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 from the American Political Science Association. 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: https://iqss.github.io/clarify
- GitHub: https://github.com/iqss/clarify
- CRAN page: https://cran.r-project.org/package=clarify
- 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: https://github.com/iqss-research/clarify
- 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. https://doi.org/10.18637/jss.v008.i01
- 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