Made Forest into an abstract class with OnlineForest (in memory; same as previous)
and OfflineForest (reads individual trees only as needed). Many methods were changed.
Fixed some tests that weren't running.
Fixed a bug where training crashed if FactorCovariates had any NA
Fixed a bug where FactorCovariates were ignored in splitting if nsplit==0
Added a covariate specific option for whether splitting on an NA variable should have a penalty.
This penalty is accomplished by first calculating the split score and best split for a covariate
without NAs as done previously before. Then NAs are randomly assigned, and the split score is
recalculated on that best split. The new score is the lower of the new score and the original.
Specifically, the integration returned an NaN if the integration was
*up to* an NaN (real inegrals are robust); and the results were negative
if integrating from a to b where a > b.
