The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparitiesamong student subgroups remain. To address this the state has created the Wisconsin Dropout EarlyWarning System (DEWS), a predictive model of student dropout risk for students in grades six throughnine. The Wisconsin DEWS is in use statewide and currently provides predictions on the likelihood ofgraduation for over 225,000 students. DEWS represents a novel statistical learning based approach to thechallenge of assessing the risk of non-graduation for students and provides highly accurate predictionsfor students in the middle grades without expanding beyond mandated administrative data collections.Similar dropout early warning systems are in place in many jurisdictions across the country. Priorresearch has shown that in many cases the indicators used by such systems do a poor job of balancing thetrade off between correct classification of likely dropouts and false-alarm (Bowers et al., 2013). Buildingon this work, DEWS uses the receiver-operating characteristic (ROC) metric to identify the best possibleset of statistical models for making predictions about individual students.This paper describes the DEWS approach and the software behind it, which leverages the open sourcestatistical language R (R Core Team, 2013). As a result DEWS is a flexible series of software modulesthat can adapt to new data, new algorithms, and new outcome variables to not only predict dropout, butalso impute key predictors as well. The design and implementation of each of these modules is describedin detail as well as the open-source R package,EWStools, that serves as the core of DEWS (Knowles,2014).