Metadata-Version: 2.1
Name: paper-network
Version: 0.0.1
Summary: Gibbs Sampler and other functions for PAPER (Preferential Attachment Plus Erdos--Renyi) model for random networks
Home-page: https://github.com/nineisprime/PAPER
Author: Min Xu
Author-email: mx76@stat.rutgers.edu
License: UNKNOWN
Description: PAPER
        ====
        
        Implementation of Gibbs sampler for computing posterior root
        probabilities under the PAPER (preferential attachment plus
        Erdos--Renyi) model for random networks.
        
        See details in the arXiv paper: https://arxiv.org/abs/2107.00153
        
        [Documentation]
        
        [Documentation]: https://nineisprime.github.io/PAPER/
        
        Installation
        -------------
        
        	$ pip install PAPER
        
        
        Usage
        ------
        
        	>>> from PAPER.gibbsSampling import gibbsToConv
        	>>> from PAPER.tree_tools import createNoisyGraph
        	>>> graf = createNoisyGraph(n=100, m=200, alpha=0, beta=1, K=1)[0]
        	>>> mcmc_out = gibbsSampling.gibbsToConv(graf, DP=False, method="full",
                               K=1, tol=0.1)
        					   
        See example.py for interpreting the inference output. Some sample
        network datasets are provided.
        
        Notes
        ------
        * No preprocessing required on input graph. If the
          input graph is disconnected, the largest connected component is
          used.
        * The algorithm
          performs roughly 1 outer Gibbs iteration in 1 second on a graph with
          10,000 edges. The number of iterations to convergence depends on the
          input graph.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
