hdImpute: A Batch Process for High Dimensional Imputation
A correlation-based batch process for fast imputation for
high dimensional missing data problems via chained random forests.
See Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597>
for more on missForest, and Mayer (2022)
<https://github.com/mayer79/missRanger> for more on missRanger.
| Version: |
0.1.1 |
| Imports: |
missRanger, plyr, purrr, magrittr, tibble, dplyr, tidyselect, cli |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, usethis, missForest, tidyverse |
| Published: |
2022-04-20 |
| Author: |
Philip Waggoner [aut, cre] |
| Maintainer: |
Philip Waggoner <philip.waggoner at gmail.com> |
| BugReports: |
https://github.com/pdwaggoner/hdImpute/issues |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
hdImpute results |
Documentation:
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