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 |