DPCD
0.0.1Dirichlet Process Clustering with Dissimilarities
Overview
A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 78%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 36%
Downloads
Repository
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Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.0.1Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-12-19
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 2.0 MB / 2 files
- Download size
- 2.0 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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Run in R for the authors' preferred citation:
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