New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
#4.6.0 -- new option for kNN 'weightDist' to use the distances for the k nearest neighbours as weights -- Bytecompile is enabled -- The R function which.minN is not used anymore, instead there is a C++ function, kNN is now about 1.6 times faster on a replication (100x) of the sleep dataset #4.4.0 -- bugfix wrong observations marked as imputed in the hotdeck function -- random sorting is now used in hotdeck if no ord_var is defined #4.2.3 -- bugfix for the computation of distances for ordered variables #4.2.1 -- new option for kNN 'useImputedDist' if the imputed values of a variable should be used in subsequent imputation of another variable. #4.2.0 -- bug fixed in irmi with newer version of nnet (multinom) and if residual scale can not be computed (noise) -- Improvement Gower dist with only missing values in data.x or data.y #4.1.0 -- new parameter modelFormula in irmi -- bug fixes in irmi -- updated hotdeck based on data.table -> faster and quite stable -- bug fix if range of a variable is 0 in gower.dist -- small fixed kNN #4.0.1 -- small bugfix for using makeNA in kNN -- "Nothing to impute"-Error is now a warning -- imp_var now updates existing TF imp_vars (with warning)
#4.0.0 -- new pacakge VIMGUI contains all GUI functions -- vignettes moved to VIMGUI -- new imputation function regressionImp -- roxygen style comments -> help files