Metadata-Version: 2.1
Name: bayessynth
Version: 0.1
Summary: Python implementation of Bayesian Synthetic Controls
Home-page: https://github.com/eliastuo/bayessynth
Author: Elias Tuomaala
Author-email: mail@eliastuomaala.com
License: MIT
Description: # *bayessynth:* BSC Models in Python
        
        The bayessynth package is a Python implementation of the Bayesian Synthetic Control (BSC). BSC is a probabilistic method for quantitative social science, developed in [Tuomaala (2019)](https://arxiv.org/abs/1910.06106 "Arxiv preprint")[1]. It includes tools to estimate the BSC model with Markov Chain Monte Carlo (MCMC) sampling and to analyze and visualize the results.
        
        ## Documentation
        
        Limited documentation for the library is available separately within this git repository.
        
        ## Dependencies
        
        Fitting of the BSC model is done using `pymc3`, which itself uses depends on `theano` and `scipy`. Other fundamental dependencies include `numpy`, `pandas`, and `sklearn`, as well as the visualization libraries `matplotlib` and `seaborn`.
        
        ## Author
        
        Elias Tuomaala
        
        Website: [www.eliastuomaala.com](www.eliastuomaala.com "Author Homepage")
        
        Email: [mail@eliastuomaala.com](mailto:mail@eliastuomaala.com)
        
        ## License
        
        The bayessynth copyright belongs to Elias Tuomaala (2020). It is released under the MIT License.
        
        ## Example
        ```
        import numpy as np
        import pandas as pd
        import bayessynth as bs
        
        data_source, target_country, cutoff_year = 'gdp.csv', 'DEU', 1990
        factors = 4
        prior_distribution = {
                              'sigma_gamma': 500,
                              'k_mu': 16000,
                              'k_sd': 7000,
                              'k_gamma': 7000,
                              'alpha_sd': 30000,
                              'alpha_mu': 0,
                              'b_mu': 0,
                              'b_sd': 1,
                              'b_gamma': 1,
                              'delta_mu': 0,
                              'delta_sd': 10000
        }
        data = pd.read_csv(data_source)
        
        bs.fit(data, target_country, cutoff_year, prior_distribution)
        trace = bs.read_tracefile(target, data, factors)
        result_summary = bs.summarize_ppc(target_country, data, trace, factors)
        bs.plot(result_summary, cutoff_year, target_country, output='display')
        ```
        
        [1]: Elias Tuomaala. (2019) "The Bayesian Synthetic Control: Improved Counterfactual Estimation in the Social Sciences through Probabilistic Modeling." [Arxiv Open Access](https://arxiv.org/abs/1910.06106 "The Bayesian Synthetic Control").
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7.4
Description-Content-Type: text/markdown
