Clarify: Software for Interpreting and Presenting Statistical Results

This is a set of easy-to-use tools that implement the techniques described in Gary King, Michael Tomz, and Jason Wittenberg's "Making the Most of Statistical Analyses: Improving Interpretation and Presentation". Winner of the Okidata Best Research Software Award. These tools use Monte Carlo simulations to compute interpretable quantities from regression models and perform inference on them.

{clarify} for R

  • Implements predictions at representative values, average marginal effects, and any user-specified quantities of interest in a simulation framework, as well as visualization methods. {clarify} for R represents an evolution of the {Zelig} R package by restoring and adding to simulation-based functionality for translating hard-to-interpret coefficients into meaningful quantities of interest. 
  • Authors: Noah Greifer, Steven Worthington, Stefano Iacus, and Gary King.
  • Website:
  • GitHub:
  • CRAN page:
  • See website for installation instructions, documentation, and examples.
  • Provides functionality previously provided by {Zelig}; see instructions on website for converting a {Zelig}-based workflow to one that uses {clarify} instead.

clarify for Stata

  • Implements predictions at representative values and visualization methods in a simulation framework.
  • Authors: Michael Tomz, Jason Wittenberg, and Gary King.
  • Github: 
  • Installation instructions and documentation are provided in a JSS Paper: 
    • Tomz, Michael, Jason Wittenberg, and Gary King. 2003. “Clarify: Software for Interpreting and Presenting Statistical Results.” Journal of Statistical Software 8: 1–30.
  • A user donated wrapper from Fred Wolfe is available to automate clarify's simulation of dummy variables and can be installed with: ssc install qsim