IsingFit

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This network estimation procedure combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion to identify relevant symptom-symptom relationships. These relationships define connections in a network.

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IsingFit
==========

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

## Background Information
For more information on IsingFit, take a look at:

Van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific reports, 4(1), 5918.

## Bug Reports, Feature Request, or Contributing
If you encounter any bugs or have ideas for new features, you can submit them by creating an issue on Github. Additionally, if you want to contribute to the development of IsingFit, you can initiate a branch with a pull request; we can review and discuss the proposed changes.

## Credits
The package was developed by [Claudia van Borkulo](https://cvborkulo.com/) during her PhD at the University of Amsterdam. It is now maintained by [Sacha Epskamp](https://scholar.google.nl/citations?hl=en&user=fQpiw-sAAAAJ), an Associate Professor at the National University of Singapore: Department of Psychology.

IsingFit

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