Tirgit/missCompare: 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.

Getting started

Package details

MaintainerTibor V. Varga <[email protected]>
LicenseMIT + file LICENSE
Version1.0.1.9000
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("Tirgit/missCompare")
Tirgit/missCompare documentation built on May 25, 2019, 2:57 a.m.