Gary King

Authors: Kosuke Imai, Gary King, Olivia Lau

Zelig is a single, easy-to-use program that can estimate, help interpret, and present the results of a large range of statistical methods. It literally is "everyone's statistical software" because Zelig uses (R) code from many researchers. We also hope it will become "everyone's statistical software" for applications, and we have designed it so that anyone can use it or add their methods to it. Zelig comes with detailed, self-contained documentation that minimizes startup costs for Zelig and R, automates graphics and summaries for all models, and, with only three simple commands required, generally makes the power of R accessible for all users. Zelig also works well for teaching, and is designed so that scholars can use the same program with students that they use for their research.

Zelig adds considerable infrastructure to improve the use of existing methods. It generalizes the program Clarify (for Stata), which translates hard-to-interpret coefficients into quantities of interest; combines multiply imputed data sets (such as output from Amelia) to deal with missing data; automates bootstrapping for all models; uses sophisticated nonparametric matching commands which improve parametric procedures (via MatchIt); allows one-line commands to run analyses in all designated strata; automates the creation of replication data files so that you (or, if you wish, anyone else) can replicate the results of your analyses (hence satisfying the replication standard); makes it easy to evaluate counterfactuals (via WhatIf); and allows conditional population and superpopulation inferences. Zelig includes many specific methods, based on likelihood, frequentist, Bayesian, robust Bayesian, and nonparametric theories of inference.

• Documentation and Installation
• Please send All questions, bugs and requests: Zelig Mailing List, [Un]Subscribe, or Browse/Search Archives
• A paper that describes the advances underlying Zelig software: Kosuke Imai, Gary King, and Olivia Lau. "Toward A Common Framework for Statistical Analysis and Development" Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913 (Abstract: HTML | Paper: PDF)

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