Data fix

This includes some fixes to the optimizers to allow them to handle missing data as well as to issue warnings when the optimization fails. I redefined the check for optimization resulting in a worse fit than the single best model by recalculating the AUC/RMSE outside of the while loop. 

I also fixed #102. The dataset we use is now stored in the datasets directory and has no need to be rebuilt from EWStools. 

I also modified `caretEnsemble` to issue a message when the library models produce predictions with missingness that is inconsistent within the models. Ensembling will still work, but the weights might be biased depending on the differences in the pattern of missingness between the predictors. It is not a warning, though we could change it to a warning. 
Should this maybe be an error, rather than a message?  How do we handle the case where Missingness is not consistent across models?