This package is considered a duplicate. The official version of this package is found at:https://polkas.r-universe.dev/miceFast

Package: miceFast 0.8.5

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_0.8.5.tar.gz
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miceFast_0.8.5.tgz(r-4.4-emscripten)miceFast_0.8.5.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'))

Bug tracker:https://github.com/polkas/micefast/issues2 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

On CRAN:

openblas cpp openmp

2.70 score 682 downloads 9 exports 3 dependencies

Last updated 2 months agofrom:f57885a1b2. Checks:3 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 06 2025
R-4.5-linux-x86_64	OK	Mar 06 2025
R-4.4-linux-x86_64	OK	Mar 06 2025

Exports:compare_imp corrData fill_NA fill_NA_N miceFast naive_fill_NA neibo upset_NA VIF

Dependencies:data.table Rcpp RcppArmadillo

miceFast - Introduction

Maciej Nasinski

Rendered frommiceFast-intro.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2025-02-03
Started: 2018-03-19

Citation

To cite package ‘miceFast’ in publications use:

Nasinski M (2025). miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'. R package version 0.8.5, https://CRAN.R-project.org/package=miceFast.

Corresponding BibTeX entry:

  @Manual{,
    title = {miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'},
    author = {Maciej Nasinski},
    year = {2025},
    note = {R package version 0.8.5},
    url = {https://CRAN.R-project.org/package=miceFast},
  }

Readme and manuals

miceFast

Author: Maciej Nasinski

Check the miceFast website for more details

Overview

miceFast provides fast methods for imputing missing data, leveraging an object-oriented programming paradigm and optimized linear algebra routines.
The package includes convenient helper functions compatible with data.table, dplyr, and other popular R packages.

Major speed improvements occur when:

Using a grouping variable, where the data is automatically sorted by group, significantly reducing computation time.
Performing multiple imputations, by evaluating the underlying quantitative model only once for multiple draws.
Running Predictive Mean Matching (PMM), thanks to presorting and binary search.

For performance details, see performance_validity.R in the extdata folder.

Installation

You can install miceFast from CRAN:

install.packages("miceFast")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("polkas/miceFast")

Quick Example

Below is a short demonstration. See the vignette for advanced usage and best practices.

library(miceFast)

set.seed(1234)
data(air_miss)

# Visualize the NA structure
upset_NA(air_miss, 6)

# Simple and naive fill
imputed_data <- naive_fill_NA(air_miss)

# Compare with other packages:
# Hmisc
library(Hmisc)
data.frame(Map(function(x) Hmisc::impute(x, "random"), air_miss))

# mice
library(mice)
mice::complete(mice::mice(air_miss, printFlag = FALSE))

Key Features

Object-Oriented Interface via miceFast objects (Rcpp modules).
Convenient Helpers:
- fill_NA(): Single imputation (lda, lm_pred, lm_bayes, lm_noise).
- fill_NA_N(): Multiple imputations (pmm, lm_bayes, lm_noise).
- VIF(): Variance Inflation Factor calculations.
- naive_fill_NA(): Automatic naive imputations.
- compare_imp(): Compare original vs. imputed values.
- upset_NA(): Visualize NA structure using UpSetR.

Quick Reference Table:

Function	Description
`new(miceFast)`	Creates an OOP instance with numerous imputation methods (see the vignette).
`fill_NA()`	Single imputation: `lda`, `lm_pred`, `lm_bayes`, `lm_noise`.
`fill_NA_N()`	Multiple imputations (N repeats): `pmm`, `lm_bayes`, `lm_noise`.
`VIF()`	Computes Variance Inflation Factors.
`naive_fill_NA()`	Performs automatic, naive imputations.
`compare_imp()`	Compares imputations vs. original data.
`upset_NA()`	Visualizes NA structure using an UpSet plot.

Performance Highlights

Benchmark testing (on R 4.2, macOS M1) shows miceFast can significantly reduce computation time, especially in these scenarios:

Linear Discriminant Analysis (LDA): ~5x faster.
Grouping Variable Imputations: ~10x faster (and can exceed 100x in some edge cases).
Multiple Imputations: ~x * (number of multiple imputations) faster, since the model is computed only once.
Variance Inflation Factors (VIF): ~5x faster, because we only compute the inverse of X'X.
Predictive Mean Matching (PMM): ~3x faster, thanks to presorting and binary search.