Optimal Initial Value for Gaussian Mixture Model R package GMMinit version 1.0.0 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). Software Imports: mvtnorm, mclust, mvnfast, stats Jing Li <jli178@crimson.ua.edu> Comprehensive R Archive Network (CRAN) Jing Li [aut, cre], Yana Melnykov [aut] GPL (>= 2) 2026-01-24 application/tgz https://CRAN.R-project.org/package=GMMinit doi:10.32614/CRAN.package.GMMinit