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 reference in 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).
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
and many more
geex can implement all of these using a user-defined estimating function.
To learn more about geex, see the package vignettes:
browseVignettes(package = 'geex').
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, by itself, necessarily aim to be fast nor precise. Such goals are left to the user to implement or confirm.
Maintainer: Bradley Saul [email protected]
Brian Barkley [contributor]
Report bugs at https://github.com/bsaul/geex/issues
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