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)
Suggests: testthat, knitr, rmarkdown, devtools
Published: 2019-02-05
Maintainer: Tibor V. Varga <tirgit at hotmail.com>
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

