Metadata-Version: 2.1
Name: pystoned
Version: 0.5.4
Summary: A Python Package for Convex Regression and Frontier Estimation
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 :: 5 - Production/Stable
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
License-File: LICENSE

# [pyStoNED](https://pystoned.readthedocs.io/en/latest/) [![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 multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. It also facilitates eï¬ƒciency 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`](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

# 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 following paper and other related [works](https://pystoned.readthedocs.io/en/latest/citing/index.html). We appreciate it.

    Dai S, Fang YH, Lee CY, Kuosmanen T. (2021). pyStoNED: A Python Package for Convex Regression and Frontier Estimation. arXiv preprint arXiv:2109.12962.



