{ "_id": "6a106252acfb0bcc41ca7f7f", "Package": "SSLfmm", "Type": "Package", "Title": "Semi-Supervised Learning under a Mixed-Missingness Mechanism in\nFinite 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 ", "Description": "Implements a semi-supervised learning framework for finite\nmixture models under a mixed-missingness mechanism. The\napproach models both missing completely at random (MCAR) and\nentropy-based missing at random (MAR) processes using a\nlogistic–entropy formulation. Estimation is carried out via an\nExpectation–-Conditional Maximisation (ECM) algorithm with\nrobust initialisation routines for stable convergence. The\nmethodology relates to the statistical perspective and\ninformative missingness behaviour discussed in Ahfock and\nMcLachlan (2020) and Ahfock\nand McLachlan (2023) . The\npackage provides functions for data simulation, model\nestimation, prediction, and theoretical Bayes error evaluation\nfor analysing partially labelled data under a mixed-missingness\nmechanism.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-08 07:42:32 UTC", "User": "root" }, "Author": "Jinran Wu [aut, cre] (ORCID:\n), Geoffrey J. 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