Package: SSLfmm
Type: Package
Title: Semi-Supervised Learning under a Mixed-Missingness Mechanism in
        Finite Mixture Models
Version: 0.1.0
Authors@R: c(person("Jinran", "Wu", email = "jinran.wu@uq.edu.au", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2388-3614")),person("Geoffrey J.", "McLachlan", email = "g.mclachlan@uq.edu.au", role = "aut", comment = c(ORCID = "0000-0002-5921-3145")))
Maintainer: Jinran Wu <jinran.wu@uq.edu.au>
Description: 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.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.2.0)
Imports: stats, mvtnorm, matrixStats
NeedsCompilation: no
Packaged: 2025-12-04 00:22:06 UTC; uqjwu15
Author: Jinran Wu [aut, cre] (ORCID: <https://orcid.org/0000-0002-2388-3614>),
  Geoffrey J. McLachlan [aut] (ORCID:
    <https://orcid.org/0000-0002-5921-3145>)
Repository: CRAN
Date/Publication: 2025-12-09 16:30:28 UTC
Built: R 4.6.0; ; 2026-04-27 03:53:34 UTC; unix
