miceFast: 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.

Version: 0.6.1
Depends: R (≥ 3.6.0)
Imports: methods, data.table, dplyr, magrittr, Rcpp (≥ 0.12.12), lifecycle
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, pacman, testthat, mice, broom, car, ggplot2
Published: 2020-07-06
Author: Maciej Nasinski [aut, cre]
Maintainer: Maciej Nasinski <nasinski.maciej at gmail.com>
BugReports: https://github.com/Polkas/miceFast/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/Polkas/miceFast
NeedsCompilation: yes
SystemRequirements: C++11
Materials: NEWS
In views: MissingData
CRAN checks: miceFast results

Downloads:

Reference manual: miceFast.pdf
Vignettes: miceFast - Introduction
Package source: miceFast_0.6.1.tar.gz
Windows binaries: r-devel: miceFast_0.6.1.zip, r-release: miceFast_0.6.1.zip, r-oldrel: miceFast_0.5.1.zip
macOS binaries: r-release: miceFast_0.6.1.tgz, r-oldrel: miceFast_0.6.1.tgz
Old sources: miceFast archive

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