Optimal Initial Value for Gaussian Mixture Model // 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).