Metadata-Version: 2.1
Name: lingam
Version: 1.1.0
Summary: LiNGAM Python Package
Home-page: https://github.com/cdt15/lingam
Author: T.Ikeuchi, G.Haraoka, S.Shimizu
License: UNKNOWN
Description: # LiNGAM - Discovery of non-gaussian linear causal models
        
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/cdt15/lingam/blob/master/LICENSE)
        [![Read the Docs](https://readthedocs.org/projects/lingam/badge/?version=latest)](https://lingam.readthedocs.io/)
        
        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.
        
        * [The LiNGAM Project](https://sites.google.com/site/sshimizu06/lingam)
        
        ## Requirements
        * Python3
        * numpy
        * scipy
        * scikit-learn
        
        ## Installation
        To install lingam package, use `pip` as follows:
        
        ```
        $ pip install lingam
        ```
        
        ## Documentation
        [Tutrial and API reference](https://lingam.readthedocs.io/)
        
        ## License
        This project is licensed under the terms of the [MIT license](./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]](https://www.cs.helsinki.fi/group/neuroinf/lingam/JMLR06.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]](http://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.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]](https://www.cs.helsinki.fi/u/ahyvarin/papers/JMLR13.pdf)
        * S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012. [[PDF]](http://dx.doi.org/10.1016/j.neucom.2011.11.005)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
