Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
-parameter to yield prediction vectors of sub-ensembles and also summary and plots -add parameter to use percentile ranks instead of calibration -incorporate statistical test in table -mechanism to compute and plot variance of predictions for all sub-ensembles and meta ensemble
-added a seventh base classifier: rotation forest -add an eight base classifier: bagged nearest neighbors -added parameters for size of sub-ensembles -added oversampling to alleviate problems related to class imbalance and subsequent subsetting -added a filter parameter to remove near constants that often produced problems in subsetting. -added error handling -added formal automated tests -refactored for faster and shorter code -changed to [-1,1] scaling in neural networks -made sure that no (near) constants can be produced in bagging procedures -corrected typesetting issues in documentation