R Observatory: Metrics for mlim version 0.3.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

mlim

0.3.0

Single and Multiple Imputation with Automated Machine Learning

0packages depend

2.8Kdownloads / year

–test coverage

0/13checks pass

Overview

About

Maintained by E. F. HaghishFirst published 2022-08-135 releasesCRAN page ↗GitHub ↗

Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)

install.packages("mlim", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("haghish/mlim")

Development version from haghish/mlim. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-mlim

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-mlim

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

Health

CRAN checks

13NOTE

Failing flavors

NOTE r-devel-linux-x86_64-debian-clang
NOTE r-devel-linux-x86_64-debian-gcc
NOTE r-devel-linux-x86_64-fedora-clang
NOTE r-devel-linux-x86_64-fedora-gcc
NOTE r-devel-windows-x86_64
NOTE r-oldrel-macos-arm64
NOTE r-oldrel-macos-x86_64
NOTE r-oldrel-windows-x86_64
NOTE r-patched-linux-x86_64
NOTE r-release-linux-x86_64
NOTE r-release-macos-arm64
NOTE r-release-macos-x86_64
NOTE r-release-windows-x86_64

Slowest check: 2.7 min · r-oldrel-windows-x86_64

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

6

Dependencies · direct

Check history

NOTE2026-06-09
0 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-06-08
0 OK · 12 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
NOTE2026-05-02
0 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-04-25
0 OK · 11 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
NOTE2026-03-10
0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 2,306 wordsVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 0%
Documented parameters: 100%
Return-value docs: 100%
References docs: 13%

Downloads

2.8K

CRAN downloads in the past year

Rank #17,126 · ~8/day · ~231/mo

Releases R versions

Daily download trend is not available in this view yet.

12330 days

62790 days

2.8K1 year

Compare downloads with other packages →

Also on99 r2u

Repository

GitHub ↗ haghish/mlim

33Stars

0Forks

0Open issues

0Open PRs

20Releases

392Commits

2Contributors

automatic-machine-learningautomldata-scienceelastic-netextreme-gradient-boostinggbmglmgradient-boosting

392 commits · Last activity 2026-02-22 · 0% stars, 30d

Stars over time

2025-08-06 · 322026-07-07 · 33

Repository practices

Upstream repositoryBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/haghish/mlim on 2026-08-16.

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

6 external dependencies (excludes base and recommended)

Depends (1)

R >= 3.5.0

Imports (6)

h2o curl mice missRanger memuse md.log

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)

Maintainer (1)

E. F. Haghish

Author, Maintainer, Copyright holder

Authors (1)

E. F. Haghish

Author, Maintainer, Copyright holder

Copyright holders (1)

E. F. Haghish

Author, Maintainer, Copyright holder

Package Timeline

5 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
0.3.0Latest
2022-12-16 · current release · diff ↗
unarchivedReturned to CRAN
2022-12-16
archivedRemoved from CRAN
2022-11-26
issues were not corrected in time
0.2.0
2022-09-26 · diff ↗
0.0.9
2022-09-07 · diff ↗
unarchivedReturned to CRAN
2022-09-07
archivedRemoved from CRAN
2022-09-04
issues were not corrected in time
0.0.2
2022-08-15 · diff ↗
0.0.1
2022-08-13
R
R 4.2.0 released · 2022-04-22

Package metadata

First published: 2022-08-13
Total releases: 5 / 4 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 3.5.0
Bundled data: 73 KB / 2 files
Download size: 899 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("mlim")

Format

E. F. Haghish. (2022). mlim: Single and Multiple Imputation with Automated Machine Learning (Version 0.3.0) [Computer software]. https://doi.org/10.32614/CRAN.package.mlim

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for mlim version 0.3.0 [Data set]. HJJB, LLC. Data release v2026-08-16. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-16, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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