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MoEClust  

Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("MoEClust", "1.5.2")



Attach the package and use:
library("MoEClust")
Maintained by
Keefe Murphy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-28
Latest Update: 2022-12-20
Description:
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) . This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
How to cite:
Keefe Murphy (2017). MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component. R package version 1.5.2, https://cran.r-project.org/web/packages/MoEClust. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0.0 (2017-11-28 19:24), 1.1.0 (2018-02-06 15:41), 1.2.0 (2018-08-24 16:34), 1.2.1 (2018-12-11 13:20), 1.2.2 (2019-05-15 15:30), 1.2.3 (2019-07-31 15:05), 1.2.4 (2019-12-11 06:50), 1.3.0 (2020-03-31 00:52), 1.3.1 (2020-05-12 19:00), 1.3.2 (2020-11-18 00:40), 1.3.3 (2021-01-06 17:10), 1.4.0 (2021-06-22 06:00), 1.4.1 (2021-10-12 14:20), 1.4.2 (2021-12-20 00:32), 1.5.0 (2022-03-28 16:50), 1.5.1 (2022-12-20 14:30)
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