CLARIFY: Stata modules for Monte Carlo simulation

Clarify is a suite of Stata macros that use Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing any statistical assumptions. The ado files, designed for use with Stata version 6.0 or higher, offer a convenient way to implement the techniques described in Gary King, Michael Tomz, and Jason Wittenberg, "Making the Most of Statistical Analyses: Improving Interpretation and Presentation," American Journal of Political Science 44 (April 2000): 347-61. Clarify 2.0 simulates quantities of interest for many commonly used statistical models: linear regression, binary logit and probit, ordered logit and probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data. The current release of Clarify inlcudes a number of enhancements over previous versions, including: support for several new models, including weibull, SUR, and the additive logistic normal model for compositional data (e.g. the distribution of votes in multiparty elections); the ability to apply standard transformations -- such as natural logs and exponents -- to dependent variables, estimate a model, and then automatically reverse those transformations when interpreting the results; compatibility with Amelia. If you use multiple imputation for missing data, Clarify will analyze all the multiply imputed datasets, appropriately combine the results, and compute quantities of interest; More powerful and convenient commands for setting the values of explanatory variables (the X's), using either single or multiply-imputed datasets to compute descriptive statistics