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: 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
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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