SSLfmm: Semi-Supervised Learning with Mixed Missingness in Finite Mixture Models

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.

Version:	0.2.1
Depends:	R (≥ 3.6.0)
Imports:	graphics, stats
Suggests:	testthat (≥ 3.0.0)
Published:	2026-09-08
DOI:	10.32614/CRAN.package.SSLfmm
Author:	Geoffrey J. McLachlan [aut], Jinran Wu [aut, cre]
Maintainer:	Jinran Wu <jinran.wu at uq.edu.au>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	SSLfmm results

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