Package: mlim
Type: Package
Title: Single and Multiple Imputation with Automated Machine Learning
Version: 0.6.0
Authors@R: 
    person("E. F. Haghish",
           role = c("aut", "cre", "cph"),
           email = "haghish@hotmail.com")
Depends: R (>= 3.5.0)
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: mice, missRanger, memuse, mlr3, mlr3pipelines, mlr3tuning,
        paradox, md.log (>= 0.2.0), readstata13 (>= 0.11.0)
Suggests: mlr3learners
LazyData: true
URL: https://github.com/haghish/mlim
BugReports: https://github.com/haghish/mlim/issues
Config/roxygen2/version: 8.1.0
Config/pak/sysreqs: cmake make libicu-dev libx11-dev zlib1g-dev
Repository: https://haghish.r-universe.dev
Date/Publication: 2026-10-02 17:54:10 UTC
RemoteUrl: https://github.com/haghish/mlim
RemoteRef: HEAD
RemoteSha: 304a6d46b2de16835a26dd534e5b5a7f5da06aa7
NeedsCompilation: no
Packaged: 2026-10-02 18:41:27 UTC; root
Author: E. F. Haghish [aut, cre, cph]
Maintainer: E. F. Haghish <haghish@hotmail.com>
