missMethyl
Bioc currentAnalysing Illumina HumanMethylation BeadChip Data
Release Lineage
Entered 3.0 · Oct 14, 2014
Current · Requires R 4.6
Description
Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
34 18 exported
Complexity
4.6 avg / 20 max
Call network
34 nodes / 20 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
6,791
Files
36
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
18
Internal functions
16
Recent export changes
Testing
Has tests
No
Test-to-code ratio
0.00
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
3.6.0
System requirements
–
C++ standard
–
License
GPL-2
License flags
SPDX valid, OSI approved
History
Versions
24
First release
2014-10-13
Latest release
2026-04-28
Avg cadence
183 days
Cold removal rate
100%
Dep drift
21
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 19includes a package overview page
- Examples that run
- 67%12 run in a plain check · 6 never run
- Exported functions with examples
- 100%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 63%
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.
Topics
Depended on by (5)
Bioconductor (5)
Suggested by (1)
People (from the free-text Author field)
Cite
Cite this package
Run in R for the authors' entry:
citation("missMethyl")The package provides a citation file, so citation() returns the authors' own entry.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.