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A package for inferring sparse partial correlation networks

Project description

Network Inference Toolkit

A bunch of scripts written to infer correlation/partial correlation networks from data. The goal is to have them like sklearn models. Currently very much a work in progress

Implemented: SPACE - Partial Correlation Estimation by Joint Sparse Regression Models by Peng, Wang and Zhu - SCIO - Fast and adaptive sparse precision matrix estimation in high dimensions - Liu and Luo - CLIME - A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation - Cai, Liu and Luo - DTrace - Sparse precision matrix estimation via lasso penalized D-trace loss - Zou and Zhang - Correlation Permutation - Estimates a sparse correlation matrix by permuting the dataset repeatedly to get a p-value to see if the correlation between two variables is just as likely to occur through noise Scaled Lasso - "Sparse Matrix Inversion with Scaled Lasso" by Sun and Zhang -

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