gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.

Version: 1.1.5
Depends: R (≥ 3.1.0), mvtnorm, stats, methods
Published: 2023-10-16
Author: Ziyang Lyu [aut, cre], Daniel Ahfock [aut], Ryan Thompson [aut], Geoffrey J. McLachlan [aut]
Maintainer: Ziyang Lyu <ziyang.lyu at>
License: GPL-3
NeedsCompilation: no
CRAN checks: gmmsslm results


Reference manual: gmmsslm.pdf


Package source: gmmsslm_1.1.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): gmmsslm_1.1.5.tgz, r-oldrel (arm64): gmmsslm_1.1.5.tgz, r-release (x86_64): gmmsslm_1.1.5.tgz, r-oldrel (x86_64): gmmsslm_1.1.5.tgz
Old sources: gmmsslm archive


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