sigFeature
Bioc currentsigFeature: Significant feature selection using SVM-RFE & t-statistic
Release Lineage
Entered 3.8 · Oct 31, 2018
Current · Requires R 4.6
Description
This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
9 0 exported
Complexity
2.2 avg / 6 max
Call network
9 nodes / 1 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
1,909
Files
38
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
7
Internal functions
1
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.01
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
–
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
3.5.0
System requirements
–
C++ standard
–
License
GPL (>= 2)
License flags
SPDX valid, OSI approved
History
Versions
16
First release
2018-10-30
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Topics
People
- Pijush Das Developer author maintainer
- Dr. Sucheta Tripathy User contributor
- Dr. Susanta Roychudhury User contributor
Cite
Cite this package
Run in R for the authors' preferred citation:
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Cite the R Observatory
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