Fast Imputations Using 'Rcpp' and 'Armadillo'

Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. 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. Moreover there are offered a few functions built to work with popular R packages such as 'data.table'.


News

0.2.1-3

  • 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

0.2.0

  • 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

0.1.0

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

0.0.3

  • vignette,readme,description,todo

0.0.2

  • 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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install.packages("miceFast")

0.2.3 by Maciej Nasinski, 9 months ago


https://github.com/Polkas/miceFast


Report a bug at https://github.com/Polkas/miceFast/issues


Browse source code at https://github.com/cran/miceFast


Authors: Maciej Nasinski [aut, cre]


Documentation:   PDF Manual  


Task views: Missing Data


GPL (>= 2) license


Imports methods, Rcpp

Suggests data.table, knitr, rmarkdown, pacman, testthat, mice, dplyr, broom, car, magrittr

Linking to Rcpp, RcppArmadillo

System requirements: C++11


See at CRAN