(Rässler, 2002, D’Orazio et al. 2006). This is due to the large amount of data-sets available and, at the same time, to the need of timely and not costly information. Statistical Matching techniques aim at combining information from different sources. In particular, it is assumed that the two sources (e.g. two samples) do not observe the same set of units, so that neither merging nor record linkage techniques can be applied. In order to explore the properties of matching techniques and therefore apply them to real data problems, a series of matching experiments have been carried out in the R environment. R has already been used for the definition of some statistical matching algorithms (Rässler 2002). In D’Orazio et al (2006) more algorithms have been translated in R. The codes will be available on the web pag
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