1.2.1 now Frechet.bounds.cat() can be called just to compute the uncertainty bounds
		when no X variables are available.
		
		RANDwNND.hotdeck can search for the closest k nearest neighbours by using the
		function nn2() in the package RANN (wrap of the Artificial Neural Network
		implemented in the package ANN).  It is very fast and efficient when dealing
		with large data sources.
		
		Fix of a minor bug in mixed.mtc()

1.2.0	new function comp.prop() for computing similarities/dissimilarities
		between marginal/joint distributions of one or more categorical variables
		
		new function pw.assoc() to compute pairwise association measures among 
		categorical response variable and a series of categorical predictors 
        
		rankNND.hotdeck() can perform constrained matching too
		
		rankNND.hotdeck(), NND.hotdeck() and mixed.mtc() solve constrained problems 
		more efficiently  and faster by using solve_LSAP() in package "clue" 
		or (slower) by means of functions in the package "lpSolve".  
		It is no more possible to solve constrained  problems by means 
		of functions in package "optmatch"
		
		NDD.hotdeck(), RDDwNND.hotdeck() and rankNND.hotdeck() are more
		efficient in handling donation classes (thanks to Alexis Eidelman
		for suggestion).
		
		fixed a bug in mahalanobis.dist (thanks to Bruno C. Vidigal)
 
1.1.0   The function comb.samples() now allows to derive predictions
		at micro level for the target variables Y and Z

1.0.5	fixed some minor bugs

1.0.4	fixed some minor bugs

1.0.3	now mixed.mtc() can handle also categorical common variables

		fixed a bug in comb.samples() when handling factor levels

		new error messages in RANDwNND.hotdeck() when computing ditances 
		between units with missing values

1.0.2   new function mahalanobis.dist() to compute the mahalanobis distance

		fixed a bug in mixed.mtc() when computing the range of admissible values
		for rho_yz
		
		fixed a bug in NND.hotdeck()  and RANDwNND.hotdeck() when
		managing the row.names
		
1.0.1	new functions harmonize.x() and comb.samples() to perform statistical
		matching when dealing with complex sample survey data via 
		weight calibration.

		new function Frechet.bounds.cat() to explore uncertainty when dealing with 
		categorical variables. The function Fbwidths.by.x()	permits to
		identify the subset of the common variables that performs better in reducing 
		uncertainty
		
		New function rankNND.hotdeck() to perform rank hot deck distance
		
		Update of RANDwNND.hotdeck() to use donor weight in selecting a donor
		
		new function maximum.dist() that computes distances according to the
		L^Inf norm. A rank transformation of the variables can be used.
		
0.8    fixed some bugs in NND.hotdeck() and RANDwNND.hotdeck()

