geex-package: geex: M-estimation API

In bsaul/geex: An API for M-Estimation

geex: M-estimation API

Description

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).

Details

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.

To learn more about geex, see the package vignettes: browseVignettes(package = 'geex').

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, by itself, necessarily aim to be fast nor precise. Such goals are left to the user to implement or confirm.

Author(s)

Maintainer: Bradley Saul [e-mail]

Other contributors:

• Brian Barkley [contributor]

References

Saul, Bradley C., and Michael G. Hudgens. (2020). "The Calculus of M-estimation in R with geex." Journal of Statistical Software 92(2), 1-15. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v092.i02")}.

See Also

Useful links:

• https://github.com/bsaul/geex

• https://bsaul.github.io/geex/

• Report bugs at https://github.com/bsaul/geex/issues

bsaul/geex documentation built on May 8, 2024, 5:36 p.m.