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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 - https://doi.org/10.1198/jasa.2009.0126 SCIO - Fast and adaptive sparse precision matrix estimation in high dimensions - Liu and Luo - https://doi.org/10.1016/j.jmva.2014.11.005 CLIME - A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation - Cai, Liu and Luo - https://doi.org/10.1198/jasa.2011.tm10155 DTrace - Sparse precision matrix estimation via lasso penalized D-trace loss - Zou and Zhang - https://doi.org/10.1093/biomet/ast059 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 - http://www.jmlr.org/papers/volume14/sun13a/sun13a.pdf

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