Fast Imputations Using 'Rcpp' and 'Armadillo'

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.



  • data.table problem - jump to R 3.5.0
  • valgrind - a lot of optimizations - problem with arma::exp and arma::randn
  • optimize a lot of code
  • methods/functions resistant to glitches


  • fix imputations with a grouping variable - error if there is precisly one NA at any group
  • add data.table to benchmarks - model with a grouping variable
  • add R functions (fill_NA_N,fill_NA,VIF) which could be used by a data.table user


  • add impute_N method - optimized multiple imputations
  • add vif method - Variance inflation factors


  • vignette,readme,description,todo


  • adjust to solaris
  • reference - set a grouping variable by a reference but as a numeric vector - integer vector do not work (randomly lost pointer)

Reference manual

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0.7.1 by Maciej Nasinski, 3 months ago

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Browse source code at

Authors: Maciej Nasinski [aut, cre]

Documentation:   PDF Manual  

Task views: Missing Data

GPL (>= 2) license

Imports methods, data.table, dplyr, magrittr, Rcpp, UpSetR, ggplot2, tidyr, assertthat

Suggests knitr, rmarkdown, pacman, testthat, mice, broom, car

Linking to Rcpp, RcppArmadillo

System requirements: C++11

See at CRAN