A Python Package for Convex Regression and Frontier Estimation
Project description
pyStoNED 
pyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. It also facilitates efficiency measurement using the conventional Data Envelopement Analysis (DEA) and Free Disposable Hull (FDH) approaches. The pyStoNED package allows practitioners to estimate these models in an open access environment under a GPL-3.0 License.
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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