mixgb: Multiple Imputation Through 'XGBoost'
Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.48550/arXiv.2106.01574>. Our method utilizes the capabilities of XGBoost, a highly efficient implementation of gradient boosted trees, to capture interactions and non-linear relations automatically. Moreover, we have integrated subsampling and predictive mean matching to minimize bias and reflect appropriate imputation variability. This package supports various types of variables and offers flexible settings for subsampling and predictive mean matching. Additionally, it includes diagnostic tools for evaluating the quality of the imputed values.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | data.table, ggplot2, Matrix, mice, Rfast, rlang, scales, stats, tidyr, utils, xgboost |
| Suggests: | knitr, rmarkdown, RColorBrewer |
| Published: | 2023-02-16 |
| DOI: | 10.32614/CRAN.package.mixgb |
| Author: | Yongshi Deng 
[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 |
| CRAN checks: | mixgb results |
Documentation:
| Reference manual: | mixgb.pdf |
| Vignettes: |
Imputing newdata with a saved mixgb imputer mixgb: Multiple Imputation Through XGBoost |
Downloads:
| Package source: | mixgb_1.0.2.tar.gz |
| Windows binaries: | r-devel: mixgb_1.0.2.zip, r-release: mixgb_1.0.2.zip, r-oldrel: mixgb_1.0.2.zip |
| macOS binaries: | r-release (arm64): mixgb_1.0.2.tgz, r-oldrel (arm64): mixgb_1.0.2.tgz, r-release (x86_64): mixgb_1.0.2.tgz, r-oldrel (x86_64): mixgb_1.0.2.tgz |
| Old sources: | mixgb archive |
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