bioc-issue-bot
commented
3 years ago Hi @Dekermanjian
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: MAI
Type: Package
Title: Mechanism-Aware Imputation
Version: 0.99.0
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.
Where step 1 uses a random forest classifier to classify missing values as
either MCAR/MAR or MNAR. Step 2 imputes the missing values based on the
classified missing mechanisms, using the appropriate imputation algorithms.
MCAR/MAR tested and available imputation algorithms include Bayesian
Principle Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest
Neighbors (Multi_nsKNN), and random forest. The imputation algorihms that
are available and tested for MNAR values include nsKNN and a single
imputation approach for linear regression with a randomly right-censored
covariate, we developed a method for imputation of metabolites where
left-censoring is present.
License: GPL-3
Encoding: UTF-8
Imports:
caret,
parallel,
doParallel,
e1071,
future.apply,
future,
missForest,
pcaMethods,
tidyverse,
stats,
utils
biocViews:
Software,
Metabolomics,
StatisticalMethod,
Classification
Suggests:
knitr,
rmarkdown,
testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
LiNk-NY
Dekermanjian
vjcitn
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