Multiple Imputation Through 'XGBoost'
Show multiply imputed values for a single variable
Sort data by increasing number of missing values
PMM for numeric or binary variable
Create missing values for a dataset
Data cleaning
mixgb: Multiple Imputation Through 'XGBoost'
Auxiliary function for validating xgb.params compatible with XGBoost C...
Auxiliary function for validating xgb.params
Impute new data with a saved mixgb imputer object
Use cross-validation to find the optimal nrounds
Multiple imputation through XGBoost
Pipe operator
PMM for multiclass variable
Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.