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.


News

missCompare 1.0.1

CRAN resubmission of missCompare. Fix of minor bugs.

missCompare 1.0.0

The first stable version of missCompare.

Reference manual

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install.packages("missCompare")

1.0.1 by Tibor V. Varga, 8 months ago


Report a bug at https://github.com/Tirgit/missCompare/issues


Browse source code at https://github.com/cran/missCompare


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