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.


Reference manual

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1.0.1 by Tibor V. Varga, 14 days ago

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Authors: Tibor V. Varga [aut, cre] , David Westergaard [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

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

See at CRAN