Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

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()