GSbench
Benchmarking Genomic Selection and Machine-Learning Prediction Models
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | GSbench_0.1.0.tar.gz |
89.5 KiB |
0.1.0 |
rolling linux/noble R-4.5 | GSbench_0.1.0.tar.gz |
89.7 KiB |
0.1.0 |
rolling source/ R- | GSbench_0.1.0.tar.gz |
32.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | GSbench_0.1.0.tar.gz |
89.5 KiB |
0.1.0 |
latest linux/noble R-4.5 | GSbench_0.1.0.tar.gz |
89.7 KiB |
0.1.0 |
latest source/ R- | GSbench_0.1.0.tar.gz |
32.6 KiB |
0.1.0 |
2026-04-23 source/ R- | GSbench_0.1.0.tar.gz |
0 B |