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hdImpute: A Batch Process for High Dimensional Imputation
A correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on 'hdImpute', 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'.
Getting started
Browse package contents
Package details |
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| Author | Philip Waggoner [aut, cre] |
| Maintainer | Philip Waggoner <philip.waggoner@gmail.com> |
| License | MIT + file LICENSE |
| Version | 0.2.1 |
| URL | https://github.com/pdwaggoner/hdImpute |
| Package repository | View on CRAN |
| Installation |
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