MultiModalR
Fast Bayesian Probability Estimation for Multimodal Categorical Data
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).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | MultiModalR_1.0.0.tar.gz |
120.4 KiB |
1.0.0 |
rolling linux/noble R-4.5 | MultiModalR_1.0.0.tar.gz |
123.0 KiB |
1.0.0 |
rolling source/ R- | MultiModalR_1.0.0.tar.gz |
28.2 KiB |
1.0.0 |
latest linux/jammy R-4.5 | MultiModalR_1.0.0.tar.gz |
120.4 KiB |
1.0.0 |
latest linux/noble R-4.5 | MultiModalR_1.0.0.tar.gz |
123.0 KiB |
1.0.0 |
latest source/ R- | MultiModalR_1.0.0.tar.gz |
28.2 KiB |
1.0.0 |
2026-04-23 source/ R- | MultiModalR_1.0.0.tar.gz |
0 B |