GMMinit
1.0.0Optimal Initial Value for Gaussian Mixture Model
Overview
Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) doi:10.1007/s11634-016-0264-8 and Biernacki et al. (2003) doi:10.1016/S0167-9473(02)00163-9, and on the EM algorithm of Dempster et al. (1977) doi:10.1111/j.2517-6161.1977.tb01600.x. Background on model-based clustering includes Fraley and Raftery (2002) doi:10.1198/016214502760047131 and McLachlan and Peel (2000, ISBN:9780471006268).
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
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 17%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-24
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 2) OSI
- Download size
- 13 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("GMMinit")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.