graper
Bioc currentAdaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes
Release Lineage
Entered 3.9 · May 3, 2019
Current · Requires R 4.6
Description
This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
123 7 exported
Complexity
3.3 avg / 8 max
Call network
123 nodes / 133 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
4,086
Files
39
Compiled share
55.9%
Has compiled src
Yes
Language breakdown
API
Exported functions
7
Internal functions
21
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.06
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
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
System requirements
–
C++ standard
–
License
GPL (>= 2)
License flags
SPDX valid, OSI approved
History
Versions
15
First release
2019-05-02
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.
Documentation
- Examples that run
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Britta Velten author maintainer
- Wolfgang Huber author