dar
Bioc currentDifferential Abundance Analysis by Consensus
Release Lineage
Entered 3.19 · May 1, 2024
Current · Requires R 4.6
Description
Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
114 42 exported
Complexity
1.9 avg / 10 max
Call network
114 nodes / 250 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
10,156
Files
174
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
42
Internal functions
72
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.15
testthat edition
3
CI present
Yes
CI type
["github-actions"]
PR gated
Yes
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
9
Dep constraint coverage
4.5%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.5.0
System requirements
–
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
5
First release
2024-04-30
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
100%
Dep drift
2
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
- 98%
- References docs
- 0%
Topics
People
- Francesc Catala-Moll author maintainer
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("dar")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 v2026-08-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.