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