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EDAmame: Interactive exploratory data analyses with explainable models

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Chuah, Aaron
Hewitt, Tim C.
Ali, Sidra A.
May, Maryam
Xu, Tony
Christiadi, Daniel
Choi, Philip Y.I.
Gardiner, Elizabeth E.
Andrews, T. Daniel

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Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements.

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Bioinformatics

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