# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
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 with Mixed Missingness in Finite Mixture
  Models'
version: 0.2.2
doi: 10.32614/CRAN.package.SSLfmm
abstract: Semi-supervised Gaussian finite mixture models for partially labelled data
  under complete-case, missing completely at random (MCAR), entropy-dependent missing
  at random (MAR), and mixed MCAR/MAR label-missingness formulations. For the mixed
  formulation, the source of a missing label may be observed or latent. The package
  supports equal and component-specific covariance matrices, model fitting, simulation,
  initialization, prediction, classification performance assessment, and entropy-based
  diagnostics. A semi-synthetic Blood Transfusion data set is included to illustrate
  the applied workflow.
authors:
- family-names: McLachlan
  given-names: Geoffrey J.
  email: g.mclachlan@uq.edu.au
  orcid: https://orcid.org/0000-0002-5921-3145
- family-names: Wu
  given-names: Jinran
  email: jinran.wu@uq.edu.au
  orcid: https://orcid.org/0000-0002-2388-3614
repository: https://wujrtudou.r-universe.dev
repository-code: https://github.com/wujrtudou/SSLfmm
commit: 8fd2ce0804e2700e400fa4878b62aabe5d9c8f87
url: https://github.com/wujrtudou/SSLfmm
date-released: '2026-09-30'
contact:
- family-names: Wu
  given-names: Jinran
  email: jinran.wu@uq.edu.au
  orcid: https://orcid.org/0000-0002-2388-3614