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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




# install.packages("devtools")

Recommended to download boosted BLAS library, even x100 faster:

  • Windows Users recommended to download MRO MKL:
  • Linux users recommended to download Optimized BLAS (linear algebra) library: sudo apt-get install libopenblas-dev
  • Apple vecLib BLAS:
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



# plot NA structure
upset_NA(air_miss, 6)


# 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::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:

  • Linear Discriminant Analysis around (x5)
  • where a grouping variable have to be used (around x10 depending on data dimensions and number of groups and even more than x100 although compared to data.table only a few k faster or even the same) because of pre-sorting by grouping variable
  • multiple imputations is faster around x(a number of multiple imputations) because the core of a model is evaluated only ones.
  • Variance inflation factors (VIF) (x5) because the unnecessary linear regression is not evaluated - we need only inverse of X'X
  • Predictive mean matching (PMM) (x3) because of pre-sorting and binary search (mice algorithm was improved too).

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.