
Changes in 0.1-1:

* Significant reductions in memory footprint.

* Default predictor-selction mode changed to 'predFixed' (like 'mTry')
  for small predictor counts.  'predProb' remains the default at higher
  count.

* Binary classification now employs faster, weight-based algorithm.

* Training produces rich internal state by default.  In particular,
  quantile validation and prediction can be performed without having
  to train specially for them.

* ForestFloorExport objects can be produced from training state for
  use by 'forestFloor' feature-analysis package.

* PreTrain method produces pre-sorted predictor format, saving
  recomputation when retraining iteratively, such as during a Caret
  session.

* OMP parallelization now performed per node/predictor pair, rather
  than per predictor.

* Optional 'regMono' vector enforces monotonic constraints on numeric
  regressors.
