mixggm-package: Mixtures of Gaussian graphical models
In mixggm: Mixtures of Gaussian Graphical Models
Description
Details
Author(s)
References
Description
Mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices.
Details
A package implementing mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices. Model fitting is carried out by means of a structural-EM algorithm for parameter estimation and graph structure search.
The main functions are mixGGM, searchGGM and fitGGM.
Author(s)
Michael Fop, Luca Scrucca and Thomas Brendan Murphy.
Maintainer: Michael Fop michael.fop@ucd.ie
References
Fop, M., Murphy, T.B., and Scrucca, L. (2018). Model-based clustering with sparse covariance matrices. Statistics and Computing. To appear.
mixggm documentation built on May 2, 2019, 6:10 a.m.
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Description Details Author(s) References
Description
Mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices.
Details
A package implementing mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices. Model fitting is carried out by means of a structural-EM algorithm for parameter estimation and graph structure search.
The main functions are mixGGM, searchGGM and fitGGM.
Author(s)
Michael Fop, Luca Scrucca and Thomas Brendan Murphy.
Maintainer: Michael Fop michael.fop@ucd.ie
References
Fop, M., Murphy, T.B., and Scrucca, L. (2018). Model-based clustering with sparse covariance matrices. Statistics and Computing. To appear.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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