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miceFast

Maciej Nasinski

Check the miceFast website for more details

R build status CRAN codecov Dependencies

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.

Performance benchmarks (check performance_validity.R file at extdata).

Advanced Usage - Vignette

Installation

install.packages('miceFast')

or

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

Recommended to download boosted BLAS library, even x100 faster:

cd /Library/Frameworks/R.framework/Resources/lib
ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib

Quick Implementation

library(miceFast)

set.seed(1234)
data(air_miss)

# plot NA structure
upset_NA(air_miss, 6)

naive_fill_NA(air_miss)

# Check out the vignette for an advance usage
# There is required a thorough examination

# Other packages - popular simple solutions
# Hmisc
data.frame(Map(function(x) Hmisc::impute(x, 'random'), air_miss))

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

Quick Reference Table

Function Description
new(miceFast) OOP instance with bunch of methods - check out vignette
fill_NA() imputation - lda,lm_pred,lm_bayes,lm_noise
fill_NA_N() multiple imputation - pmm,lm_bayes,lm_noise
VIF() Variance inflation factor
naive_fill_NA() auto imputations
compare_imp() comparing imputations
upset_NA() visualize NA structure - UpSetR::upset

Summing up, miceFast offer a relevant reduction of a calculations time for:

Environment: R 4.2.1 Mac M1

If you are interested about the procedure of testing performance and validity check performance_validity.R file at the extdata folder.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.