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: |
0.8.2 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
methods, Rcpp (≥ 0.12.12), data.table |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, pacman, testthat, mice, magrittr, ggplot2, UpSetR, dplyr |
| Published: |
2022-11-17 |
| DOI: |
10.32614/CRAN.package.miceFast |
| 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: |
README NEWS |
| In views: |
MissingData |
| CRAN checks: |
miceFast results |
Documentation:
Downloads:
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