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.
1.2.5 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()