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Introduction

${\mathfrak{C}}$larify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models. The program, designed for use with the Stata statistics package, offers a convenient way to implement the techniques described in:

Gary King, Michael Tomz, and Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation.'' American Journal of Political Science 44, no. 2 (April 2000): 347$-$61.

We recommend that you read this paper before using the software.

${\mathfrak{C}}$larify 2.0 simulates quantities of interest for the most commonly used statistical models, including linear regression, binary logit, binary probit, ordered logit, ordered probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data.