multiple imputation based on the conditional distribution formed by prediction nodes of random forests |
| imp.rfnode.prox | Multiple imputation using chained random forests and node proximities |
| mice.impute.rfemp | Multiple imputation for categorical variables based on predictions of random forest |
| mice.impute.rfnode | Sampling function for multiple imputation based on predicting nodes of random forests |
| mice.impute.rfnode.cond | Sampling function for multiple imputation based on predicting nodes of random forests |
| mice.impute.rfnode.prox | Sampling function for multiple imputation based on predicting nodes of random forests |
| mice.impute.rfpred.cate | Multiple imputation for categorical variables based on predictions of random forest |
| mice.impute.rfpred.emp | Multiple imputation using chained random forests: RfPred.Emp |
| mice.impute.rfpred.norm | Multiple imputation using chained random forests: RfPred.Norm |
| reg.ests | Get regression estimates for pooled object |