Metadata-Version: 2.1
Name: delicatessen
Version: 2.2
Summary: Generalized M-Estimation
Home-page: https://github.com/pzivich/Deli
Author: Paul Zivich
Author-email: zivich.5@gmail.com
License: MIT
Description: ![delicatessen](docs/images/delicatessen_header.png)
        
        # Delicatessen
        
        ![tests](https://github.com/pzivich/Delicatessen/actions/workflows/python-package.yml/badge.svg)
        [![version](https://badge.fury.io/py/delicatessen.svg)](https://badge.fury.io/py/delicatessen)
        [![arXiv](https://img.shields.io/badge/arXiv-2203.11300-b31b1b.svg)](https://arxiv.org/abs/2203.11300)
        [![docs](https://readthedocs.org/projects/deli/badge/?version=latest)](https://deli.readthedocs.io/en/latest/?badge=latest)
        [![Downloads](https://pepy.tech/badge/delicatessen/month)](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 more detailed introductions to M-estimation, see Ross
        et al. (2024), Stefanski & Boos (2002), or 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
        variance estimation via the empirical sandwich variance estimator.
        
        While M-Estimation is a powerful tool, the derivatives and matrix algebra can quickly become unwieldy. This is where 
        `delicatessen` comes in. `delicatessen` takes estimating functions and data, and solves for the parameter estimates,
        computes the derivatives, and performs the matrix algebra calculations. Therefore, M-estimators can be more easily
        adopted without having to perform by-hand calculations. In other words, we can let the computer do the math for us.
        
        To further ease use, `delicatessen` also comes with a variety of built-in estimating equations. See
        the [delicatessen website](https://deli.readthedocs.io/en/latest/) for details on the available estimating equations,
        how to use them, and practical examples.
        
        
        ## 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 corresponding estimating equation for the mean, 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 estimate
        ```
        
        For further details on using `delicatessen`, see the full documentation and worked examples available
        at [delicatessen website](https://deli.readthedocs.io/en/latest/).
        
        ## 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.
        
        Ross RK, Zivich PN, Stringer JS, & Cole SR. (2024). M-estimation for common epidemiological measures: introduction and
        applied examples. *International Journal of Epidemiology*, 53(2).
        
        Zivich PN, Klose M, Cole SR, Edwards JK, & Shook-Sa BE. (2022). Delicatessen: M-Estimation in Python.
        *arXiv preprint arXiv:2203.11300*.
        
Keywords: m-estimation sandwich-variance estimating-equations
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
