SSLfmm: Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models

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) <doi:10.1007/s11222-020-09971-5> and Ahfock and McLachlan (2023) <doi: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.

Version:	0.1.0
Depends:	R (≥ 4.2.0)
Imports:	stats, mvtnorm, matrixStats
Published:	2025-12-09
DOI:	10.32614/CRAN.package.SSLfmm
Author:	Jinran Wu [aut, cre], Geoffrey J. McLachlan [aut]
Maintainer:	Jinran Wu <jinran.wu at uq.edu.au>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	SSLfmm results

Documentation:

Reference manual:

SSLfmm.html , SSLfmm.pdf

Downloads:

Package source:	SSLfmm_0.1.0.tar.gz
Windows binaries:	r-devel: SSLfmm_0.1.0.zip, r-release: SSLfmm_0.1.0.zip, r-oldrel: SSLfmm_0.1.0.zip
macOS binaries:	r-release (arm64): SSLfmm_0.1.0.tgz, r-oldrel (arm64): SSLfmm_0.1.0.tgz, r-release (x86_64): SSLfmm_0.1.0.tgz, r-oldrel (x86_64): SSLfmm_0.1.0.tgz

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