Changes in 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.