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Tools for working with compressed graphs and solving forward and the inverse problems in undersampling and latent variable cases. The package provides implementation of MSL, RASL, ASP-based, CSP-based, and other algorithms

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neuroneural/gunfolds

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gunfolds

Tools to explore dynamic causal graphs in the case of undersampled data helping to unfold the apparent structure into the underlying truth.

Documentation

Please refer to the Documentation for more information.

Installation

Install the gunfolds package

   pip install gunfolds

Additionally, install these packages to use gunfolds

graph-tool installation

1. Install graph-tool

To install graph-tool package with conda install run the following command

   conda install -c conda-forge graph-tool

To install graph-tool package with brew install run the following command

   brew install graph-tool

PyGObject installation

2. Install PyGObject

This is only required if you need to use gtool module of the gunfolds package

To install PyGObject package with brew install run the following command

   brew install pygobject3 gtk+3

To install PyGObject package in Windows, Linux and any other platforms please refer to the link

https://pygobject.readthedocs.io/en/latest/getting_started.html

Acknowledgment

This work was initially supported by NSF IIS-1318759 grant and is currently supported by NIH 1R01MH129047.

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Tools for working with compressed graphs and solving forward and the inverse problems in undersampling and latent variable cases. The package provides implementation of MSL, RASL, ASP-based, CSP-based, and other algorithms

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