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: 1.1.6
Depends: R (≥ 3.1.0), mvtnorm, stats, methods
Published: 2025-04-17
DOI: 10.32614/CRAN.package.gmmsslm
Author: Ziyang Lyu [aut, cre], Daniel Ahfock [aut], Ryan Thompson [aut], Geoffrey J. McLachlan [aut]
Maintainer: Ziyang Lyu <ziyang.lyu at unsw.edu.au>
License: GPL-3
NeedsCompilation: no
CRAN checks: gmmsslm results

Documentation:

Reference manual: gmmsslm.html , gmmsslm.pdf

Downloads:

Package source: gmmsslm_1.1.6.tar.gz
Windows binaries: r-devel: gmmsslm_1.1.6.zip, r-release: gmmsslm_1.1.6.zip, r-oldrel: gmmsslm_1.1.6.zip
macOS binaries: r-release (arm64): gmmsslm_1.1.6.tgz, r-oldrel (arm64): gmmsslm_1.1.6.tgz, r-release (x86_64): gmmsslm_1.1.6.tgz, r-oldrel (x86_64): gmmsslm_1.1.6.tgz
Old sources: gmmsslm archive

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