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ADAPT

Bioc current

Analysis of Microbiome Differential Abundance by Pooling Tobit Models

v1.6.0 · software · MIT + file LICENSE

Release Lineage

Entered 3.20 · Oct 30, 2024

Current · Requires R 4.6

1.0 In 4 of 49 releases 3.23

Description

ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones.

Line coverage

Expression

Tests / Examples

Functions

33 1 exported

Complexity

4 avg / 11 max

Call network

33 nodes / 10 edges

Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.

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Lowest coverage

Per-function coverage is not measured for this package yet.

Code

Structure

Lines of code

1,467

Files

38

Compiled share

33.3%

Has compiled src

Yes

Language breakdown

R 552 (37.6%)C/C++/src 488 (33.3%)Tests 39 (2.7%)Docs 221 (15.1%)Vignettes 167 (11.4%)

API

Exported functions

1

Internal functions

6

Recent export changes

v3.20+1 adapt

Testing & CI

Has tests

Yes

Test-to-code ratio

0.07

testthat edition

3

CI present

No

CI type

[]

PR gated

No

Docs

Return-value doc rate

100%

\dontrun example ratio

0%

Roxygen coverage

100%

Has pkgdown

No

NEWS present

Yes

Health & Security signals

Informational signals; not verdicts.

on.exit coverage

Unsafe pattern score

0

Dep constraint coverage

75%

Secret pattern count

0

Bundled 3rd-party code

2 items

Portability & License

Min R version

4.1.0

System requirements

C++ standard

License

MIT + file LICENSE

License flags

SPDX valid, OSI approved

History

Versions

4

First release

2024-10-29

Latest release

2026-04-28

Avg cadence

181 days

Cold removal rate

Dep drift

0

LOC over versions

v3.20: 1,467 LOCv3.21: 1,467 LOCv3.22: 1,467 LOCv3.23: 1,467 LOC

Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.

Topics

People