Metadata-Version: 2.1
Name: alexandria-python
Version: 0.0.1
Summary: a software for Bayesian time-series econometrics applications
Home-page: UNKNOWN
Author: Romain Legrand
Author-email: alexandria.toolbox@gmail.com
License: Other/Proprietary License
Description: # Alexandria
        
        **Alexandria** is a Python package for Bayesian time-series econometrics applications. This is the first official release of the software. For its first release, Alexandria includes only the most basic model: the linear regression. However, it proposes a wide range of Bayesian linear regressions:
        
        - maximum likelihood / OLS regression (non-Bayesian)
        - simple Bayesian regression
        - hierarchical (natural conjugate) Bayesian regression
        - independent Bayesian regression with Gibbs sampling
        - heteroscedastic Bayesian regression
        - autocorrelated Bayesian regression
        
        Alexandria is user-friendly and can be used from a simple Graphical User Inteface (GUI). More experienced users can also run the models directly from the Python console by using the model classes and methods.
        
        ===============================
        
        Installing Alexandria
        ~~~~~~~~~~~~~~~~~~~~~
        
        Alexandria can be installed from pip: ``pip install alexandria-python``
        A local installation can also obtain by copy-pasting the folder containing the toolbox prgrammes. The folder can be downloaded from the project website or Github repo: 
        https://alexandria-toolbox.github.io
        https://github.com/alexandria-toolbox
        
        ===============================
        
        Getting started
        ~~~~~~~~~~~~~~~
        
        Simple Python example
        
            # imports
            from alexandria.linear_regression import IndependentBayesianRegression
            from alexandria.datasets import data_sets as ds
            import numpy as np
            
            # load Taylor dataset, split as train/test
            taylor_data = ds.load_taylor()
            y_train, X_train = taylor_data[:198,0], taylor_data[:198,1:]
            y_test, X_test = taylor_data[198:,0], taylor_data[198:,1:]
            
            # set prior mean and prior variance for the model
            b = np.array([1.5, 0.5])
            b_const = 1
            V = np.array([0.01, 0.0025])
            V_const = 0.01
            
            # create and train regression
            br = IndependentBayesianRegression(endogenous=y_train, exogenous=X_train,
            constant=True, b_exogenous=b, V_exogenous=V, b_constant=b_const, V_constant=V_const)
            br.estimate()
            
            # get predictions on test sample, run forecast evaluation, display log score
            estimates_forecasts = br.forecast(X_test, 0.95)
            br.forecast_evaluation(y_test)
            print('log score on test sample : ' + str(round(br.forecast_evaluation_criteria['log_score'], 2)))
        
        ===============================
        
        Documentation
        ~~~~~~~~~~~~~~~
        
        Complete manuals and user guides can be found on the project website and Github repo:
        https://alexandria-toolbox.github.io/
        https://github.com/alexandria-toolbox
        
        ===============================
        
        Contact
        ~~~~~~~
        
        alexandria.toolbox@gmail.com
        
Keywords: python,Bayesian,time-series,econometrics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
