Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. The central assumption behind missCompare is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
Version: | 1.0.1 |
Depends: | R (≥ 3.5.0) |
Imports: | Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm, magrittr, MASS, Matrix, mi, mice, missForest, missMDA, pcaMethods, plyr, rlang, stats, utils, tidyr, VIM |
Suggests: | testthat, knitr, rmarkdown, devtools |
Published: | 2019-02-05 |
Author: | Tibor V. Varga |
Maintainer: | Tibor V. Varga <tirgit at hotmail.com> |
BugReports: | https://github.com/Tirgit/missCompare/issues |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Materials: | NEWS |
CRAN checks: | missCompare results |
Reference manual: | missCompare.pdf |
Vignettes: |
missCompare |
Package source: | missCompare_1.0.1.tar.gz |
Windows binaries: | r-devel: missCompare_1.0.1.zip, r-release: missCompare_1.0.1.zip, r-oldrel: missCompare_1.0.1.zip |
OS X binaries: | r-release: not available, r-oldrel: missCompare_1.0.1.tgz |
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