Changes in 1.1.6

1 - 'rufImpute( )' has integrated all methods for the imputation of missing values present in the package. 
The option 'na.action' has been added with arguments 'na.action = c("accurateImpute", "fastImpute", "veryFastImpute")'. 'na.action' is also used in 'randomUniformForest( )' and includes, in addition, the argument "omit". See ?rufImpute for more details and examples.

2 - Breiman's bounds integrate now bias estimation (in classification) in order to warn the user about the possible
 estimation error of the Breiman's bounds. One has to integrate it to the 'strength' in order to correct bounds.
 A bias correction is proposed, see 'print.randomUniformForest( )' function.
 
3 - 'postProcessingVotes( )' integrates new options to improve classification and regression. In the latter a 'method'
 with argument 'residuals', is added. It has been first proposed by Xu (Improvements to random forest methodology, 2013).
 The method computes a second model that is built on the residuals of the first one. Then the resulted predictions 
 are subtracted from the predictions of the first model. 

4 - 'kappa statistic' has been introduced for classification.

5 - 'nstart' option has been added to the unsupervised mode for consistency with k-Means clustering.

6 - 'clusterAnalysis( )' now allows regression, transforming the dependent variable into 4 quantile intervals.
One can also transforms regression responses into factors and send them to 'clusteredObject' option.
Some bugs have also been corrected.

7 - 'clusteringObservations( )' did not work for large datasets. It has been corrected. 'subset' option is also integrated, allowing to choose a subsample.

8 - 'unsupervised.randomUniformForest( )' has been updated to allow more options. Please still consider it as a beta
release, since it uses many layers that need more optimization.

9 - 'reSMOTE()' now accepts data frame as an input.

10 - Interactions between pairs of features are now standardized in order for each variable to have an average interaction
comparable to 1.

11 - 'partialDependenceBetweenPredictors( )' now accepts more than two features in order to model dependencies between
all the features and their effect on the dependent variable. 

12 - Various bugs corrected.

Changes in 1.1.3 to 1.1.5

1 - Unsupervised mode has been updated to deeper take into account dimension reduction and visualization, 
and to be more flexible. Options and examples have also been updated and are more detailed.

2 - 'clusterAnalysis( )' function has been introduced. It allows to get a full analysis of the clustering results
in a compact and granular representation. It also works for classification.

3 - 'splitClusters( )' function has been added. it allows to split a cluster in 2 parts, creating new ones.

4 - Spectral clustering (Ng, Jordan and Weiss algorithm with slight modifications) has been introduced 
in a beta release.

5 - 'rm.coordinates( )' has been introduced. It allows to remove coordinates that do not matter when, and after, doing spectral clustering.
While not documented for now, one can look examples in 'unsupervised.randomUniformForest( )' function.

6 - Proximities matrix was not correctly normalized to have maximum value equal to 1. It is now corrected.

7 - Proximities matrix uses now two ways to compute similarities. The former one used similarities with a value 1
for any proximity, leading to a binary matrix. The new and default one follows exactly Breiman's formulation. 
Associated option is called 'sparseProximities', to choose between the two.

8 - Option 'categoricalvariablesidx' can now be replaced by its alias 'categorical' and values can be either subscripts 
or names of the variables (between quotes), or "all" (as before).

9 - 'rufImpute( )' has been added as an alias of 'fillNA2.randomUniformForest( )'. Examples have been detailed.

10 - 'reSMOTE( )' has been updated to sample minority class conditionally to the class distribution or to another
 variable of the training set.

11 - OOB evaluation, using 'rebalancedsampling' option, has been updated to take into account the whole sample
instead of a part of it (which were formerly, for each tree, OOB cases in the rebalanced sample).

12 - 'print( )' method of 'importance( )' and 'summary( )' methods of 'randomUniformForest( )' have been updated. 
 The especially add a comment to warn the user between the order of predictive (distinguish all classes) features, 
 which are plotted, different to the order of discriminant (distinguish one class much better than another) ones. 
 Both cases are present in the table of Global Variable Importance.

