Metadata-Version: 2.2
Name: pystoned
Version: 0.7.5
Summary: A Python Package for Convex Regression and Frontier Estimation
Home-page: https://github.com/ds2010/pyStoNED
Download-URL: https://pypi.org/project/pystoned/
Author: Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen
Author-email: sheng.dai@zuel.edu.cn
License: GPLv3
Keywords: StoNED,CNLS,CER,CQR,Z-variables,CNLSG
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
Requires-Dist: pyomo>=6.8.0
Requires-Dist: pandas>=1.1.3
Requires-Dist: numpy>=1.19.2
Requires-Dist: scipy>=1.5.2
Requires-Dist: matplotlib>=3.5.1
Requires-Dist: mosek>=10.1.13
Requires-Dist: openpyxl>=3.1.2
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: download-url
Dynamic: home-page
Dynamic: keywords
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# [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 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`](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/)[![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://daisheng.org), Associate Professor, School of Economics, Zhongnan University of Economics and Law.
 + [Yu-Hsueh Fang](https://github.com/Fangop), 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, Turku School of Economics, University of Turku.

# 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. (2024). pyStoNED: A Python Package for Convex Regression and Frontier Estimation. **Journal of Statistical Software**. 111, 1-43.
