Fast Imputations Using 'Rcpp' and 'Armadillo'
Comparing imputations and original data distributions
fill_NA function for the imputations purpose.
fill_NA_N function for the multiple imputations purpose
miceFast package for fast multiple imputations.
naive_fill_NA function for the simple and automatic imputation
Finding in random manner one of the k closets points in a certain vect...
Class "Rcpp_corrData"
Class "Rcpp_miceFast"
upset plot for NA values
VIF function for assessing VIF.
Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.