13 - 'plot( )' method of 'importance( )' function has been adapted to display partial dependencies for a better
 visibility, e.g. at first order for large datasets and by not filtering outliers. 

14 - Computing Breiman's bounds formerly used all available cores except logical ones, overriding 'threads' option.
It is now consistent with 'threads'.

15 - 'combine.unsupervised( )' has been renamed to 'combineUnsupervised( )' for consistency with S3 methods.

16 - Vignette has been updated. 

17 - Various bugs corrected and documentation improved.

Changes in 1.1.2

1 - Option 'usesubtrees' has been introduced for large datasets support. It allows to grow trees with an arbitrary depth
that are, in a second step, updated by extending all non pure nodes. The option is mainly designed to reduce memory
footprint while possibly providing faster convergence and computing times. Note that many other usages can be found. 

2 - Improvements on preprocessing time.

3 - Improvements on categorical variables. Previously, the engine did not take into account that a categorical variable
could be selected many times in each node ('the sampling with replacement features'), leading to a lower accuracy.
This new release allows categorical variables to, possibly, get further improvements using large 'mtry' values. 
For more informations about how categorical variables are handled, see the Details section of ?randomUniformForest.

4 - Extended support of large files for the unsupervised mode. New functions, 'update.unsupervised( )' and 'combine.unsupervised( )', added. 
The first one allows the unsupervised learning engine to be updated with new data. The second one (while experimental) allows to combine many unsupervised objects.
Both lead to incremental unsupervised learning while the second one will be able to handle (at least partially) shifting distributions. Some bugs have also been corrected.

5 - 'bagging' option updated to support the selection of an arbitrary number of variables when used in conjunction with 'mtry'. 
More precisely if 'bagging' is enabled and 'mtry' being a value lower or equal to the dimension of data, then sample without replacement of 'mtry' variables will be done.

6 - For imbalanced multi-classes classification, the algorithm was not reporting correctly the true labels. The bug is now fixed.

7 - Improvements and new examples for the generic cross-validation function 'generic.cv( )'.

8 - Improvements on the robustness to datasets which could, formerly, be weakened by some optimizations.

9 - Vignette has been updated to revise consistency between theoretical formulae and R code implementation.

10 - New vignette has been added.

Changes in 1.1.1

1 - 'subset' option added when using formula.

2 - Limited support for large files in unsupervised learning. More precisely, for datasets with more than 2000 rows
a subsample is learned in a full unsupervised mode and the remaining points are predicted in the MDS space, before applying clustering. 
Some options have also be removed, since they are not easy to assess. Next update will allow full support.

3 - 'clusteringObservations( )' function is now matching objects coming from a formula.

4 - 'modifyClusters( )' function is now working for unsupervised objects that use hierarchical clustering.

5 - Using specific combinations of options lead to build some trees with no defined leaves. Such trees lead R to crash.
Patch has been added to force all trees to have well defined leaves. 
Note that the algorithm deletes variables that are no longer useful during the growth of a tree (which was possibly a cause for the bug).

6 - Cut-points, information gain and 'Lp' distances return now values rounded to have 4 decimals.

7 - Missing values imputation, 'fillNA2.randomUniformForest( )' function, has been improved to better match categorical variables.

8 - Automatic detection of categorical variables has been improved.

9 - Algorithm has been updated to convert null character, "", into a numeric value. 
Hence it still is a missing value from the point of view of original data, 
but internally it is treated as an explicit numeric value different from others numeric ones in the variable. 

10 - OOB evaluation now detects cases with very imbalanced classes that can not be predicted correctly.

11 - A new (experimental) function, 'reSMOTE( )', has been added in order to handle very imbalanced datasets.

12 - Main functionalities have been detailed, see the Details section of ?randomUniformForest, and more examples added.

13 - Seed has been added to the unsupervised mode for stability of the results.

14 - Various bugs corrected.

