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mixgb (version 1.5.3)

Multiple Imputation Through 'XGBoost'

Description

Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) . 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.

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Install

install.packages('mixgb')

Monthly Downloads

337

Version

1.5.3

License

GPL (>= 3)

Issues

3

Pull Requests

0

Stars

23

Forks

5

Maintainer

Yongshi Deng

Last Published

April 6th, 2025

Functions in mixgb (1.5.3)

sortNA

Sort data by increasing number of missing values
pmm.multiclass

PMM for multiclass variable
show_var

Show multiply imputed values for a single variable
data_clean

Data cleaning
mixgb_cv

Use cross-validation to find the optimal nrounds
impute_new

Impute new data with a saved mixgb imputer object
createNA

Create missing values for a dataset
nhanes3_newborn

NHANES III (1988-1994) newborn data
mixgb

Multiple imputation through XGBoost
default_params

Auxiliary function for validating xgb.params
default_params_cran

Auxiliary function for validating xgb.params compatible with XGBoost CRAN version
pmm

PMM for numeric or binary variable
nhanes3

A small subset of the NHANES III (1988-1994) newborn data
mixgb-package

mixgb: Multiple Imputation Through 'XGBoost'
%>%

Pipe operator