CHANGES IN VERSION 1.3.7
-------------------------

  o DaMiRseq performs multi-class classification anlysis.

  o The Stacking meta-learner can be composed by the user,
	  setting the new parameter 'cl_type' of the
	  DaMiR.EnsembleLearning() function. Any combination
	  of the 8 classifiers is now allowed.

	o If the dataset is imbalanced, a 'Down-Sampling'
	  strategy is automatically applied.

	o The DaMiR.FSelect() function has the new
	  argument, called 'nPlsIter', which allows the
	  user to have a more robust features set. In fact,
	  several feature sets are generated by the bve_pls()
	  fuction (embedded in DaMiR.FSelect()), setting
	  'nPLSIter' parameter greater than 1.
	  Finally, an intersection among all the feature sets
	  is performed to return those features which constantly
	  occur in all runs. However, by default, 'nPlsIter = 1'.

	o DaMiR.Allplot() accepts also 'matrix' objects as well as NA values
	  (which are not plotted).

	o The DaMiR.normalization() function estimates the
	  dispersion, through the parameter 'nFitType'; as in
	  DESeq2 package, the argument can be
	  'parametric' (default), 'local' and 'mean'.

	o In the DaMiR.normalization() function, the gene
	  filtering is desabled if 'minCount = 0'.

	o In the DaMiR.EnsembleLearning() function, the method for
	  implementing the Logistic Regression has been changed
	  to allow multi-class comparisons; instead of the native
      'lm' function, 'bayesglm' method implemented in the caret
	  'train' function, properly set, is now used.

	o The new parameter 'second.var' of the DaMiR.SV() function,
      allows the user to take into account a secondary variable
	  of interest (factorial or numerical) that the user does
	  not wish to correct for, during the sv identification.