# SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness

arXiv: https://arxiv.org/abs/2512.03322 · HTML: https://mlsanity.com/paper/2512.03322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [An Apparent Paradox: A Classifier Trained from a Partially Classified Sample May Have Smaller Expected Error Rate Than That If the Sample Were Completely Classified](https://mlsanity.com/paper/1910.09189/) (2019)
- [Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review](https://mlsanity.com/paper/2104.04046/) (2021)
- [Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism](https://mlsanity.com/paper/2302.07540/) (2023)
- [EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions](https://mlsanity.com/paper/1509.02069/) (2015)

## Abstract (arXiv)

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.