Receiver operating characteristic (ROC) analysis is used for comparing predictive models, both in model selection and model evaluation. This method is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic regression model with a set of independent variables, the predictive performance of this set of variables -as assessed by the area under the curve (AUC) from a ROC curve- must be estimated for a sample (the test sample) that is independent of the sample used to predict the dependent variable (the training sample). An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to new cases. Evaluating the predictive performance (AUC) of a set of independent variables using all cases from the original analysis sample tends to result in an overly optimistic estimate of predictive performance. K-fold cross-validation can be used to generate a more realistic estimate of predictive performance. To assess this ability in situations in which the number of observations is not very large, cross-validation and bootstrap strategies are useful. cvauroc implements k-fold cross-validation for the AUC for a binary outcome after fitting a logistic or probit regression model averaging the AUCs corresponding to each fold and bootstrapping the cross-validated AUC to obtain statistical inference and 95% bootstrap bias-corrected confidence Intervals (CI). Furthermore, cvauroc optionally provides the cross-validated fitted probabilities for the dependent variable or outcome contained in a new variable named _fit, the sensitivity and specificity for each level of the dependent variable, contained in two new variables named, _sen and _spe and the plot for the mean cvAUC and k-fold ROC curves.