Package: mixgb
Title: Multiple Imputation Through 'XGBoost'
Version: 1.0.2
Authors@R: c(
    person(given = "Yongshi", family ="Deng", email = "yongshi.deng@auckland.ac.nz",
           role = c("aut","cre"),comment =c(ORCID = "0000-0001-5845-859X")),
    person(given = "Thomas", family = "Lumley", email = "t.lumley@auckland.ac.nz",
           role = "ths")
  ) 
Description: Multiple imputation using 'XGBoost', subsampling, and predictive mean 
    matching as described in Deng and Lumley (2023) <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.
URL: https://github.com/agnesdeng/mixgb,
        https://agnesdeng.github.io/mixgb/
BugReports: https://github.com/agnesdeng/mixgb/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: data.table, ggplot2, Matrix, mice, Rfast, rlang, scales,
        stats, tidyr, utils, xgboost
Suggests: knitr, rmarkdown, RColorBrewer
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
RoxygenNote: 7.2.0
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2023-02-16 09:15:12 UTC; agnes
Author: Yongshi Deng [aut, cre] (<https://orcid.org/0000-0001-5845-859X>),
  Thomas Lumley [ths]
Maintainer: Yongshi Deng <yongshi.deng@auckland.ac.nz>
Repository: CRAN
Date/Publication: 2023-02-16 11:00:02 UTC
Built: R 4.1.2; ; 2023-02-17 12:32:32 UTC; unix
