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.
|Author||Tibor V. Varga [aut, cre] (<https://orcid.org/0000-0002-2383-699X>), David Westergaard [aut] (<https://orcid.org/0000-0003-0128-8432>)|
|Maintainer||Tibor V. Varga <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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