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. 
What the optimizer will do is calculate the `colAUC` for the non-missing values in each column and exclude any missing values column by column. So the problem I was running into occurred when 1 model had missingness and the other 3 did not. I still came up with a vector of weights but those weights were based on the AUC for 3 of the models on a bigger set of observations than the model with missing data. 

I think we should allow this to proceed, but maybe issue a warning that says the model weights may be biased by missingness? It seems overly prescriptive to throw an error and stop.