
##### Version 0.14.0
* Faster permutation variable importance for high dimensional data (thanks to Roman Hornung)
* Add deforest() function to remove trees from ensemble
* Allow split.select.weights and always.split.variables together
* Add as.data.frame() method for predictions
* Fix weight calculation in case-specific RF (csrf())
* Fix cross compiling for Windows

##### Version 0.13.0
* Faster quantile prediction
* Add ... argument to ranger()
* Bug fixes

##### Version 0.12.0
* Faster computation (in some cases)
* Add local variable importance 
* Add "hellinger" splitrule for binary classification
* Add "beta" splitrule for bounded outcomes
* Accept user-specified function in quantile prediction
* Add regularization
* Add x/y interface
* Internal changes (seed differences possible, prediction incompatible with older versions)
* Bug fixes

##### Version 0.11.0
* Add max.depth parameter to limit tree depth
* Add inbag argument for manual selection of observations in trees
* Add support of splitting weights for corrected impurity importance 
* Internal changes (slightly improved computation speed)
* Warning: Possible seed differences compared to older versions
* Bug fixes

##### Version 0.10.0
* Change license of C++ core to MIT (R package is still GPL3)
* Better 'order' mode for unordered factors for multiclass and survival
* Add 'order' mode for unordered factors for GenABEL SNP data (binary classification and regression)
* Add class-weighted Gini splitting
* Add fixed proportion sampling
* Add impurity importance for the maxstat splitting rule
* Remove GenABEL from suggested packages (removed from CRAN). GenABEL data is still supported
* Improve memory management (internal changes)
* Bug fixes

##### Version 0.9.0
* Add bias-corrected impurity importance (actual impurity reduction, AIR)
* Add quantile prediction as in quantile regression forests
* Add treeInfo() function to extract human readable tree structure
* Add standard error estimation with the infinitesimal jackknife (now the default)
* Add impurity importance for survival forests
* Faster aggregation of predictions
* Fix memory issues on Windows 7
* Bug fixes

##### Version 0.8.0
* Handle sparse data of class Matrix::dgCMatrix
* Add prediction of standard errors to predict()
* Allow devtools::install_github() without subdir and on Windows
* Bug fixes

##### Version 0.7.0
* Add randomized splitting (extraTrees)
* Better formula interface: Support interactions terms and faster computation
* Split at mid-point between candidate values
* Improvements in holdoutRF and importance p-value estimation
* Drop unused factor levels in outcome before growing
* Add predict.all for probability and survival prediction
* Bug fixes

##### Version 0.6.0
* Set write.forest=TRUE by default
* Add num.trees option to predict()
* Faster version of getTerminalNodeIDs(), included in predict()
* Handle new factor levels in 'order' mode
* Use unadjusted p-value for 2 categories in maxstat splitting
* Bug fixes

##### Version 0.5.0
* Add Windows multithreading support for new toolchain
* Add splitting by maximally selected rank statistics for survival and regression forests
* Faster method for unordered factor splitting
* Add p-values for variable importance
* Runtime improvement for regression forests on classification data
* Bug fixes

##### Version 0.4.0
* Reduce memory usage of savest forest objects (changed child.nodeIDs interface)
* Add keep.inbag option to track in-bag counts
* Add option sample.fraction for fraction of sampled observations
* Add tree-wise split.select.weights
* Add predict.all option in predict() to get individual predictions for each tree for classification and regression
* Add case-specific random forests
* Add case weights (weighted bootstrapping or subsampling)
* Remove tuning functions, please use mlr or caret
* Catch error of outdated gcc not supporting C++11 completely
* Bug fixes

##### Version 0.3.0
* Allow the user to interrupt computation from R
* Transpose classification.table and rename to confusion.matrix
* Respect R seed for prediction
* Memory improvements for variable importance computation
* Fix bug: Probability prediction for single observations
* Fix bug: Results not identical when using alternative interface

##### Version 0.2.7 
* Small fixes for Solaris compiler

##### Version 0.2.6 
* Add C-index splitting
* Fix NA SNP handling

##### Version 0.2.5 
* Fix matrix and gwaa alternative survival interface
* Version submitted to JSS

##### Version 0.2.4 
* Small changes in documentation

##### Version 0.2.3 
* Preallocate memory for splitting

##### Version 0.2.2 
* Remove recursive splitting

##### Version 0.2.1 
* Allow matrix as input data in R version

##### Version 0.2.0 
* Fix prediction of classification forests in R

##### Version 0.1.9 
* Speedup growing for continuous covariates
* Add memory save option to save memory for very large datasets (but slower)
* Remove memory mode option from R version since no performance gain

##### Version 0.1.8 
* Fix problems when using Rcpp <0.11.4

##### Version 0.1.7 
* Add option to split on unordered categorical covariates

##### Version 0.1.6 
* Optimize memory management for very large survival forests

##### Version  0.1.5 
* Set required Rcpp version to 0.11.2
* Fix large $call objects when using BatchJobs
* Add details and example on GenABEL usage to documentation
* Minor changes to documentation

##### Version 0.1.4 
* Speedup for survival forests with continuous covariates
* R version: Generate seed from R. It is no longer necessary to set the
  seed argument in ranger calls.

##### Version 0.1.3 
* Windows support for R version (without multithreading)

##### Version 0.1.2 
* Speedup growing of regression and probability prediction forests
* Prediction forests are now handled like regression forests: MSE used for
	prediction error and permutation importance
* Fixed name conflict with randomForest package for "importance"
* Fixed a bug: prediction function is now working for probability
	prediction forests
* Slot "predictions" for probability forests now contains class probabilities
* importance function is now working even if randomForest package is
	loaded after ranger
* Fixed a bug: Split selection weights are now working as expected
* Small changes in documentation
