Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble R package BioMoR version 0.1.1 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>). Software Depends: R (>= 4.2.0) Imports: caret, recipes, themis, xgboost, magrittr, dplyr, pROC Suggests: randomForest, testthat (>= 3.0.0), PRROC, ggplot2, purrr, tibble, yardstick, knitr, rmarkdown MD. Arshad <arshad10867c@gmail.com> Comprehensive R Archive Network (CRAN) MD. Arshad [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=BioMoR/LICENSE) 2025-12-10 application/tgz https://CRAN.R-project.org/package=BioMoR doi:10.32614/CRAN.package.BioMoR