Preprocessing is often the most time-consuming phase in data analysis and preprocessing transformations interdependent in unexpected ways. This package helps to make preprocessing faster and more effective. It provides an S4 framework for creating and evaluating preprocessing combinations for classification, clustering and outlier detection. The framework supports adding of user-defined preprocessors and preprocessing phases. Default preprocessors can be used for low variance removal, missing value imputation, scaling, outlier removal, noise smoothing, feature selection and class imbalance correction.