mixgb: Multiple Imputation Through 'XGBoost'

Multiple imputation using 'XGBoost', bootstrapping and predictive mean matching as described in Deng and Lumley (2021) <arXiv:2106.01574>. It is built under Fully Conditional Specification, where 'XGBoost' imputation models are built for each incomplete variable. It supports various types of variables and offers different settings regarding bootstrapping and predictive mean matching. Visual diagnostic functions are also provided for inspecting multiply imputed values for incomplete variables.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: data.table, ggplot2, Matrix, mice, Rfast, rlang, scales, stats, tidyr, utils, xgboost
Suggests: knitr, rmarkdown, RColorBrewer
Published: 2022-06-07
Author: Yongshi Deng ORCID iD [aut, cre], Thomas Lumley [ths]
Maintainer: Yongshi Deng <yongshi.deng at auckland.ac.nz>
BugReports: https://github.com/agnesdeng/mixgb/issues
License: GPL (≥ 3)
URL: https://github.com/agnesdeng/mixgb, https://agnesdeng.github.io/mixgb/
NeedsCompilation: no
Materials: README
CRAN checks: mixgb results


Reference manual: mixgb.pdf
Vignettes: Imputing newdata with a saved mixgb imputer
mixgb: Multiple Imputation Through XGBoost


Package source: mixgb_0.1.0.tar.gz
Windows binaries: r-devel: mixgb_0.1.0.zip, r-release: mixgb_0.1.0.zip, r-oldrel: mixgb_0.1.0.zip
macOS binaries: r-release (arm64): mixgb_0.1.0.tgz, r-oldrel (arm64): mixgb_0.1.0.tgz, r-release (x86_64): mixgb_0.1.0.tgz, r-oldrel (x86_64): mixgb_0.1.0.tgz


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