missCompare: 'missCompare': Missing Data Imputation Comparison Framework
In missCompare: Intuitive Missing Data Imputation Framework

Description Details Author(s) See Also

The 'missCompare' package offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated 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 and other combinations) 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 set missingness patterns. 'missCompare' will give you a comparative analysis of missing data imputation algorithms, offer a report with the best performing algorithms assuming various missing data patterns and publication ready visualizations, impute your dataset for you, assess imputation performance using a validation framework and help you better understand missing data in your dataset.