Metadata-Version: 2.1
Name: PyPCAlg
Version: 1.0.1
Summary: A Python implementation of the original PC algorithm.
Home-page: https://github.com/Black-Swan-ICL/PyPCAlg
Author: K. M-H
Author-email: kmh.pro@protonmail.com
License: MIT
Description: # PyPCAlg
        
        This repository contains a Python implementation of the original PC algorithm.
        
        # Structure of the package
        
        Folder **examples** contains examples of small dimensional graphs (i.e. with 
        a low number of nodes) to test the PC algorithm on. 
        
        The exhaustive lists of the (conditional) independence relationships 
        satisfied by these examples (assuming both the causal Markov condition and 
        causal Faithfulness) have been worked out. They are contained in files :
        - examples/true_independence_relationships_graph_1.csv,
        - examples/true_independence_relationships_graph_2.csv,
        - examples/true_independence_relationships_graph_3.csv, and
        - examples/true_independence_relationships_graph_4.csv.
        
        In practice, the results of the PC algorithm depend on the statistical 
        tests of (conditional) independence that we use. Considering the high 
        number of statistical (conditional) independence tests carried out by the PC 
        algorithm (even on graphs of moderate sizes), it is inevitable that some of 
        these statistical tests will be erroneous (that is the whole problem of 
        Multiple Hypothesis Testing). 
        
        By providing the lists of (conditional) independence relationships satisfied 
        by the examples, we make it possible to check whether the implementation of 
        the PC algorithm itself is correct (indeed, things are as if we had at our 
        disposal statistical tests of unconditional/conditional dependence that 
        always return a correct result : no type I error, no type II error).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Requires-Python: >=3.9
Description-Content-Type: text/markdown
