epigraHMM
Bioc currentEpigenomic R-based analysis with hidden Markov models
Release Lineage
Entered 3.13 · May 20, 2021
Current · Requires R 4.6
Description
epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
110 18 exported
Complexity
3 avg / 31 max
Call network
110 nodes / 121 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
6,447
Files
88
Compiled share
12.6%
Has compiled src
Yes
Language breakdown
API
Exported functions
18
Internal functions
64
Testing
Has tests
Yes
Test-to-code ratio
0.20
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
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
1 externalGNU make
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
11
First release
2021-10-05
Latest release
2026-05-13
Avg cadence
186 days
Cold removal rate
–
Dep drift
3
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 19
- Examples that run
- 100%19 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 99%
- Return-value docs
- 83%
- Help pages citing references
- 84%
Repository practices
Checks read the Bioconductor git repository (git.bioconductor.org), branch RELEASE_3_23.
No development-tooling practices detected in the Bioconductor git repository.
Datasets
Topics
People
- Pedro Baldoni author maintainer
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("epigraHMM")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 v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.