Metadata-Version: 1.1
Name: netrics
Version: 0.0.4
Summary: Econometric methods for the analysis of networks.
Home-page: http://github.com/bryangraham/netrics
Author: Bryan S. Graham
Author-email: bgraham@econ.berkeley.edu
License: MIT
Description: netrics: a Python 2.7 package for econometric analysis of networks
        -----------------------------------------------------------------------------
        by Bryan S. Graham, UC - Berkeley, e-mail: bgraham@econ.berkeley.edu
        
        
        This package includes a Python 2.7 implementation of the two econometric
        network formation models introduced in Graham (2014, NBER).
        
        This package is offered "as is", without warranty, implicit or otherwise. While I would
        appreciate bug reports, suggestions for improvements and so on, I am unable to provide any
        meaningful user-support. Please e-mail me at bgraham@econ.berkeley.edu
        
        Please cite both the code and the underlying source articles listed below when using this 
        code in your research.
        
        A simple example script to get started is::
        
        	>>>> # Import numpy in order to correctly read test data
        	>>>> import numpy as np
        
        	>>>> # Import urllib in order to download test data from Github repo
        	>>>> import urllib
        
        	>>>> # Append location of netrics module base directory to system path
        	>>>> # NOTE: only required if permanent install not made 
        	>>>> # NOTE: edit path to location on netrics package on local machine
        	>>>> import sys
        	>>>> sys.path.append('/Users/bgraham/Dropbox/Sites/software/netrics/')
        
        	>>>> # Load netrics module
        	>>>> import netrics as netrics
        	
        	>>>> # Download Nyakatoke test dataset from GitHub
        	>>>> download =  '/Users/bgraham/Dropbox/' # Edit to location on your machine   
        	>>>> url = 'https://github.com/bryangraham/netrics/blob/master/Notebooks/Nyakatoke_Example.npz?raw=true'
        	>>>> urllib.urlretrieve(url, download + "Nyakatoke_Example.npz")
        
        	>>>> # Open dataset
        	>>>> NyakatokeTestDataset = np.load(download + "Nyakatoke_Example.npz")
        
        	>>>> # Extract adjacency matrix
        	>>>> D = NyakatokeTestDataset['D']
        
        	>>>> # Initialize list of dyad-specific covariates as elements
        	>>>> # W = [W0, W1, W2,...WK-1]
        	>>>> W = []
        
        	>>>> # Initialize list with covariate labels
        	>>>> cov_names = []
        
        	>>>> # Construct list of regressor matrices and corresponding variable names
        	>>>> for matrix in NyakatokeTestDataset.files:
        	>>>>     if matrix != 'D':
        	>>>>         W.append(NyakatokeTestDataset[matrix])
        	>>>>         cov_names.append(matrix)   
        
        	>>>> # Apply tetrad logit procedure to dataset	
        	>>>> [beta_TL, vcov_beta_TL, tetrad_frac_TL, success] = \
            	 	 netrics.tetrad_logit(D, W, dtcon=None, silent=False, W_names=cov_names)
        
        
        CODE CITATION
        ---------------
        Graham, Bryan S. (2016). "netrics: a Python 2.7  package for econometric analysis of 
        	networks," (Version 0.0.1) [Computer program]. Available at 
        	https://github.com/bryangraham/netrics (Accessed 04 September 2016) 
        	
        PAPER CITATIONS
        ---------------
        Graham, Bryan S. (2014). "An econometric model of link formation with degree 
        	heterogeneity," NBER Working Paper No. w20341.	
Keywords: Tetrad Logit,Networks,Degree heterogeneity
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
