fastseg
Bioc currentfastseg - a fast segmentation algorithm
Release Lineage
Entered 2.9 · Nov 1, 2011
Current · Requires R 4.6
Description
fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
7 3 exported
Complexity
25 avg / 64 max
Call network
7 nodes / 3 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
2,450
Files
21
Compiled share
26.7%
Has compiled src
Yes
Language breakdown
API
Exported functions
3
Internal functions
1
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
2.13
System requirements
–
C++ standard
–
License
LGPL (>= 2.0)
License flags
not SPDX, not OSI
History
Versions
30
First release
2012-03-12
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
5
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 5
- 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
- 40%
Repository practices
Checks run against github.com/alexg9010/fastseg on 2026-10-21.
No development-tooling practices detected in the upstream repository.
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| coriell | data.frame | 2,271 × 5 | 2 packages |
| fastsegData | vector | 14,253 elements | No other package |
Topics
Depended on by (1)
Bioconductor (1)
People
- Alexander Blume maintainer
- Guenter Klambauer author
- Sonali Kumari contributor
Cite
Cite this package
Run in R for the authors' entry:
citation("fastseg")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/nar/gks003.
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.