Package: gmmsslm
Type: Package
Title: Semi-Supervised Gaussian Mixture Model with a Missing-Data
        Mechanism
Version: 1.1.6
Authors@R: c(person("Ziyang Lyu", role = c("aut", "cre"), email = "ziyang.lyu@unsw.edu.au"),person("Daniel Ahfock", role = "aut"),person("Ryan Thompson", role = "aut"),person("Geoffrey J. McLachlan", role = "aut"))
Description: 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.
Depends: R (>= 3.1.0), mvtnorm,stats,methods
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2025-03-29 06:44:04 UTC; lyu
Author: Ziyang Lyu [aut, cre],
  Daniel Ahfock [aut],
  Ryan Thompson [aut],
  Geoffrey J. McLachlan [aut]
Maintainer: Ziyang Lyu <ziyang.lyu@unsw.edu.au>
Repository: CRAN
Date/Publication: 2025-04-17 04:20:02 UTC
Built: R 4.6.0; ; 2026-04-25 19:18:45 UTC; unix
