Tools for bioinformatics modeling using recursive transformer-inspired 
    architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. 
    Includes utilities for cross-validation, calibration, benchmarking, and threshold 
    optimization in predictive modeling workflows. The methodology builds on ensemble 
    learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and 
    Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 
    2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches 
    such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.

Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.

Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event.  A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages.
    This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.

Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially 
    microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) 
    <doi:10.21037/jlpm.2019.04.05>).