Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS

Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS

missing data, multiple imputation, machine learning, Python, R.

This paper introduces software packages for efficiently imputing missing data using
deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement
a recently developed approach to multiple imputation known as MIDAS, which involves
introducing additional missing values into the dataset, attempting to reconstruct these
values with a type of unsupervised neural network known as a denoising autoencoder, and
using the resulting model to draw imputations of originally missing data. These steps are
executed by a fast and flexible algorithm that expands both the quantity and the range of
data that can be analyzed with multiple imputation. To help users optimize the algorithm
for their particular application, MIDASpy and rMIDAS offer a host of user-friendly tools
for calibrating and validating the imputation model. We provide a detailed guide to these
functionalities and demonstrate their usage on a large real dataset.

Ranjit Lall

https://www.jstatsoft.org/article/view/v107i09

University of Oxford

October 2023

Fajar bagus W

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England

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