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multimix  

Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("multimix", "1.0-10")



Attach the package and use:
library("multimix")
Maintained by
James Curran
[Scholar Profile | Author Map]
First Published: 2023-01-18
Latest Update: 2023-01-18
Description:
A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 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) Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440.
How to cite:
James Curran (2023). multimix: Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm. R package version 1.0-10, https://cran.r-project.org/web/packages/multimix. Accessed 07 May. 2025.
Previous versions and publish date:
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Complete documentation for multimix
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