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 B├╝hlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on 'missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on 'missRanger'.

Version: 0.2.1
Imports: missRanger, plyr, purrr, magrittr, tibble, dplyr, tidyselect, tidyr, cli
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, usethis, missForest, tidyverse
Published: 2023-08-07
Author: Philip Waggoner [aut, cre]
Maintainer: Philip Waggoner <philip.waggoner at gmail.com>
BugReports: https://github.com/pdwaggoner/hdImpute/issues
License: MIT + file LICENSE
URL: https://github.com/pdwaggoner/hdImpute
NeedsCompilation: no
Materials: README NEWS
CRAN checks: hdImpute results

Documentation:

Reference manual: hdImpute.pdf
Vignettes: Getting Started
MAD Evaluation
NA Checking

Downloads:

Package source: hdImpute_0.2.1.tar.gz
Windows binaries: r-devel: hdImpute_0.2.1.zip, r-release: hdImpute_0.2.1.zip, r-oldrel: hdImpute_0.2.1.zip
macOS binaries: r-release (arm64): hdImpute_0.2.1.tgz, r-oldrel (arm64): hdImpute_0.2.1.tgz, r-release (x86_64): hdImpute_0.2.1.tgz
Old sources: hdImpute archive

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