nnNorm
Bioc currentSpatial and intensity based normalization of cDNA microarray data based on robust neural nets
Release Lineage
Entered 1.5 · Oct 25, 2004
Current · Requires R 4.6
Description
This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
3 3 exported
Complexity
16.7 avg / 31 max
Call network
3 nodes / 0 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
667
Files
13
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
3
Internal functions
0
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
2.2.0
System requirements
–
C++ standard
–
License
LGPL
License flags
not SPDX, not OSI
History
Versions
44
First release
2004-11-03
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
4
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 3
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
- nnNorm Tutorial PDF
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
People (from the free-text Author field)
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("nnNorm")Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
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