Multiple Imputation Through 'XGBoost'

Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) . 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.


Reference manual

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install.packages("mixgb")

2.2.3 by Yongshi Deng, 9 months ago


https://github.com/agnesdeng/mixgb


Report a bug at https://github.com/agnesdeng/mixgb/issues


Browse source code at https://github.com/cran/mixgb


Authors: Yongshi Deng [aut, cre] (ORCID: , Thomas Lumley [ths]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports cli, data.table, Matrix, mice, Rcpp, Rfast, stats, utils, xgboost

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo

System requirements: macOS: Accelerate framework


Suggested by vismi.


See at CRAN