Zelig is an easy-to-use, free, open source, general purpose statistics program for estimating, interpreting, and presenting results from any statistical method. Zelig turns the power of R, with thousands of open source packages — but with free ranging syntax, diverse examples, and documentation written for different audiences — into the same three functions and consistent documentation for every method. Zelig uses R code from many researchers, making it “everyone’s statistical software”. We hope it becomes everyone’s statistical software for applications too, as we designed it so anyone can use it or add their methods to it. We aim for Zelig to be the best way to do analysis, prepare replication files, learn new methods, or teach.
Zelig is no longer being maintained, but a new package,
clarify, is available to provide much of Zelig’s functionality for simulation-based inference of interpretable post-estimation quantities. Please see the
clarify website for information on installing and using
clarify and for performing tasks in Zelig workflow using
Zelig includes many methods, based on likelihood, frequentist, Bayesian, robust Bayesian, nonparametric, and population and superpopulation theories of inference.
Zelig adds considerable infrastructure to improve the use of existing methods. It translates hard-to-interpret coefficients into meaningful quantities of interest, along with the uncertainty estimates (generalizing Clarify for Stata); automates graphics and numerical summaries for all models.
Zelig can also evaluate counterfactuals (via WhatIf); combine multiply imputed data sets to impute missing data and correct for measurement error (via Amelia and other multiple imputation functions); automates bootstrapping for all models; allows for matching for causal inference to reduce model dependence (via MatchIt and cem); and generates replication data files (for Dataverse to satisfy community replication standards).
To find out what’s new in the most recent version of Zelig, checkout the NEWS.
We love to get feedback on how to improve Zelig.
You can even make your R packages usable from Zelig by writinga few simple bridge functions. Checkout the Developer’s Guide for details.