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In multimix: Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm

multimix: Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm

Description

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.

Author(s)

Maintainer: James Curran [e-mail] [contributor]

Authors:

• Murray Jorgensen [e-mail]

See Also

Useful links:

• https://github.com/jmcurran/multimix

• Report bugs at https://github.com/jmcurran/multimix/issues

multimix documentation built on Jan. 22, 2023, 1:13 a.m.