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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "gmmsslm" in publications use:'
type: software
license: GPL-3.0-only
title: 'gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism'
version: 1.1.5
doi: 10.32614/CRAN.package.gmmsslm
abstract: 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) <https://doi.org/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.
authors:
- name: Ziyang Lyu
  email: ziyang.lyu@unsw.edu.au
- name: Daniel Ahfock
- name: Ryan Thompson
- name: Geoffrey J. McLachlan
repository: https://lyu9118.r-universe.dev
commit: 41a58bb54af90fd74e4c19873dc6e99f4f22bb03
date-released: '2023-10-16'
contact:
- name: Ziyang Lyu
  email: ziyang.lyu@unsw.edu.au