Skip to main content

A Package for Stochastic Nonparametric Envelopment of Data (StoNED) in Python

Project description

StoNED-Python

StoNED-Python project provides the python codes for estimating Convex Nonparametric Least Square (CNLS), Stochastic Nonparametric Envelopment of Data (StoNED), and other different StoNED-related variants. It allows the user to estimate the CNLS/StoNED models in an open-access environment rather than in commercial software, e.g., GAMS, MATLAB. The StoNED-Python project is built based on the PYOMO.

Installation

We have published a beta version pyStoNED package on PyPI. Please feel free to download and test it. We welcome any bug reports and feedback.

PyPI PyPI version Downloads Downloads

pip install pystoned

GitHub -- Latest development version

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

Tutorials

A number of Jupyter Notebooks are provided in the repository pyStoNED-Tutorials, and more detailed technical reports are currently under development.

Authors

  • Timo Kuosmanen, Professor, Aalto University School of Business.
  • Sheng Dai, Ph.D. candidate, Aalto University School of Business.
  • Chia-Yen Lee, Professor, College of Management, National Taiwan University.
  • Yu-Hsueh Fang, Computer Engineer, Institute of Manufacturing Information and Systems, National Cheng Kung University.

To do list

  • CNLS/StoNED
    • Production function estimation
    • Cost function estimation
    • variables returns to scale (VRS) model
    • constant returns to scale (CRS) model
    • Additive composite error term
    • Multiplicative composite error term
    • Residuals decomposition by method of moments(MoM)
    • Residuals decomposition by quasi-likelihood estimation(QLE)
    • Residuals decomposition by nonparametric kernel deconvolution (NKD)
  • A more efficient algorithm for CNLS (CNLSG)
  • StoNEZD (contextual variables)
  • Convex quantile regression (CQR)
  • Convex expectile regression (CER)
  • Isotonic CNLS (ICNLS)
  • Isotonic convex quantile regression (ICQR)
  • Isotonic convex expectile regression (ICER)
  • Corrected convex nonparametric least squares (C2NLS)
  • Multiple outputs (CNLS-DDF formulation)
    • with undesirable outputs
    • without undesirable outputs
  • Multiple outputs (CQR/CER-DDF formulation)
    • with undesirable outputs
    • without undesirable outputs
  • Data Envelopment Analysis (DEA)
    • Radial input oriented model: CRS and VRS
    • Radial output oriented model: CRS and VRS
    • Directional model: CRS and VRS
    • Directional model with undesirable outputs: CRS and VRS
  • Free Disposal Hull (FDH) Analysis
    • Radial input oriented FDH model
    • Radial output oriented FDH model
  • Representation of StoNED-related frontier/quantile function
    • one input and one output
    • two inputs and one output
    • three inputs and one output

Change log

[0.3.8] - 2020-08-09

Added

  • CNLSG()
  • CNLSG1()
  • CNLSG2()
  • sweet()

[0.3.7] - 2020-07-25

Added

  • ICQER()

[0.3.6] - 2020-06-20

Changed

  • StoNED()

[0.3.5] - 2020-06-18

Added

  • FDH()
  • FDH2ICNLS()

Changed

  • ICNLS()

[0.3.4] - 2020-06-17

Added

  • DEA2CNLS()

Removed

  • CCNLS2()

[0.3.3] - 2020-06-16

Changed

  • CNLSDDF()
  • CQRDDF()
  • CERDDF()

[0.3.2] - 2020-06-16

Changed

  • CNLSDDF()
  • CQRDDF()
  • CERDDF()

[0.3.1] - 2020-06-16

Changed

  • kde()
  • CNLSDDF()

[0.3.0] - 2020-06-12

Changed

  • DEA()
  • CQER()
  • CQEDDF()

[0.2.9] - 2020-06-10

Added

  • DEA()

[0.2.8] - 2020-06-04

Added

  • CQRDDF()
  • CERDDF()

Changed

  • directV()

[0.2.7] - 2020-05-24

Added

  • CNLSPLOT()

Changed

  • adjust the argument pps to rts
  • adjust the argument func to fun
  • adjust the argument crt to cet
  • CNLSDDF()

Removed

  • CNLSDDFB()

[0.2.6] - 2020-05-05

Changed

  • qle()
  • stoned()
  • LICENSE

[0.2.5] - 2020-05-01

Added

  • ked()

Changed

  • CNLSDDFb()
  • directV()
  • stoned()
  • HISTORY.md

Removed

  • directVb()

[0.2.4] - 2020-04-30

Changed

  • qlle()
  • stoned()

[0.2.3] - 2020-04-27

Added

  • cnlsddfb()
  • directVb()

Changed

  • cnlsddf()
  • directV()

[0.2.2] - 2020-04-26

Added

  • cnlsddf()
  • directV()

Changed

  • cnls()
  • ceqr()
  • cnlsz()
  • icnls()

[0.2.1] - 2020-04-23

Added

  • icnls()
  • bimatp()

Changed

  • REDAME.md
  • All functions

[0.2.0] - 2020-04-19

Added

  • ccnls()
  • ccnls2()
  • cnlsz()

Changed

  • REDAME.md
  • cqer()

[0.0.7] - 2020-04-18

Changed

  • cnls()

[0.0.6] - 2020-04-17

Added

  • README.md
  • LICENSE.txt
  • HISTORY.md

[0.0.2] - 2020-04-17

Added

  • cqer()
  • qllf()

[0.0.1] - 2020-04-01

Added

  • stoned()
  • cnls()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pystoned-0.3.8.tar.gz (22.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pystoned-0.3.8-py3-none-any.whl (47.3 kB view details)

Uploaded Python 3

File details

Details for the file pystoned-0.3.8.tar.gz.

File metadata

  • Download URL: pystoned-0.3.8.tar.gz
  • Upload date:
  • Size: 22.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0.post20200714 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.7

File hashes

Hashes for pystoned-0.3.8.tar.gz
Algorithm Hash digest
SHA256 bb3df8c5dc231d4907b56443065e7711c1d4c12da3108637fece0d7dcc6c032c
MD5 94f9f6caf6c2cf7c16108a335fec09be
BLAKE2b-256 2291b7d32d7b3f917aea1e042d428c3e192bd8f7d8eb7bf6d7d623d8ea1c4a78

See more details on using hashes here.

File details

Details for the file pystoned-0.3.8-py3-none-any.whl.

File metadata

  • Download URL: pystoned-0.3.8-py3-none-any.whl
  • Upload date:
  • Size: 47.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0.post20200714 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.7

File hashes

Hashes for pystoned-0.3.8-py3-none-any.whl
Algorithm Hash digest
SHA256 9521ed606d063c8b4789728a095fb0d9d7b5007f8ee703483565cf41e6ad1f44
MD5 de4b5def95d9d32163b8db76c1878fb7
BLAKE2b-256 5949504352367dbe9bcdfc85c58a33350ad44d1bf7cafc68a6b8cc848951571f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page