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BioMoR  

Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble
View on CRAN: Click here


Download and install BioMoR package within the R console
Install from CRAN:
install.packages("BioMoR")

Install from Github:
library("remotes")
install_github("cran/BioMoR")

Install by package version:
library("remotes")
install_version("BioMoR", "0.1.1")



Attach the package and use:
library("BioMoR")
Maintained by
MD. Arshad
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-03
Latest Update: 2025-10-03
Description:
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>).
How to cite:
MD. Arshad (2025). BioMoR: Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble. R package version 0.1.1, https://cran.r-project.org/web/packages/BioMoR. Accessed 26 Oct. 2026.
Previous versions and publish date:
0.1.0 (2025-10-03 15:50), (2026-07-09 07:59)
Other packages that cited BioMoR R package
View BioMoR citation profile
Other R packages that BioMoR depends, imports, suggests or enhances
Complete documentation for BioMoR
Functions, R codes and Examples using the BioMoR R package
Full BioMoR package functions and examples
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