Ensembling across missing data

This PR adds functionality to the `Dev` branch to allow the user to predict across models with data available and preserve predictions from an ensemble of models with available data. If models have different predictors and those predictors have differential missingness, this will allow the caretEnsemble list to produce a prediction from all available models and weight it by their strength. I'm not sure this is the best way to do this just yet, but I added some structure for a proposal of how it can be done. Further refinements are probably necessary. 

A necessary consequence of this is that any arguments passed to `...` must now be named. I don't think this is a big problem, but it would break existing scripts.