A Package for Stochastic Nonparametric Envelopment of Data (StoNED) in Python
Project description
StoNED-Python
StoNED-Python project provides the python codes for estimating Convex Nonparametric Least Square (CNLS), Stochastic Nonparametric Envelopment of Data (StoNED), and other different StoNED-related variants. It allows the user to estimate the CNLS/StoNED models in an open-access environment rather than in commercial software, e.g., GAMS, MATLAB. The StoNED-Python project is built based on the PYOMO.
Installation
The pyStoNED package is now avaiable on PyPI and the latest development version can be installed from the Github repository StoNED-Python. Please feel free to download and test it. We welcome any bug reports and feedback.
PyPI 
pip install pystoned
GitHub
pip install -U git+https://github.com/ds2010/StoNED-Python
Tutorials
A number of Jupyter Notebooks are provided in the repository pyStoNED, and more detailed technical reports are currently under development.
Authors
- Timo Kuosmanen, Professor, Aalto University School of Business.
- Sheng Dai, Ph.D. candidate, Aalto University School of Business.
- Chia-Yen Lee, Professor, College of Management, National Taiwan University.
- Yu-Hsueh Fang, Computer Engineer, Institute of Manufacturing Information and Systems, National Cheng Kung University.
Citation
If you use pyStoNED for published work, we encourage you to cite our papers and upcoming techinical report. We appreciate it.
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