Metadata-Version: 2.1
Name: cgm
Version: 0.0.1
Summary: Causal Graphical Models
Home-page: https://github.com/kyleellefsen/cgm
Author: Kyle Ellefsen
Author-email: kyleellefsen@gmail.com
License: UNKNOWN
Project-URL: Source, https://github.com/kyleellefsen/cgm
Description: # Causal Graphical Models
        
        A python library for building causal graphical models, closely following Daphne 
        Koller's Coursera course on Probabilistic Graphical Models, and her 2009 book 
        _Probabilistic Graphical Models: Principles and Techniques_. 
        The source for this project is available [here][src].
        
        ## Installation
        [NumPy][numpy] is the only dependency. Python version must be >= 3.7. 
        
            pip install cgm
        
        ## Usage
        
        ```python
        import numpy as np
        import cgm
        
        np.random.seed(30)
        # Define all nodes
        A = DAG_Node('A', 3)
        B = DAG_Node('B', 3)
        C = DAG_Node('C', 3)
        D = DAG_Node('D', 3)
        # Specify all parents of nodes
        CPD(B, [A])
        CPD(C, [B])
        CPD(D, [A, B])
        nodes = [A, B, C, D]
        # Create graph
        graph = DAG(nodes)
        ```
        
        [src]: https://github.com/kyleellefsen/cgm
        [numpy]: https://numpy.org/
Keywords: cgm,pgm,dag,causal inference,factors
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
