BioMoR
0.1.1Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble
Overview
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).
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-2511 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- not tracked
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 1.5 median / 3 max
- Test cases
- 5 / 0.25 per code line
Test coverage
Line coverage
53%
Expression
47.8%
Tests / Examples
52.8% / – ex
Functions
12 9 exported
Complexity
1.6 avg / 3 max
Call network
12 nodes / 4 edges
Call graph
Open call graph →Lowest coverage
12 functions| Function | Cyclo | Coverage |
|---|---|---|
| get_embeddings exp | 1 | 0% |
| prepare_model_data exp | 1 | 0% |
| train_autoencoder exp | 1 | 0% |
| biomor_run_pipeline | 2 | 0% |
| calibrate_model | 3 | 0% |
| train_biomor | 1 | 0% |
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- 0.1.02025-10-03
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-10-03
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.2.0
- Download size
- 17 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet