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.

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