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.


Changes in 0.3-0:

* Prediction and validation introduce permutation testing.

* Missing predictor values accepted for training, validation and prediction.

* Option 'trapUnobserved' allows validation and prediction to report
  a nonterminal score upon ecountering missing data or values not
  observed during training.

* Prediction no longer limits data set size to 32 bits.

* Training accepts data sets with size exceeding 32 bits under
  conditional compilation.  This is an experimental feature and has
  not been well tested.


Changes in 0.3-3:

* Option 'indexing' indicates whether to report final indices of tree
  traversal during prediction and validation.

* Introduces 'forestWeight' utility to report prediction weights as
  described by Meinshausen, 2006.

* Option 'keyedFrame' indicates whether predictor columns in a "new"
  data frame submitted to prediction need may be unordered.  That
  is, column names may be looked up and matched with their counterparts
  from the original training frame.  Among other advantages, this
  allows the prediction frame to be a superset of the training frame.