glmSparseNet
Bioc currentNetwork Centrality Metrics for Elastic-Net Regularized Models
Release Lineage
Entered 3.8 · Oct 31, 2018
Current · Requires R 4.6
Description
glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
57 29 exported
Complexity
3.9 avg / 13 max
Call network
57 nodes / 70 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
8,554
Files
109
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
29
Internal functions
28
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.65
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
4.3.0
System requirements
–
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
16
First release
2018-10-30
Latest release
2026-04-28
Avg cadence
179 days
Cold removal rate
100%
Dep drift
19
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 43 (2 internal)includes a package overview page
- Examples that run
- 91%31 run in a plain check (2 of them under a condition) · 3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 95%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 2%
- Example for Classification -- Breast Invasive Carcinoma HTML
- Breast survival dataset using network from STRING DB HTML
- Example for Survival Data -- Breast Invasive Carcinoma HTML
- Example for Survival Data -- Prostate Adenocarcinoma HTML
- Example for Survival Data -- Skin Melanoma HTML
- Separate 2 groups in Cox regression HTML
Repository practices
8 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/sysbiomed/glmsparsenet on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-03-20 · The repository has a site deploy workflow
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| string.network.700.cache | dgCMatrix | 11,033 × 11,033 | No other package |
Topics
Depended on by (1)
CRAN (1)
People
- André Veríssimo author maintainer
- Eunice Carrasquinha contributor
- Marta Lopes contributor
- Susana Vinga author
Cite
Cite this package
Run in R for the authors' entry:
citation("glmSparseNet")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 v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.