A Python Package for Stochastic Nonparametric Envelopment of Data
Project description
pyStoNED 
pyStoNED is a Python package that provides functions for estimating Convex Nonparametric Least Square (CNLS), Stochastic Nonparametric Envelopment of Data (StoNED), and other various StoNED-related variants such as Convex Quantile Regression (CQR), Convex Expectile Regression (CER), and Isotonic CNLS (ICNLS). It also provides efficiency measurement using Data Envelopement Analysis (DEA) and Free Disposal Hull (FDH). The pyStoNED package allows the user to estimate the CNLS/StoNED frontiers in an open-access environment and 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 pyStoNED. 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/pyStoNED
Documentation
A number of Jupyter Notebooks are provided in the Documentation website, and more detailed technical reports are currently under development.
Authors
- Sheng Dai, Ph.D. candidate, Aalto University School of Business.
- Yu-Hsueh Fang, Computer Engineer, Institute of Manufacturing Information and Systems, National Cheng Kung University.
- Chia-Yen Lee, Professor, College of Management, National Taiwan University.
- Timo Kuosmanen, Professor, Aalto University School of Business.
Citation
If you use pyStoNED for published work, we encourage you to cite our papers. We appreciate it.
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