nannyrust: Rust Library for Dataset-Drift Detection and Label-Free Performance Estimation in Bioinformatics Machine-Learning Workflows

Machine-learning models used in bioinformatics are exposed to covariate shift from new sequencing platforms, batches and cohorts, while ground-truth labels arrive late or never. We present nannyrust, a pure-Rust library with no third-party dependencies that implements univariate drift statistics (Kolmogorov–Smirnov, Wasserstein, Jensen–Shannon, Hellinger, chi-squared, L-infinity), classification metrics with ROC AUC, and confidence-based performance estimation (CBPE), so that monitoring can run inside compiled pipelines. Against SciPy and scikit-learn the core statistics agree to within 5 × 10⁻¹³ on 700 test datasets.