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). 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); also available here).
M-estimation encompasses a broad swath of statistical estimators and ideas including:
geex can implement all of these using a user-defined estimating function.
If you can specify a set of unbiased estimating equations,
geexdoes the rest.
The goals of geex are simply:
geex does not necessarily aim to be fast nor precise. Such goals are left to the user to implement or confirm.
To install the current version:
devtools::install_github("bsaul/geex")
Start with the examples in the package introduction (also accessible in R by vignette('00_geex_intro')).
Please review the contributing guidelines. If you have bug reports, feature requests, or other ideas for geex, please file an issue or contact @bsaul.
If you use geex in a project, please cite the Journal of Statistical Software paper.
BibTex entry:
@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},
}
Need help using geex or writing your estimating function? Feel free to contact @bsaul. You can find examples of help in the geex-help repository.
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