BioMoR: Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble

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>).

Version: 0.1.0
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
Published: 2025-10-03
DOI: 10.32614/CRAN.package.BioMoR (may not be active yet)
Author: MD. Arshad [aut, cre]
Maintainer: MD. Arshad <arshad10867c at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: BioMoR results

Documentation:

Reference manual: BioMoR.html , BioMoR.pdf
Vignettes: BioMoR Autoencoder (source, R code)
BioMoR Benchmarking (source, R code)
Getting Started with BioMoR (source, R code)

Downloads:

Package source: BioMoR_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BioMoR to link to this page.