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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | SSLfmm_0.1.0.tar.gz |
108.4 KiB |
0.1.0 |
rolling linux/noble R-4.5 | SSLfmm_0.1.0.tar.gz |
108.2 KiB |
0.1.0 |
rolling source/ R- | SSLfmm_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
latest linux/jammy R-4.5 | SSLfmm_0.1.0.tar.gz |
108.4 KiB |
0.1.0 |
latest linux/noble R-4.5 | SSLfmm_0.1.0.tar.gz |
108.2 KiB |
0.1.0 |
latest source/ R- | SSLfmm_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
2026-04-26 source/ R- | SSLfmm_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
2026-04-23 source/ R- | SSLfmm_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | SSLfmm_0.1.0.zip |
110.9 KiB |