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:

• 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:

devtools::install_github("bsaul/geex")

# Usage

Start with the examples in the package introduction (also accessible in R by vignette('00_geex_intro')).

# Contributing to geex

Please review the contributing guidelines. If you have bug reports, feature requests, or other ideas for geex, please file an issue or contact @bsaul.