DESCRIPTION

Package: MAI
Type: Package
Title: Mechanism-Aware Imputation
Version: 1.3.0
Authors@R: 
  c(person(given = "Jonathan",
           family = "Dekermanjian",
           role = c("aut", "cre"),
           email = "Jonathan.Dekermanjian@CUAnschutz.edu"),
    person(given = "Elin",
           family = "Shaddox",
           role = c("aut"),
           email = "Elin.Shaddox@CUAnschutz.edu"),
    person(given = "Debmalya",
           family = "Nandy",
           role = c("aut"),
           email = "Debmalya.Nandy@CUAnschutz.edu"),
   person(given = "Debashis",
           family = "Ghosh",
           role = c("aut"),
           email = "Debashis.Ghosh@CUAnschutz.edu"),
   person(given = "Katerina",
           family = "Kechris",
           role = c("aut"),
           email = "Katerina.Kechris@CUAnschutz.edu"))
Description: 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.
License: GPL-3
Encoding: UTF-8
Imports:
    caret,
    parallel,
    doParallel,
    foreach,
    e1071,
    future.apply,
    future,
    missForest,
    pcaMethods,
    tidyverse,
    stats,
    utils,
    methods,
    SummarizedExperiment,
    S4Vectors
biocViews:
    Software,
    Metabolomics,
    StatisticalMethod,
    Classification
Suggests: 
    knitr,
    rmarkdown,
    BiocStyle,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://github.com/KechrisLab/MAI
BugReports: https://github.com/KechrisLab/MAI/issues