Skip to content
General estimating equations with or without bias-reducing adjustments
Julia
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs Added arxiv reference Jan 14, 2020
src inluded fit.jl Jan 14, 2020
test Updates to doc environment + new tests Jan 13, 2020
.travis.yml codecov Jan 10, 2020
LICENSE.md license updates Jan 9, 2020
Manifest.toml Full support for explicit and implicit trace RBM Jan 12, 2020
NEWS.md Updated NEWS Jan 14, 2020
Project.toml Roll back version number to satisfy registrator Jan 14, 2020
README.md Added arxiv reference Jan 14, 2020

README.md

GEEBRA.jl

General Estimating Equations with or without Bias-Reducing Adjustments (pronounced zee· bruh)

Travis status Coverage Status

Package description

GEEBRA is a Julia package that implements M-estimation for statistical models, either by solving estimating equations or by maximizing inference objectives, like likelihoods and composite likelihoods (see, Varin et al, 2011, for a review), using only user-specified templates of the estimating function or the objective functions contributions.

A key feature is the use of only those templates and forward mode automatic differentiation (as implemented in ForwardDiff) to provide methods for reduced-bias M-estimation (RBM-estimation; see, Kosmidis & Lunardon, 2020).

See the documentation for more information, and the examples for a showcase of the functionaly GEEBRA provides.

See NEWS.md for changes, bug fixes and enhancements.

Authors

Ioannis Kosmidis (author, maintainer)
Nicola Lunardon (author)

References

  • Varin, C., N. Reid, and D. Firth (2011). An overview of composite likelihood methods. Statistica Sinica 21(1), 5-42. Link
  • Kosmidis, I., N. Lunardon (2020). Empirical bias-reducing adjustments to estimating functions. ArXiv:2001.03786. Link
You can’t perform that action at this time.