Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models R package SSLfmm version 0.1.0 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. Software Depends: R (>= 4.2.0) Imports: stats, mvtnorm, matrixStats Jinran Wu <jinran.wu@uq.edu.au> Comprehensive R Archive Network (CRAN) Jinran Wu [aut, cre] (ORCID: <https://orcid.org/0000-0002-2388-3614>), Geoffrey J. McLachlan [aut] (ORCID: <https://orcid.org/0000-0002-5921-3145>) GPL-3 2025-12-09 application/tgz https://CRAN.R-project.org/package=SSLfmm doi:10.32614/CRAN.package.SSLfmm