mixgb-package

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

internal

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.

Yongshi Deng

mixgb

Multiple Imputation Through 'XGBoost'

Thomas Lumley

mixgb-package function

Maintainer: Yongshi Deng <a href="/link/agnes.yongshideng%40gmail.com?package=mixgb&version=1.5.2" data-mini-rdoc="mixgb::agnes.yongshideng@gmail.com">agnes.yongshideng@gmail.com</a> (<a href="https://orcid.org/0000-0001-5845-859X">ORCID</a>)
Other contributors:<ul>
<li>Thomas Lumley <a href="/link/t.lumley%40auckland.ac.nz?package=mixgb&version=1.5.2" data-mini-rdoc="mixgb::t.lumley@auckland.ac.nz">t.lumley@auckland.ac.nz</a> [thesis advisor]</li>
</ul>

Author

mixgb: Multiple Imputation Through 'XGBoost' — mixgb-package

Maintainer: Yongshi Deng <a href='mailto:agnes.yongshideng@gmail.com'>agnes.yongshideng@gmail.com</a> (<a href='https://orcid.org/0000-0001-5845-859X'>ORCID</a>)
Other contributors:<ul>
<li>Thomas Lumley <a href='mailto:t.lumley@auckland.ac.nz'>t.lumley@auckland.ac.nz</a> [thesis advisor]</li>
</ul>

mixgb-package: mixgb: Multiple Imputation Through 'XGBoost'

Description

Arguments

Author

See Also