Maciej Nasinski pkgdown:

Check the R CRAN for more details

R build status CRAN codecov

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:

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 vigniette for an advance usage
#there is required a thorough examination

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

mice::complete(mice::mice(air_miss, printFlag = F))

Quick Reference Table

| Function | Description | |----------------------|----------------------| | new(miceFast) | OOP instance with bunch of methods - check vigniette | | 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:

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

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miceFast documentation built on July 11, 2021, 1:06 a.m.