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 [aut, cre], David Westergaard [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

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