Estimates of proportion and rate-based performance measures may involve discrete distributions, small sample sizes, and extreme outcomes. Common methods for uncertainty characterization have limited accuracy in these circumstances. Accurate confidence interval estimators for proportions, rates, and their differences are described and MATLAB programs are made available. The resulting confidence intervals are validated and compared to common methods. The programs search for confidence intervals using an integration of the Bayesian posterior with diffuse priors to measure the confidence level. The confidence interval estimators can find one or two-sided intervals. For two-sided intervals, either minimal-length, balanced-tail probabilities, or balanced-width can be selected.