Package: BioMoR
Type: Package
Title: Bioinformatics Modeling with Recursion and Autoencoder-Based
        Ensemble
Version: 0.1.1
Authors@R: 
    person(given = "MD.",
           family = "Arshad",
           email = "arshad10867c@gmail.com",
           role = c("aut", "cre"))
Author: MD. Arshad [aut, cre]
Maintainer: MD. Arshad <arshad10867c@gmail.com>
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>).
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
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
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-12-10 23:12:20 UTC; sulky
Repository: CRAN
Date/Publication: 2025-12-10 23:50:02 UTC
Built: R 4.6.0; ; 2026-04-30 08:05:12 UTC; unix
