multimix: Fit Mixture Models Using the Expectation Maximisation (EM)
Algorithm
A set of functions which use the Expectation Maximisation (EM)
algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977)
<doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from
incomplete data via the EM algorithm, Journal of the Royal Statistical
Society, 39(1), 1–22) to take a finite mixture model approach to
clustering. The package is designed to cluster multivariate data that have
categorical and continuous variables and that possibly contain missing
values. The method is described in Hunt, L. and Jorgensen, M. (1999)
<doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics
41(2), 153–171 and Hunt, L. and Jorgensen, M. (2003)
<doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data
with missing information, Computational Statistics & Data Analysis, 41(3-4),
429–440.
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