R: Multiple Imputation using Chained Random Forests

# Multiple Imputation using Chained Random Forests

## Documentation for package ‘RfEmpImp’ version 2.1.8

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

conv.factor | Convert variables to factors
gen.mcar | Generate missing (completely at random) cells in a data set
imp.rfemp | Perform multiple imputation using the empirical error distributions and predicted probabilities of random forests
imp.rfnode.cond | Perform multiple imputation based on the conditional distribution formed by prediction nodes of random forests
imp.rfnode.prox | Perform multiple imputation based on the conditional distribution formed using node proximity
mice.impute.rfemp | Univariate sampler function for mixed types of variables for prediction-based imputation, using empirical distribution of out-of-bag prediction errors and predicted probabilities of random forests
mice.impute.rfnode | Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfnode.cond | Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfnode.prox | Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfpred.cate | Univariate sampler function for categorical variables for prediction-based imputation, using predicted probabilities of random forest
mice.impute.rfpred.emp | Univariate sampler function for continuous variables using the empirical error distributions
mice.impute.rfpred.norm | Univariate sampler function for continuous variables for prediction-based imputation, assuming normality for prediction errors of random forest
query.rf.pred.idx | Identify corresponding observations indexes under the terminal nodes for a random forest model by 'ranger'
query.rf.pred.val | Identify corresponding observed values for the response variable under the terminal nodes for a random forest model by 'ranger'
rangerCallerSafe | Remove unnecessary arguments for 'ranger' function
reg.ests | Get regression estimates for pooled object