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README

Author: Maciej Nasinski

Check the miceFast website for more details

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.

It is recommended to read the Advanced Usage Vignette.

You can install miceFast from CRAN:

install.packages("miceFast")

Or install the development version from GitHub:

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

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))

• 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:

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.

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

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