Semi-Supervised Learning under a Mixed-Missingness Mechanism in
Finite Mixture Models
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) and
Ahfock and McLachlan (2023) . 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.