LiNGAM Python Package
Project description
LiNGAM - Discovery of non-gaussian linear causal models
LiNGAM is a new method for estimating structural equation models or linear Bayesian networks. It is based on using the non-Gaussianity of the data.
Requirements
- Python3
- numpy
- scipy
- scikit-learn
Installation
To install lingam package, use pip as follows:
$ pip install lingam
Documentation
License
This project is licensed under the terms of the MIT license.
Reference Papers
- S. Shimizu, P. O. Hoyer, A. Hyvﾃ､rinen and A. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7: 2003--2030, 2006. [PDF]
- S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvﾃ､rinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(Apr): 1225--1248, 2011. [PDF]
- A. Hyvﾃ､rinen and S. M. Smith. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research, 14(Jan): 111--152, 2013. [PDF]
- S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012. [PDF]
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