https://github.com/bsaul/geex
Framework for estimating parameters and the empirical sandwich covariance matrix from a set of unbiased estimating equations (i.e. M-estimation) in R.
https://github.com/bsaul/geex
asymptotics covariance-estimates covariance-estimation estimate-parameters estimating-equations estimation inference m-estimation r robust sandwich
Last synced: 8 months ago
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Framework for estimating parameters and the empirical sandwich covariance matrix from a set of unbiased estimating equations (i.e. M-estimation) in R.
- Host: GitHub
- URL: https://github.com/bsaul/geex
- Owner: bsaul
- License: other
- Created: 2016-09-02T19:32:25.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2024-05-06T18:09:44.000Z (about 2 years ago)
- Last Synced: 2025-09-22T15:49:58.255Z (9 months ago)
- Topics: asymptotics, covariance-estimates, covariance-estimation, estimate-parameters, estimating-equations, estimation, inference, m-estimation, r, robust, sandwich
- Language: R
- Homepage: https://bsaul.github.io/geex/
- Size: 4.59 MB
- Stars: 9
- Watchers: 2
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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# Overview
`geex` provides an extensible API for estimating parameters and their covariance from a set of estimating functions (M-estimation). M-estimation theory has a long history (see the [M-estimation bibliography](https://bsaul.github.io/geex/articles/articles/mestimation_bib.html)). For an excellent introduction, see the primer by L.A. Stefanski and D.D. Boos, "The Calculus of M-estimation" ([The American Statistician (2002), 56(1), 29-38)](http://www.jstor.org/stable/3087324?seq=1#page_scan_tab_contents); [also available here](http://www4.stat.ncsu.edu/~boos/papers/mest6.pdf)).
M-estimation encompasses a broad swath of statistical estimators and ideas including:
* the empirical "sandwich" variance estimator
* generalized estimating equations (GEE)
* many maximum likelihood estimators
* robust regression
* and many more
`geex` can implement all of these using a user-defined estimating function.
## Goals
> If you can specify a set of unbiased estimating equations, `geex` does the rest.
The goals of `geex` are simply:
* To minimize the translational distance between a set of estimating functions and R code;
* To return numerically *accurate* point and covariance estimates from a set of unbiased estimating functions.
`geex` does not necessarily aim to be fast nor precise. Such goals are left to the user to implement or confirm.
# Installation
To install the current version:
```r
devtools::install_github("bsaul/geex")
```
# Usage
Start with the examples in the [package introduction](https://bsaul.github.io/geex/articles/v00_geex_intro.html) (also accessible in R by `vignette('00_geex_intro')`).
# Contributing to geex
Please review the [contributing guidelines](https://github.com/bsaul/geex/blob/master/CONTRIBUTING.md). If you have bug reports, feature requests, or other ideas for `geex`, please file an issue or contact [@bsaul](https://github.com/bsaul).
# Citation
If you use `geex` in a project,
please cite the
[Journal of Statistical Software paper](https://www.jstatsoft.org/article/view/v092i02).
BibTex entry:
```bib
@Article{,
title = {The Calculus of M-Estimation in {R} with {geex}},
author = {Bradley C. Saul and Michael G. Hudgens},
journal = {Journal of Statistical Software},
year = {2020},
volume = {92},
number = {2},
pages = {1--15},
doi = {10.18637/jss.v092.i02},
}
```
# Get Help
Need help using `geex` or writing your estimating function?
Feel free to contact [@bsaul](https://github.com/bsaul).
You can find examples of help [in the `geex-help` repository](https://gitlab.com/bsaul/geex-help).