MAI
Bioc currentMechanism-Aware Imputation
Release Lineage
Entered 3.14 · Oct 27, 2021
Current · Requires R 4.6
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.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
9 0 exported
Complexity
11.7 avg / 28 max
Call network
9 nodes / 8 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
1,370
Files
17
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
1
Internal functions
9
Testing
Has tests
Yes
Test-to-code ratio
0.02
testthat edition
3
Docs
Roxygen coverage
0%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
–
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
10
First release
2021-10-26
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 2
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- not tracked
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 50%
Repository practices
Checks run against github.com/kechrislab/mai on 2026-10-21.
No development-tooling practices detected in the upstream repository.
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| untargeted_LCMS_data | matrix | 300 × 100 | No other package |
Topics
People
- Jonathan Dekermanjian author maintainer
- Debashis Ghosh author
- Katerina Kechris author
- Debmalya Nandy author
- Elin Shaddox author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("MAI")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.