Package: miceFast 0.8.2

Maciej Nasinski

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.

Authors:Maciej Nasinski [aut, cre]

miceFast_0.8.2.tar.gz
miceFast_0.8.2.tar.gz(r-4.5-noble)miceFast_0.8.2.tar.gz(r-4.4-noble)
miceFast_0.8.2.tgz(r-4.4-emscripten)miceFast_0.8.2.tgz(r-4.3-emscripten)
miceFast.pdf |miceFast.html
miceFast/json (API)
NEWS

# Install 'miceFast' in R:
install.packages('miceFast', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/polkas/micefast/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • air_miss - Airquality dataset with additional variables

openblascppopenmp

3.13 score 27 scripts 330 downloads 9 exports 3 dependencies

Last updated 2 years agofrom:f21d8ca900. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 23 2024
R-4.5-linux-x86_64NOTEDec 23 2024

Exports:compare_impcorrDatafill_NAfill_NA_NmiceFastnaive_fill_NAneiboupset_NAVIF

Dependencies:data.tableRcppRcppArmadillo

miceFast - Introduction

Rendered frommiceFast-intro.Rmdusingknitr::rmarkdownon Dec 23 2024.

Last update: 2022-11-17
Started: 2018-03-19

Readme and manuals

Help Manual

Help pageTopics
miceFast package for fast multiple imputations.miceFast-package
airquality dataset with additional variablesair_miss
Comparing imputations and original data distributionscompare_imp
'fill_NA' function for the imputations purpose.fill_NA fill_NA.data.frame fill_NA.data.table fill_NA.matrix
'fill_NA_N' function for the multiple imputations purposefill_NA_N fill_NA_N.data.frame fill_NA_N.data.table fill_NA_N.matrix
'naive_fill_NA' function for the simple and automatic imputationnaive_fill_NA naive_fill_NA.data.frame naive_fill_NA.data.table naive_fill_NA.matrix
Finding in random manner one of the k closets points in a certain vector for each value in a second vectorneibo
Class '"Rcpp_corrData"'corrData Rcpp_corrData-class
Class '"Rcpp_miceFast"'miceFast Rcpp_miceFast-class
upset plot for NA valuesupset_NA
'VIF' function for assessing VIF.VIF VIF.data.frame VIF.data.table VIF.matrix