Metadata-Version: 2.1
Name: pystoned2
Version: 0.0.2
Summary: A Python Package for Convex Regression and Frontier Estimation
Home-page: https://github.com/advancehs/pyStoNED2
Author: Shuo Hu
Author-email: 1019753743@qq.com
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

# [pyStoNED2]

pyStoNED2 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 pyStoNED2 package allows practitioners to estimate these models in an open access environment under a GPL-3.0 License.

# Installation

The [`pyStoNED2`](https://pypi.org/project/pystoned2/) package is now avaiable on PyPI and the latest development version can be installed from the Github repository [`pyStoNED2`](https://github.com/advancehs/pyStoNED2). 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/advancehs/pyStoNED2

#### Contribute

- 在tests添加相应的单元测试
- 使用python -m pytest来运行所有单元测试，确保所有单测都是通过的
