Python Model Builder - fit statistical models using algorithmic differentiation
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README.md

PyMB

Python Model Builder - fit statistical models using algorithmic differentiation

Concept

PyMB uses algorithmic differentiation from CppAD to compute derivatives of objective functions, allowing users to quickly find optimal solutions to large, multidimensional statistical models.

Implementation

Current

PyMB is currently just a light wrapper around Template Model Builder, allowing users to run TMB models directly from Python by transparently passing numpy and other numeric data into an embedded rpy2 instance. TMB models are specified using C++ templates, requiring the user to be at least somewhat familiar with C++.

Future

We intend to make a fully Pythonic modeling library that closely mirrors PyMC2 in syntax and ease of model specification, but uses either CppAD mixed or TensorFlow under the hood.

Example

A demo iPython notebook can be found at Examples/Linear Regression.ipynb or an online version can be viewed here.

Installation

Dependencies

Importing

setup.py has not yet been added. For now simply git clone git@github.com:kforeman/PyMB.git into your working directory, startup Python or an iPython notebook, and import PyMB.

License (GPLv2)

PyMB - Python Model Builder
Copyright (C) 2015 Kyle Foreman
Email: kforeman <at> post <dot> harvard <dot> edu

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.