Changes in 1.1.0

1 - New method used to choose cut-points lead to loss of accuracy for low values of 'mtry' and in the unsupervised case. 
Cut-points choice has been changed in order to separate classification, unsupervised mode and regression. 
In Regression and unsupervised learning, cut-points are chosen using the whole support of each candidate variable, 
as in initial release and prior to the 1.0.9 one. In Classification, the support is now reduced to two random points 
chosen among a small number. This leads to more diversity in trees. 

2 - Categorical variables were not correctly matched when one was using 'categoricalvariablesidx' option.

3 - Better handling of categorical variables when assessing partial dependencies.

Changes in 1.0.9 - 1.1.x :

1 - Cut-points of each node are now chosen between two random points of each candidate variable. 
Previously, the whole support was chosen. This update on the optimization criterion leads to faster computation while not changing accuracy. 

2 - Introduction of unsupervised learning using the following chain commands paradigm:
pre-processing (following Breiman's ideas) + randomUniformForest proximities matrix +  Multidimensional scaling + 
gap statistics (for estimating the number of clusters) + kmeans (for identifying every observation)
Unsupervised learning introduces then many concepts that still need to be properly referenced. 
Note that implementation for large datasets is currently not integrated.

3 - 'model.stats( )' and 'generic.cv( )' are now documented and bugs fixed.
'model.stats( )' function displays many informations (confusion matrix, ROC curves, residuals,...) for any vector
of predictions and responses. 'generic.cv( )' function proceeds to a k-fold cross-validation for any algorithm (with however some manual code).

4 - Better matching of NA values and comments to allow the right choice of imputation when predicting data with missing values.

5 - Improvements in speed and accuracy when using one (or both) of the options 'depth' or 'depthcontrol'.
Especially for large files (> 100 000 rows), increase both speed and accuracy when using the 'rUniformForest.big( )' function.
Note that the latter splits data in chunks, therefore one can not reach the accuracy of an (or the same) algorithm using the whole sample.

6 - Variable importance bug corrected for 'rUniformForest.big( )' function.

7 - Variable importance bug corrected when using formula in 'randomUniformForest( )' function.

8 - Improvements in speed, especially for large samples (> 50 000 rows) and high dimension (> 100 variables) in the regression case.
More precisely, large datasets take most of the benefits of optimizations added.

9 - Improvements in the accuracy of variable importance

10 - Minor bugs corrected

Changes in 1.0.7 - 1.0.8 :

1- Full support of categorical variables.
They were formerly matched by the same function than continuous ones, leading to some troubles in Variable importance and a loss of accuracy.
Now, categorical variables used their own engine, from modelling to variable importance and selection.
In addition, one can now use discrete values as categorical, e.g., in order to know if one frequent value in a variable can affect the response.
Note that accuracy might drop in contrast of the default case, for which algorithm considers all variables as continuous ones,

2 - Partial dependence plots.
better match of categorical variables and 3D representation

3 - Quantile regression.
Value of quantile was between 1 and 99 and not 0 and 1 as required.

4 - Variable importance. 
Bugs correction : when prediction Object was present, importance was not correctly computed, leading to wrong interactions between features.
Interactions plot was also inverted (1rst order was 2nd).

5 - Variable importance is now allowed with mtry = 1. As a consequence, one can assess variable importance using purely random forest.

6 - outputperturbationSampling.
It is now allowed to replace all original values of train responses by a random vector sampled from those values (mean and variance)
using gaussian distribution.

7 - Summary.
random Uniform forests summary is now unified and one can also use model.stats( ) function to assess predictions vs responses.

8 - Missing values.
fillNA2.randomUniformForest( ) is now working with large files.

9 - Prediction. 
Prediction is now possible for only one observation.

10- Rebalanced sampling.
Sample of any size or class distribution is allowed with rebalancedsampling option.

11 - Area Under Precision-Recall Curve is available using roc.curve( ) function.

12 - rUniformForest.grow( ) function is now working with outputperturbationSampling enabled in first run before adding more trees.

13 - Various bugs corrected.