MCbiclust
Bioc currentMassive correlating biclusters for gene expression data and associated methods
Release Lineage
Entered 3.5 · Apr 25, 2017
Current · Requires R 4.6
Description
Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
19 14 exported
Complexity
3 avg / 10 max
Call network
19 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
2,599
Files
62
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
14
Internal functions
5
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.15
testthat edition
–
CI present
Yes
CI type
["travis"]
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
3.4
System requirements
–
C++ standard
–
License
GPL-2
License flags
SPDX valid, OSI approved
History
Versions
19
First release
2017-05-22
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
1
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
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
Robert Bentham