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cff-version: 1.2.0
message: 'To cite package "MAI" in publications use:'
type: software
license: GPL-3.0-only
title: 'MAI: Mechanism-Aware Imputation'
version: 1.11.0
abstract: A two-step approach to imputing missing data in metabolomics. Step 1 uses
  a random forest classifier to classify missing values as either Missing Completely
  at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR
  are combined because it is often difficult to distinguish these two missing types
  in metabolomics data. Step 2 imputes the missing values based on the classified
  missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms
  tested and available for MCAR/MAR include Bayesian Principal Component Analysis
  (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random
  Forest. Imputation algorithms tested and available for MNAR include nsKNN and a
  single imputation approach for imputation of metabolites where left-censoring is
  present.
authors:
- family-names: Dekermanjian
  given-names: Jonathan
  email: Jonathan.Dekermanjian@CUAnschutz.edu
- family-names: Shaddox
  given-names: Elin
  email: Elin.Shaddox@CUAnschutz.edu
- family-names: Nandy
  given-names: Debmalya
  email: Debmalya.Nandy@CUAnschutz.edu
- family-names: Ghosh
  given-names: Debashis
  email: Debashis.Ghosh@CUAnschutz.edu
- family-names: Kechris
  given-names: Katerina
  email: Katerina.Kechris@CUAnschutz.edu
repository: https://bioc.r-universe.dev
repository-code: https://github.com/KechrisLab/MAI
url: https://github.com/KechrisLab/MAI
contact:
- family-names: Dekermanjian
  given-names: Jonathan
  email: Jonathan.Dekermanjian@CUAnschutz.edu