I realise I should have been more clear. My apologies.
There is an increasing attempt to identify methods in meta-learning, algorithm selection, and algorithm configuration that can a) speed-up the ML process; b) possibly simplify the overall set of tasks for data scientist in training (this is a slightly more doubtful kind of goal).
There is a website dedicated to this: http://www.ml4aad.org/automl/
There is a paying-for package with H2O and open source ones in Weka (AutoWeka) and python (with the package Auto-sklearn in GitHub - I cannot paste more than one link as I'm a new user).
I know Google has announced AutoML, possibly on similar goals.
My questions is more R focused. Is there any activity e.g. around Caret (different but the most similar thing to scikit-learn in R) or other packages on the line of the principles outlined by http://www.ml4aad.org/automl/ and published in a variety of papers at NIPS etc. since 2015?