DImodelsVis
1.0.5Visualising and Interpreting Statistical Models Fit to Compositional Data
Overview
Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.
Install
Health
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 3%
Downloads
Repository
Releases over time
PRs over time
Repository practices
5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/rishvish/dimodelsvis on 2026-07-19.
Dependencies
Code & Tests
- Cyclomatic complexity
- 6.0 median / 47 max
- Test cases
- 34 / 3.51 per code line
Test coverage
Line coverage
99%
Expression
98.6%
Tests / Examples
99.1% / 65% ex
Functions
77 39 exported
Complexity
8.8 avg / 47 max
Call network
77 nodes / 154 edges
Call graph
Open call graph →Lowest coverage
77 functions| Function | Cyclo | Coverage |
|---|---|---|
| simplex_path exp | 10 | 87% |
| check_conditional_parameter | 11 | 93% |
| prediction_contributions exp | 16 | 94% |
| add_exp_str | 25 | 95% |
| gradient_change_plot_internal | 17 | 96% |
| conditional_ternary exp | 8 | 97% |
People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2024-02-19
- Total releases
- 6 / 2 yrs
- License
- GPL (>= 3) OSI
- Download size
- 2.7 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet