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

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
ppstorts - adjust the argument
functofun - adjust the argument
crttocet 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()
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