SSLfmm - Semi-Supervised Learning with Mixed Missingness in Finite Mixture Models

Semi-supervised Gaussian finite mixture models for partially labelled data under complete-case, missing completely at random (MCAR), entropy-dependent missing at random (MAR), and mixed MCAR/MAR label-missingness formulations. For the mixed formulation, the source of a missing label may be observed or latent. The package supports equal and component-specific covariance matrices, model fitting, simulation, initialization, prediction, classification performance assessment, and entropy-based diagnostics. A semi-synthetic Blood Transfusion data set is included to illustrate the applied workflow.