Adding better argument checking in the Baum Welch learning algorithms.
Fixing failing tests.
Adding a split step option to accelerate C4.5 (though this is not an ideal solution - most likely the algorithm could avoid the several copies and recursive calls).
Updating descriptive analysis to support also quantile computation.
Adding an apply in place variant to the normalization filter;
Workaround for ChiSquare test when expected frequency is zero.
Working on documentation.
Updated tests to match correction in EmpiricalDistribution

For further information, see comments in associated commit:
https://github.com/accord-net/framework/commit/dbe8eeaddd1d3b720e4ba325e56c8cf85bffa339
implement a .Fit for Pareto.  Doesn't do anything with weights.
whitespace