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
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: BioMoR_0.1.0.zip, r-release: BioMoR_0.1.0.zip, r-oldrel: BioMoR_0.1.0.zip
macOS binaries:	r-release (arm64): BioMoR_0.1.0.tgz, r-oldrel (arm64): BioMoR_0.1.0.tgz, r-release (x86_64): BioMoR_0.1.0.tgz, r-oldrel (x86_64): BioMoR_0.1.0.tgz

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