missCompare: Intuitive Missing Data Imputation Framework
Offers a convenient pipeline to test and compare various missing data
imputation algorithms on simulated and real data. These include simpler methods, such as mean and median
imputation and random replacement, but also include more sophisticated algorithms already implemented in popular
R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren
and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012)
<doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and
'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. 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.
||R (≥ 3.5.0)
||Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm, magrittr, MASS, Matrix, mi, mice, missForest, missMDA, pcaMethods, plyr, rlang, stats, utils, tidyr, VIM
||testthat, knitr, rmarkdown, devtools
||Tibor V. Varga
||Tibor V. Varga <tirgit at hotmail.com>
||MIT + file LICENSE
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