Metadata-Version: 2.1
Name: alexandria-python
Version: 0.0.4
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 programmes. 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
