Metadata-Version: 2.1
Name: alexandria-python
Version: 0.0.2
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
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

# 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.

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**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

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**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)))

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**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


