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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "SSLfmm" in publications use:'
type: software
license: GPL-3.0-only
title: 'SSLfmm: Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite
  Mixture Models'
version: 0.1.0
doi: 10.32614/CRAN.package.SSLfmm
abstract: Implements a semi-supervised learning framework for finite mixture models
  under a mixed-missingness mechanism. The approach models both missing completely
  at random (MCAR) and entropy-based missing at random (MAR) processes using a logistic–entropy
  formulation. Estimation is carried out via an Expectation–-Conditional Maximisation
  (ECM) algorithm with robust initialisation routines for stable convergence. The
  methodology relates to the statistical perspective and informative missingness behaviour
  discussed in Ahfock and McLachlan (2020) <https://doi.org/10.1007/s11222-020-09971-5>
  and Ahfock and McLachlan (2023) <https://doi.org/10.1016/j.ecosta.2022.03.007>.
  The package provides functions for data simulation, model estimation, prediction,
  and theoretical Bayes error evaluation for analysing partially labelled data under
  a mixed-missingness mechanism.
authors:
- family-names: Wu
  given-names: Jinran
  email: jinran.wu@uq.edu.au
  orcid: https://orcid.org/0000-0002-2388-3614
- family-names: McLachlan
  given-names: Geoffrey J.
  email: g.mclachlan@uq.edu.au
  orcid: https://orcid.org/0000-0002-5921-3145
repository: https://cran.r-universe.dev
commit: 37c3c6a407052c30b28a769833fc81e8382bc763
date-released: '2025-12-09'
contact:
- family-names: Wu
  given-names: Jinran
  email: jinran.wu@uq.edu.au
  orcid: https://orcid.org/0000-0002-2388-3614