# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "mlim" in publications use:'
type: software
license: MIT
title: 'mlim: Single and Multiple Imputation with Automated Machine Learning'
version: 0.4.0
doi: 10.32614/CRAN.package.mlim
abstract: 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.
authors:
- name: E. F. Haghish
  email: haghish@hotmail.com
repository: https://haghish.r-universe.dev
repository-code: https://github.com/haghish/mlim
commit: 1fb42ccd3ed52b77fa62b7e0b671f1f63bc1efc8
url: https://www.sv.uio.no/psi/english/people/academic/haghish/
contact:
- name: E. F. Haghish
  email: haghish@hotmail.com