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delicatessen-1.1
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Generalized M-Estimation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | delicatessen-1.1 |
| نام | delicatessen |
| نسخه کتابخانه | 1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Paul Zivich |
| ایمیل نویسنده | zivich.5@gmail.com |
| آدرس صفحه اصلی | https://github.com/pzivich/Deli |
| آدرس اینترنتی | https://pypi.org/project/delicatessen/ |
| مجوز | MIT |
# Delicatessen
[](https://badge.fury.io/py/delicatessen)
[](https://deli.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/delicatessen)
The one-stop sandwich (variance) shop in Python. `delicatessen` is a Python 3.8+ library for the generalized calculus
of M-estimation.
**Citation**: Zivich PN, Klose M, Cole SR, Edwards JK, & Shook-Sa BE. (2022). Delicatessen: M-Estimation in Python.
*arXiv:2203.11300* [stat.ME]
## M-Estimation and Estimating Equations
Here, we provide a brief overview of M-estimation theory. For a detailed introduction to M-estimation, see Chapter 7 of
Boos & Stefanski (2013). M-estimation is a generalization of likelihood-based methods. *M-estimators* are solutions to
estimating equations. To apply the M-estimator, we solve the estimating equations using observed data. This is similar
to other approaches, but the key advantage of M-Estimators is estimation of the variance via the sandwich variance.
While M-Estimation is a powerful tool, the derivatives and matrix algebra can quickly become unwieldy. This is where
`delicatessen` comes in. `delicatessen` takes an array estimating equations and data, and solves for the parameter
estimates, numerically approximates the derivatives, and does the matrix calculations. Therefore, M-estimators can
be more widely adopted without by-hand calculations. We can let the computer do all the math for us.
`delicatessen` also comes with a variety of built-in estimating equations. See
the [delicatessen website](https://deli.readthedocs.io/en/latest/) for the full set of available estimating equations
and how to use them.
## Installation
### Installing:
You can install via `python -m pip install delicatessen`
### Dependencies:
The dependencies are: `numpy`, `scipy`
To replicate the tests located in `tests/`, you will additionally need to install: `panda`, `statsmodels`, and `pytest`
While versions of `delicatessen` prior to v1.0 were compatible with older versions of Python 3 and NumPy and SciPy, the
v1.0+ releases are only available for Python 3.8+ with NumPy v1.18.5+ and SciPy v1.9.0. This change was made to use
a better numerical approximation procedure for the derivative. If you want to use with older versions of those packages
or older versions of Python, install v0.6 instead.
## Getting started
Below is a simple demonstration of calculating the mean with `delicatessen`
```python
import numpy as np
y = np.array([1, 2, 3, 1, 4, 1, 3, -2, 0, 2])
```
Loading the M-estimator functionality, building the estimating equation, and printing the results to the console
```python
from delicatessen import MEstimator
def psi(theta):
return y - theta[0]
estr = MEstimator(psi, init=[0, ])
estr.estimate()
print(estr.theta) # Estimate of the mean
print(estr.variance) # Variance estimator for the mean
```
For further details on using `delicatessen`, see the full documentation and worked examples available
at [delicatessen website](https://deli.readthedocs.io/en/latest/) or in the examples folder.
## References
Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference
(pp. 297-337). Springer, New York, NY.
Stefanski LA, & Boos DD. (2002). The calculus of M-estimation. *The American Statistician*, 56(1), 29-38.
Zivich PN, Klose M, Cole SR, Edwards JK, & Shook-Sa BE. (2022). Delicatessen: M-Estimation in Python.
*arXiv preprint arXiv:2203.11300*.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.18.5 | numpy |
| >=1.9.0 | scipy |
نحوه نصب
نصب پکیج whl delicatessen-1.1:
pip install delicatessen-1.1.whl
نصب پکیج tar.gz delicatessen-1.1:
pip install delicatessen-1.1.tar.gz