[NEW] Implement higher order markov chain models

The Markov Attribution implemented uses a first order markov chain model to compute the removal effects for each channel. This underlying model assumes that the probability of the next future state depends only in the current state and none of the previous. It is reasonable to state that this is not an appropriate assumption for the buyer journey and it would be useful to allow for higher order markov models.

I'm not sure if this is the best way, but one way to accomplish this is to expand the state space to include all the recent states up to the order r we're trying to model, introducing a new _null_ state (not to be confused with the state representing non converting paths) to pad the start of the transition chain. The removal effect algorithm will need to be changed, since there will be multiple states associated with the original one we wanted to remove.