Metadata-Version: 2.1
Name: nitk
Version: 0.1
Summary: A package for inferring sparse partial correlation networks
Home-page: https://github.com/shazzzm/nitk
Author: Tristan Millington
Author-email: tristan.millington@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 2 - Pre-Alpha
Description-Content-Type: text/markdown

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



