todo: Elastic net method

33 Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J

Roy Stat Soc B 67, 301-320, (2005).

34 Zou, H. & Zhang, H. H. On the Adaptive Elastic-Net with a Diverging Number of

Parameters. Ann Stat 37, 1733-1751, (2009).

https://www.r-bloggers.com/kickin-it-with-elastic-net-regression/

"Ridge regression is a really effective technique for thwarting overfitting. It does this by penalizing the L2 norm (euclidean distance) of the coefficient vector which results in “shrinking” the beta coefficients. The aggressiveness of the penalty is controlled by a parameter ."

"Lasso regression is a related regularization method. Instead of using the L2 norm, though, it penalizes the L1 norm (manhattan distance) of the coefficient vector."

"Elastic net regression is a hybrid approach that blends both penalization of the L2 and L1 norms."