CRAN
missCompare 1.0.1
Intuitive Missing Data Imputation Framework
Released Feb 5, 2019 by Tibor V. Varga
Dependencies
tidyr 0.8.2 Hmisc 4.2-0 data.table 1.12.0 missMDA 1.14 rlang 0.3.1 ggplot2 3.1.0 dplyr 0.7.8 VIM 4.7.0 mice 3.3.0 Amelia 1.7.5 plyr 1.8.4 mi 1.0 ltm 1.1-1 ggdendro 0.1-20 Matrix 1.2-15 missForest 1.4 MASS 7.3-51.1 magrittr 1.5
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.