missCompare: Intuitive Missing Data Imputation Framework

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 ORCID iD [aut, cre], David Westergaard ORCID iD [aut]
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: not available
OS X binaries: r-release: missCompare_1.0.1.tgz, r-oldrel: not available


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