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GMMinit  

Optimal Initial Value for Gaussian Mixture Model
View on CRAN: Click here


Download and install GMMinit package within the R console
Install from CRAN:
install.packages("GMMinit")

Install from Github:
library("remotes")
install_github("cran/GMMinit")

Install by package version:
library("remotes")
install_version("GMMinit", "1.0.0")



Attach the package and use:
library("GMMinit")
Maintained by
Jing Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-24
Latest Update: 2026-01-24
Description:
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).
How to cite:
Jing Li (2026). GMMinit: Optimal Initial Value for Gaussian Mixture Model. R package version 1.0.0, https://cran.r-project.org/web/packages/GMMinit. Accessed 04 Oct. 2026.
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