1.4.0 Addedd functions for plotting results, changes to some code for better management of the NAs

		NND.hotdeck and RANDwNND.hotdeck NO more trasform the categorical matching variables in dummies
		when the chosen distance function is defined only for numerical variables; in practice, mixed-type matching variables
		can only be used with the Gower's distance

		fact2dummy: when a NA is observed for a categorical variable then the function puts NAs in all the dummy 
                variables generated from it

		pw.assoc discards NAs before calculation of the associaione or PRE measures; removal follows the pairwise 
		deletion rule (units where one of both the values are missing are discarded)

		plotTab is a NEW function for comparing the marginal distributions of the same categorica variable(s) but estimated 
		from two different data sources

		plotCont is a NEW function for comparing the marginal distributions of the same numerical variable but 
		estimated from two different data sources

		plotBounds is a NEW function providing a graphical summary of the width of the Frechet Bounds estimated with 
		the Frechet.bounds.cat function


#########################################################################################################################

1.3.0 changes in the functions related to uncertainty investigation when dealing with categorical variables

		Frechet.bounds.cat now permits to align marginal distributions of X variables via IPF algorithm
		(previously harmonization had to be done befor calling it by using harmonize.x function)
		
		Fbwidths.by.x provides penalty measures because of the increase of cells to estimate when increasing the number of Xs.
		Sparsness of tables is explicitly considered.

		New function selMtc.by.unc() permits to identify best subset of matching variables which minimize a penalized 
		uncertainty estimate, as in D'Orazio, Di Zio, Scanu 2017 paper (see ref in help pages)

		Updates in pw.assoc() to allow computation of bias corrected Cramer's V, mutual information (also 
		normalized), AIC and BIC. Results can be organized in a data.frame. Changes in the documentation layout
		to achieve coherence with documentation of other functions in the package
		
		Please note that Vignette is frozen to StatMatch 1.2.5, therefore it will not provide new feauter related to investigation
		of uncertainty and more in general selecting of matching variables. 
		New vignette related to uncertainty topic is expected to be realesed in future.

#####################################################################################################################

1.2.5 gower.dist is faster and more efficient due improvements of Jan van der Laan (also thanks to Ton de Waal )
		
		NND.hotdeck allows performing constrained search of donors, allowing donor to be selected not more than k times (k>=1). 
                argument k is set by the user		

		fixed a minor bug in RANDwNND.hotdeck (not affecting results)
		
		richer output in Frechet.bounds.cat and Fb.widths.byx

1.2.4 added the new function pBayes for applying pseudo-Bayes estimator to sparse contingency tables
		
		modified comb.samples to handle a continuous target variable (Y or Z)
		
		Faster versions of Frechet.bound.cat and Fbwidths.by.x.
		 
		Fbwidths.by.x now provides a richer output. 

1.2.3 corrected a bug in RANDwNND.hotdeck. Thanks to Kirill Muller

1.2.2 added 3 data sets used in the function's help pages and in the vignette

		modified the RANDwNND.hotdeck function to identify the subset of the donors by
		simple comparing the values of a single matching variable 

		Minor modification of the hotdeck functions to handle and monitor the processing
		when dealing with donation classes
		
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()

