sparsenetgls
Bioc currentUsing Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression
Release Lineage
Entered 3.8 · Oct 31, 2018
Current · Requires R 4.6
Description
The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
20 9 exported
Complexity
2.6 avg / 9 max
Call network
20 nodes / 16 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
1,680
Files
29
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
8
Internal functions
11
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.07
testthat edition
–
CI present
No
CI type
[]
PR gated
No
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
4.0.0
System requirements
1
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
16
First release
2019-01-04
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
1
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
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Irene Zeng author maintainer
- Thomas Lumley contributor