Metadata-Version: 2.1
Name: pystoned
Version: 0.4.8
Summary: A Python Package for Stochastic Nonparametric Envelopment of Data
Home-page: https://github.com/ds2010/pyStoNED
Author: Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen
Author-email: sheng.dai@aalto.fi
License: GPLv3
Download-URL: https://pypi.org/project/pystoned/
Keywords: StoNED,CNLS,CER,CQR,Z-variables,CNLSG
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: pyomo (>=5.7.3)
Requires-Dist: pandas (>=1.1.3)
Requires-Dist: numpy (>=1.19.2)
Requires-Dist: scipy (>=1.5.2)
Requires-Dist: matplotlib

# pyStoNED [![Documentation Status](https://readthedocs.org/projects/pystoned/badge/?version=latest)](https://pystoned.readthedocs.io/en/latest/?badge=latest)

`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](http://www.pyomo.org/). 

# Installation

The [`pyStoNED`](https://pypi.org/project/pystoned/) package is now avaiable on PyPI and the latest development version can be installed from the Github repository [`pyStoNED`](https://github.com/ds2010/pyStoNED). Please feel free to download and test it. We welcome any bug reports and feedback.

#### PyPI [![PyPI version](https://img.shields.io/pypi/v/pystoned.svg?maxAge=3600)](https://pypi.org/project/pystoned/) [![Downloads](https://pepy.tech/badge/pystoned)](https://pepy.tech/project/pystoned)[![PyPI downloads](https://img.shields.io/pypi/dm/pystoned.svg?maxAge=21600)](https://pypistats.org/packages/pystoned)

    pip install pystoned

#### GitHub

    pip install -U git+https://github.com/ds2010/pyStoNED

# Documentation

A number of Jupyter Notebooks are provided in the [Documentation](https://pystoned.readthedocs.io/en/latest/) website, and more detailed technical reports are currently under development. 

# Authors

 + [Sheng Dai](https://www.researchgate.net/profile/Sheng_Dai8), Ph.D. candidate, Aalto University School of Business.
 + [Yu-Hsueh Fang](https://github.com/JulianATA), Computer Engineer, Institute of Manufacturing Information and Systems, National Cheng Kung University.
 + [Chia-Yen Lee](http://polab.im.ntu.edu.tw/), Professor, College of Management, National Taiwan University.
 + [Timo Kuosmanen](https://www.researchgate.net/profile/Timo_Kuosmanen), Professor, Aalto University School of Business.

# Citation

If you use [pyStoNED](https://pypi.org/project/pystoned/) for published work, we encourage you to cite our [papers](https://pystoned.readthedocs.io/en/latest/citing/index.html). We appreciate it.

