LucasKook/comets
Algorithm-agnostic significance testing in supervised learning with multimodal data
This tool helps researchers and data scientists determine if a variable (or set of variables) is genuinely related to an outcome, even when other confounding factors are present. You provide your outcome data, the variables you're testing, and the confounding variables. The tool then calculates a p-value indicating the statistical significance of the relationship, helping you make informed decisions about feature importance or causal links. It is used by statisticians, data analysts, and researchers working with complex datasets.
Use this if you need to rigorously test the conditional independence between a response variable and a set of features, accounting for other confounding variables, using various supervised learning models.
Not ideal if you are looking for a simple correlation analysis or if you are not comfortable working with statistical programming in R.
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Language
R
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Last pushed
Nov 05, 2025
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