Collapsed Variational Inference for Dirichlet Process (DP) Mixture Model
Overview
Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.
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- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
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- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 19
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 6%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Package metadata
- First published
- 2026-01-12
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 80 KB
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