Changes in 0.1-9:

* Option 'nThread' limits OpenMP parallelization to maximum number of threads.

* Option 'oob' specifies an out-of-bag constraint for prediction.

* Row sampling now implemented using 'Rcpp', in place of 'rcppArmadillo'.

* Package 'data.table' now implements block decomposition of 'data.frame'.


Changes in 0.1-8:

* Command 'Validate' enables separate execution of out-of-bag validation.

* Command 'Streamline' shrinks trained Rborist objects by emptying unused fields.

* Option 'maxLeaf' prunes trees during training to a maximum number of leaves.


Changes in 0.1-4:

* Sparse 'dcGMatrix' matrices accepted, if encoded in 'i/p' format.

* Autocompression conserves space on a per-predictor basis.

* Space-saving 'thinLeaves' option suppresses creation of summary data. 

* 'splitQuantile' option allows fine tuning of split-point placement for
  numerical predictors.

* Improved scaling with row count.


Changes in 0.1-2:

 * Improved scaling with predictor count.

 * Improved conformance with Caret package.

 * 'minNode' default lowered to reflect uniqueness of indices
   referenced within a node.

 * Name change:  PreTrain deprecated in favor of PreFormat.

 * Minor reorganization to support sparse internal representation
   planned for next release.
   

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.
