MultiModalR
1.0.0Fast Bayesian Probability Estimation for Multimodal Categorical Data
Overview
Fast Bayesian probability estimation for multimodal categorical data using speed-optimized Markov chain Monte Carlo (MCMC) implementation (Metropolis-Hastings-within-partial-Gibbs). The package provides efficient algorithms for detecting subpopulations, estimating mixture components, and assigning observations to subgroups with probability estimates. The methods are described in Dioszegi, G. et al. (2026) "Automatic Bayesian Mixture Modeling for Multimodal Categorical Data via Integrated Mode Detection and Metropolis-Hastings-within-Gibbs Sampling" (submitted to Journal of Statistical Software).
Install
Health
- OK2026-07-016 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 6.0 median / 17 max
- Documented parameters
- 100%
- System requirements
- 1 external
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
17 9 exported
Complexity
6.4 avg / 17 max
Call network
17 nodes / 8 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
1 release. R releases are shown for context.
- 1.0.0Latest2026-07-01 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-01
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 5.4 KB / 2 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet