The 'epilogi' Variable Selection Algorithm for Continuous Data
Overview
The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. doi:10.1093/bioinformatics/btad545.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 3includes a package overview page
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 67%
Downloads
Dependencies
Installing epilogi adds 5 packages (5 with compiled code, 3.9 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, RcppParallel 744 KB.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.2Latest
- 1.12024-09-10 · diff ↗
- RR 4.4.0 released · 2024-04-24
- 1.02023-10-01
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-10-01
- Total releases
- 3 / 3 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0
- Download size
- 3.9 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("epilogi")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.