DGCIT: Double Generative Adversarial Networks for Conditional Independence Testing (JMLR, 2021)
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Updated
Jan 19, 2022 - Python
DGCIT: Double Generative Adversarial Networks for Conditional Independence Testing (JMLR, 2021)
Learning non-Gaussian graphical models
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Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.
Variable Selection with Knockoffs
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Knockoff-based analysis of GWAS summary statistics data
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(Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information estimators. Supports continuous, discrete, and mixed data, as well as multiprocessing.
The Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT)
Code for the paper "Estimating Transfer Entropy via Copula Entropy"
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