Partially labelled samples arise when features are observed for data, but class labels are available for only a subset. In such settings, the mechanism governing label availability may itself contain information relevant to classification, yet it is typically left unmodelled in standard semi-supervised learning procedures. The SSLfmm package implements likelihood-based Gaussian finite-mixture classification in which the label-missingness process is modelled jointly with the class distribution. It supports complete-case, missing completely at random (MCAR), entropy-based missing at random (MAR), and mixed analyses in which MCAR and MAR mechanisms may both contribute. For the mixed mechanism, the source of a missing label may be known or unknown. A common R interface is provided for model fitting, prediction, performance assessment, and simulation. We describe the statistical formulation and software implementation and demonstrate its use through reproducible simulation and a semi-synthetic Blood Transfusion application.

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