benchdamic
Bioc currentBenchmark of differential abundance methods on microbiome data
Release Lineage
Entered 3.14 · Oct 27, 2021
Current · Requires R 4.6
Description
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
86 84 exported
Complexity
8.7 avg / 40 max
Call network
86 nodes / 61 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
19,259
Files
160
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
84
Internal functions
2
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.08
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
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
4.3.0
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
10
First release
2021-10-26
Latest release
2026-04-28
Avg cadence
201 days
Cold removal rate
100%
Dep drift
19
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 99%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Matteo Calgaro author maintainer
- Davide Risso author
- Chiara Romualdi author
- Nicola Vitulo author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("benchdamic")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.