Benchmarking Genomic Selection and Machine-Learning Prediction Models
Overview
A unified interface to fit, cross-validate and benchmark genomic prediction models from SNP marker data. It implements genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP in base R, and offers a common interface to machine-learning predictors (elastic net, random forest and gradient boosting) through optional packages, together with a stacked ensemble. Cross-validation uses breeding-relevant schemes and reports prediction accuracy honestly, so models can be compared fairly. The genomic relationship matrix follows VanRaden (2008) doi:10.3168/jds.2007-0980; the mixed-model solver follows Endelman (2011) doi:10.3835/plantgenome2011.08.0024; the genomic-selection framework follows Meuwissen, Hayes and Goddard (2001) doi:10.1093/genetics/157.4.1819.
First release.
- Core (base R):
simulate_population(),qc_markers(),impute_markers(),Gmatrix()(VanRaden), andgblup()(GBLUP by REML, validated againstrrBLUP::mixed.solve). - Unified modelling interface
gs_fit()/predict()covering GBLUP, elastic net (glmnet), random forest (ranger) and gradient boosting (xgboost). gs_cv()for breeding-relevant cross-validation (random k-fold and leave-one-group-out).gs_ensemble(), a stacked super-learner combining base models with non-negative, out-of-fold-fitted weights.gs_benchmark()withprint/summary/plotto compare all available models under one cross-validation.
From NEWS.md in the release
Install
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- OK7 OK2026-07-01
Documentation
- Help pages
- 15 (1 internal)includes a package overview page
- Examples that run
- 100%14 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 20%
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Repository
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Dependencies
Code & Tests
People & History
- 0.1.0Latest2026-06-30 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-30
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 33 KB
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